Mufasa06/WoundClassification

1# config.yaml2# Main configuration file for Wound Pipeline3 4paths:5  # Base path for Segmentation Data6  seg_base_path: "c:/Users/Mustafa/Desktop/wound_pipeline/data/Segmentation_DS/Segmentation_Datasets/seed_2026"7  8  # Base paths for Classification Data9  cls_base_path: "c:/Users/Mustafa/Desktop/wound_pipeline/data/classification/images_resized"10  cls_mask_path: "c:/Users/Mustafa/Desktop/wound_pipeline/data/classification/masks_pred"11  12  # External test paths (if used)13  ext_test_base_path: "c:/Users/Mustafa/Desktop/wound_pipeline/data/classification/ext_images"14  ext_test_base_path_mask: "c:/Users/Mustafa/Desktop/wound_pipeline/data/classification/ext_masks"15 16  # Models and outputs17  mlflow_tracking_uri: "file:c:/Users/Mustafa/Desktop/wound_pipeline/mlruns"18  seg_output_dir: "c:/Users/Mustafa/Desktop/wound_pipeline/models/segmentation_runs"19  cls_output_dir: "c:/Users/Mustafa/Desktop/wound_pipeline/models/classification_runs"20  21  # Inference22  inference_input_dir: "c:/Users/Mustafa/Desktop/wound_pipeline/data/inference/input"23  inference_output_dir: "c:/Users/Mustafa/Desktop/wound_pipeline/data/inference/output"24 25segmentation:26  mlflow_experiment_name: "wound_segmentation_unet"27  img_size: [256, 256]28  in_channels: 329  out_channels: 130  epochs: 231  batch_size: 1232  lr: 0.000133  early_stopping_patience: 1034  seed: 4235 36classification:37  mlflow_experiment_name: "wound_classification_effnet"38  img_size: [256, 256]39  num_classes: 440  epochs: 7041  batch_size: 2842  lr: 0.00143  early_stopping_patience: 844  seed: 4245  use_augmented: true46  aug_split_name: "train_aug"47 48inference:49  seg_model_path: "C:/Users/Mustafa/Desktop/wound_pipeline/models/segmentation_runs/wound_segmentation_model_best_scripted.pt"50  cls_model_path: "C:/Users/Mustafa/Desktop/wound_pipeline/models/classification_runs/cls_with_mask.pt"51  threshold: 0.552  save_tensor_pt: false53