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README.md

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+# Classifier for Selecting Pathology Images
+
+
+
+This is a ConvNext-tiny model trained on 30K annotations on if image is belongs to the pathology image or non-pathology image.
+
+## Usage
+
+> #### Step1: Download model checkpoint in [convnext-pathology-classifier](https://huggingface.co/jamessyx/convnext-pathology-classifier) .
+
+
+
+> #### Step2: Load the model
+
+You can use the following code to load the model.
+
+```python
+import timm ##timm version 0.9.7
+import torch.nn as nn
+import torch
+from torchvision import transforms
+from PIL import Image
+
+class CT_SINGLE(nn.Module):
+    def __init__(self, model_name):
+        super(CT_SINGLE, self).__init__()
+        print(model_name)
+        self.model_global = timm.create_model(model_name, pretrained=False, num_classes=0)
+        self.fc = nn.Linear(768, 2)
+
+    def forward(self, x_global):
+        features_global = self.model_global(x_global)
+        logits = self.fc(features_global)
+        return logits
+
+def load_model(checkpoint_path, model):
+    checkpoint = torch.load(checkpoint_path, map_location='cpu')
+    model.load_state_dict(checkpoint['model'])
+    print("Resume checkpoint %s" % checkpoint_path)
+
+##load the model
+model = CT_SINGLE('convnext_tiny')
+model_path = 'Your model path'
+load_model(model_path, model)
+model.eval().cuda()
+
+```
+
+
+
+> ### Step3: Construct and predict your own data
+
+In this step, you'll construct your own dataset. Use PIL to load images and employ `transforms` from torchvision for data preprocessing.
+
+```python
+def default_loader(path):
+    img = Image.open(path)
+    return img.convert('RGB')
+
+data_transforms = transforms.Compose([
+        transforms.Resize((224,224)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+
+def predict(img_path, model):
+    img = default_loader(img_path)
+    img = data_transforms(img)
+    img = img.unsqueeze(0)
+    img = img.cuda()
+    output = model(img)
+    _, pred = torch.topk(output, 1, dim=-1)
+    pred = pred.data.cpu().numpy()[:, 0]
+    return pred   ## 0 indicates non-pathology image and 1 indicates pathology image
+
+img_path = 'Your image path'
+pred = predict(img_path, model)
+print(pred)
+```
+