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from transformers import AutoFeatureExtractor, ResNetForImageClassification |
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import torch |
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from datasets import load_dataset |
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dataset = load_dataset("huggingface/cats-image") |
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image = dataset["test"]["image"][0] |
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feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50") |
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model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50") |
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inputs = feature_extractor(image, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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predicted_label = logits.argmax(-1).item() |
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print(model.config.id2label[predicted_label]) |
