readme: ---
  license: apache-2.0
  tags:
  - vision
  - image-classification
  widget:
  - src: https://datasets-server.huggingface.co/assets/ShihTing/IsCausewayOffset/--/ShihTing--IsCausewayOffset/validation/0/image/image.jpg
    example_title: Ex1
  ---
  # 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])
  ```
  
  