Create README.md

README.md

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+# PanJu offset detect by image
+Use fintune from google/vit-base-patch16-224(https://huggingface.co/google/vit-base-patch16-224)
+
+## Dataset
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['image', 'label'],
+        num_rows: 329
+    })
+    validation: Dataset({
+        features: ['image', 'label'],
+        num_rows: 56
+    })
+})
+
+```
+36 Break and 293 Normal in train
+5 Break and 51 Normal in validation
+
+
+## Intended uses 
+
+### How to use
+
+Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
+
+```python
+# Load image
+import torch
+from transformers import ViTFeatureExtractor, ViTForImageClassification,AutoModel
+from PIL import Image
+import requests
+url='https://datasets-server.huggingface.co/assets/ShihTing/IsCausewayOffset/--/ShihTing--IsCausewayOffset/validation/0/image/image.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+# Load model
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+device = torch.device('cpu')
+extractor = AutoFeatureExtractor.from_pretrained('ShihTing/PanJuOffset_TwoClass')
+model = AutoModelForImageClassification.from_pretrained('ShihTing/PanJuOffset_TwoClass')
+# Predict
+inputs = extractor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+Prob = outputs.logits.softmax(dim=-1).tolist()
+print(Prob)
+# model predicts one of the 1000 ImageNet classes
+predicted_class_idx = logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[predicted_class_idx])
+```
+