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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)
```
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