created

rawresnet.py

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+from transformers import AutoImageProcessor, ResNetForImageClassification
+import torch
+from datasets import load_dataset
+
+dataset = load_dataset("huggingface/cats-image")
+image = dataset["test"]["image"][0]
+
+processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
+model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
+
+inputs = processor(image, return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+# model predicts one of the 1000 ImageNet classes
+predicted_label = logits.argmax(-1).item()
+print(model.config.id2label[predicted_label])