google
/
vit-base-patch16-224
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update model card

This commit is contained in:
Niels Rogge 2021-09-13 19:52:27 +00:00 committed by huggingface-web
parent d2989d2d10
commit c697c92e0e
1 changed files with 4 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
README.md
View File

@ -34,10 +34,13 @@ Here is how to use this model to classify an image of the COCO 2017 dataset into
from transformers import ViTFeatureExtractor, ViTForImageClassification from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image from PIL import Image
import requests import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg' url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw) image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224') feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt") inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs) outputs = model(**inputs)
logits = outputs.logits logits = outputs.logits
@ -46,7 +49,7 @@ predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx]) print("Predicted class:", model.config.id2label[predicted_class_idx])
``` ```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change. For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/vit.html#).
## Training data ## Training data