Create README.md

README.md

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+---
+license: mit
+---
+# Model Card for Model ViT fine tuning on CiFAR10
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+It's a toy experiemnt of fine tuning ViT by using huggingface transformers.
+
+## Model Details
+
+It's fine tuned on CiFAR10 for 1000 steps, and achieved accuracy of 98.7% on test split.
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** verypro
+- **Model type:** Vision Transformer
+- **License:** MIT
+- **Finetuned from model [optional]:** google/vit-base-patch16-224
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+```
+from transformers import ViTImageProcessor, ViTForImageClassification
+from torchvision import datasets
+
+# # 初始化模型和特征提取器
+image_processor = ViTImageProcessor.from_pretrained('verypro/vit-base-patch16-224-cifar10')
+model = ViTForImageClassification.from_pretrained('verypro/vit-base-patch16-224-cifar10')
+
+
+# 加载 CIFAR10 数据集
+test_dataset = datasets.CIFAR10(root='./data', train=False, download=True)
+
+sample = test_dataset[0]
+image = sample[0]
+gt_label = sample[1]
+
+# 保存原始图像，并打印其标签
+image.save("original.png")
+print(f"Ground truth class: '{test_dataset.classes[gt_label]}'")
+
+inputs = image_processor(image, return_tensors="pt")
+outputs = model(**inputs)
+
+logits = outputs.logits
+print(logits)
+
+predicted_class_idx = logits.argmax(-1).item()
+predicted_class_label = test_dataset.classes[predicted_class_idx]
+print(f"Predicted class: '{predicted_class_label}', confidence: {logits[0, predicted_class_idx]:.2f}")
+```