mit-b0

Name: mit-b0
Author: nvidia

76.8K

1 language

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nvidia

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77K downloads

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Quick Summary

SegFormer encoder fine-tuned on Imagenet-1k.

Code Examples

How to usepythontransformers

from transformers import SegformerImageProcessor, SegformerForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0")
model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

How to usepythontransformers

from transformers import SegformerImageProcessor, SegformerForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0")
model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

How to usepythontransformers

from transformers import SegformerImageProcessor, SegformerForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0")
model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

How to usepythontransformers

from transformers import SegformerImageProcessor, SegformerForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0")
model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

How to usepythontransformers

from transformers import SegformerImageProcessor, SegformerForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0")
model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

How to usepythontransformers

from transformers import SegformerImageProcessor, SegformerForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0")
model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

How to usepythontransformers

from transformers import SegformerImageProcessor, SegformerForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0")
model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

How to usepythontransformers

from transformers import SegformerImageProcessor, SegformerForImageClassification
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = SegformerImageProcessor.from_pretrained("nvidia/mit-b0")
model = SegformerForImageClassification.from_pretrained("nvidia/mit-b0")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

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