vit-age-classifier

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nateraw

Image Model

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Quick Summary

--- tags: - image-classification - pytorch datasets: - nateraw/fairface ---

Code Examples

Get example image from official fairface repo + read it in as an imagepythontransformers

import requests
from PIL import Image
from io import BytesIO

from transformers import ViTFeatureExtractor, ViTForImageClassification

# Get example image from official fairface repo + read it in as an image
r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
im = Image.open(BytesIO(r.content))

# Init model, transforms
model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')

# Transform our image and pass it through the model
inputs = transforms(im, return_tensors='pt')
output = model(**inputs)

# Predicted Class probabilities
proba = output.logits.softmax(1)

# Predicted Classes
preds = proba.argmax(1)

Get example image from official fairface repo + read it in as an imagepythontransformers

import requests
from PIL import Image
from io import BytesIO

from transformers import ViTFeatureExtractor, ViTForImageClassification

# Get example image from official fairface repo + read it in as an image
r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
im = Image.open(BytesIO(r.content))

# Init model, transforms
model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')

# Transform our image and pass it through the model
inputs = transforms(im, return_tensors='pt')
output = model(**inputs)

# Predicted Class probabilities
proba = output.logits.softmax(1)

# Predicted Classes
preds = proba.argmax(1)

Get example image from official fairface repo + read it in as an imagepythontransformers

import requests
from PIL import Image
from io import BytesIO

from transformers import ViTFeatureExtractor, ViTForImageClassification

# Get example image from official fairface repo + read it in as an image
r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
im = Image.open(BytesIO(r.content))

# Init model, transforms
model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')

# Transform our image and pass it through the model
inputs = transforms(im, return_tensors='pt')
output = model(**inputs)

# Predicted Class probabilities
proba = output.logits.softmax(1)

# Predicted Classes
preds = proba.argmax(1)

Get example image from official fairface repo + read it in as an imagepythontransformers

import requests
from PIL import Image
from io import BytesIO

from transformers import ViTFeatureExtractor, ViTForImageClassification

# Get example image from official fairface repo + read it in as an image
r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
im = Image.open(BytesIO(r.content))

# Init model, transforms
model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')

# Transform our image and pass it through the model
inputs = transforms(im, return_tensors='pt')
output = model(**inputs)

# Predicted Class probabilities
proba = output.logits.softmax(1)

# Predicted Classes
preds = proba.argmax(1)

Get example image from official fairface repo + read it in as an imagepythontransformers

import requests
from PIL import Image
from io import BytesIO

from transformers import ViTFeatureExtractor, ViTForImageClassification

# Get example image from official fairface repo + read it in as an image
r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
im = Image.open(BytesIO(r.content))

# Init model, transforms
model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')

# Transform our image and pass it through the model
inputs = transforms(im, return_tensors='pt')
output = model(**inputs)

# Predicted Class probabilities
proba = output.logits.softmax(1)

# Predicted Classes
preds = proba.argmax(1)

Get example image from official fairface repo + read it in as an imagepythontransformers

import requests
from PIL import Image
from io import BytesIO

from transformers import ViTFeatureExtractor, ViTForImageClassification

# Get example image from official fairface repo + read it in as an image
r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
im = Image.open(BytesIO(r.content))

# Init model, transforms
model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')

# Transform our image and pass it through the model
inputs = transforms(im, return_tensors='pt')
output = model(**inputs)

# Predicted Class probabilities
proba = output.logits.softmax(1)

# Predicted Classes
preds = proba.argmax(1)

Get example image from official fairface repo + read it in as an imagepythontransformers

import requests
from PIL import Image
from io import BytesIO

from transformers import ViTFeatureExtractor, ViTForImageClassification

# Get example image from official fairface repo + read it in as an image
r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
im = Image.open(BytesIO(r.content))

# Init model, transforms
model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')

# Transform our image and pass it through the model
inputs = transforms(im, return_tensors='pt')
output = model(**inputs)

# Predicted Class probabilities
proba = output.logits.softmax(1)

# Predicted Classes
preds = proba.argmax(1)

Get example image from official fairface repo + read it in as an imagepythontransformers

import requests
from PIL import Image
from io import BytesIO

from transformers import ViTFeatureExtractor, ViTForImageClassification

# Get example image from official fairface repo + read it in as an image
r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
im = Image.open(BytesIO(r.content))

# Init model, transforms
model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')

# Transform our image and pass it through the model
inputs = transforms(im, return_tensors='pt')
output = model(**inputs)

# Predicted Class probabilities
proba = output.logits.softmax(1)

# Predicted Classes
preds = proba.argmax(1)

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