google
/

vit-base-patch32-384

Image Classification

Model card Files Files and versions

nielsr HF Staff commited on Mar 30, 2021

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212b600

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1 Parent(s): fef36e9

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README.md +3 -2

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@@ -41,11 +41,12 @@ url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
 image = Image.open(requests.get(url, stream=True).raw)
 feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch32-384')
 model = ViTForImageClassification.from_pretrained('google/vit-base-patch32-384')
-inputs = feature_extractor(images=image)
 outputs = model(**inputs)
 logits = outputs.logits
 # model predicts one of the 1000 ImageNet classes
-predicted_class = logits.argmax(-1).item()
 ```
 Currently, both the feature extractor and model  support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.

 image = Image.open(requests.get(url, stream=True).raw)
 feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch32-384')
 model = ViTForImageClassification.from_pretrained('google/vit-base-patch32-384')
+inputs = feature_extractor(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])
 ```
 Currently, both the feature extractor and model  support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.