PE-Core-B-16

Name: PE-Core-B-16
Author: timm

4.8K

license:apache-2.0

timm

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Quick Summary

AI model with specialized capabilities.

Code Examples

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

How to usepythonpytorch

import torch
from urllib.request import urlopen
from PIL import Image
import open_clip

model_id = 'hf-hub:timm/PE-Core-B-16'
model, _, preprocess = open_clip.create_model_and_transforms(model_id)
tokenizer = open_clip.get_tokenizer(model_id)

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image, normalize=True)
    text_features = model.encode_text(text, normalize=True)
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

zipped_list = list(zip(labels_list, [100 * round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

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