Pid Line Segmentation Unet Resnet34

--- library_name: transformers tags: - image-segmentation - unet - resnet - pid - computer-vision --- # P&ID Line Segmentation Model (U-Net with ResNet34 Backbone) This model is a U-Net architecture with a ResNet34 encoder, fine-tuned for P&ID (Piping and Instrumentation Diagram) line segmentation. ## Model Details - **Architecture**: U-Net with `resnet34` backbone. - **Input size**: `1024x1024` pixels. - **Output**: Binary mask (single class for lines). - **Loss Function**: Combined Dice Loss and Focal Loss. ## Performance (on Test Set) - **Test Loss**: 0.5721 - **Test Dice Score**: 0.9413 - **Test IoU Score**: 0.8908 ## Usage This model can be loaded using `segmentation_models_pytorch`. ```python import torch import segmentation_models_pytorch as smp from huggingface_hub import hf_hub_download # Define the model architecture model = smp.Unet( encoder_name="resnet34", encoder_weights=None, # Weights are loaded from the saved state_dict in_channels=3, classes=1, activation="sigmoid" ) # Download and load state_dict model_path = hf_hub_download(repo_id="Frimpong1/pid-line-segmentation-unet-resnet34", filename="pytorch_model.bin") model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) model.eval() # Example of using the model (assuming you have an image_tensor preprocessed) # image_tensor = ... # (C, H, W) float32 tensor, normalized # with torch.no_grad(): # prediction = model(image_tensor.unsqueeze(0)) # Add batch dimension # mask = (prediction.squeeze(0).cpu().numpy() > 0.5).astype(float) ``` ## Training Details - **Epochs Trained**: 20 - **Batch Size**: 4 - **Learning Rate**: 0.0001 - **Optimizer**: Adam ## License (Specify your license here, e.g., MIT)

Source context: 9 downloads · 1 likes · Pipeline image-segmentation · Library transformers · Repo Frimpong1/pid-line-segmentation-unet-resnet34