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import torch |
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from datasets import load_dataset |
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from transformers import AutoImageProcessor, AutoModelForImageClassification |
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dataset = load_dataset("huggingface/cats-image") |
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image = dataset["test"]["image"][0] |
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model_name = "Hyunil/CSATv2" |
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processor = AutoImageProcessor.from_pretrained(model_name, trust_remote_code=True) |
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model = AutoModelForImageClassification.from_pretrained(model_name, trust_remote_code=True) |
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inputs = processor(image, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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pred = logits.argmax(-1).item() |
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print("Predicted label:", model.config.id2label[pred]) |