| MODELS DESCRIPTION | |
| ================== | |
| This folder contains the pre-trained models used in the paper. | |
| You can use these weights entirely without training new models. | |
| 1. clr_YOLOV8.pt | |
| ---------------- | |
| * **Pipeline Phase:** 01_leaf_extraction | |
| * **Model Type:** YOLOv8 (Ultralytics) | |
| * **Purpose:** Detects and segments coffee leaves in field images. | |
| * **Training Data:** Labeled using GroundedSAM (Autodistill). | |
| * **Usage:** Used by `extract_leaves.py` to identify individual leaves. | |
| 2. severity_rust.pt | |
| ------------------- | |
| * **Pipeline Phase:** 02_severity_segmentation | |
| * **Model Type:** SAM2 (Segment Anything Model 2) - Fine-tuned Adapter | |
| * **Purpose:** Segments "Rust" (disease) areas on extracted leaf images. | |
| * **Usage:** Used by `inference_clr.py`. | |
| 3. deeplab_binary_best.pth | |
| -------------------------- | |
| * **Pipeline Phase:** 02_severity_segmentation | |
| * **Model Type:** DeepLabV3+ (ResNet50 Encoder) | |
| * **Purpose:** Alternative model for segmenting "Rust" areas. | |
| * **Usage:** Used by `deeplab/inference_deeplabv3.py`. | |
| HOW TO USE | |
| ========== | |
| To use these models, update the `CHECKPOINT_PATH` or `MODEL_PATH` variable in the corresponding python script to point to these files. | |
| Example (inference_clr.py): | |
| CHECKPOINT_PATH = "./03_trained_models/severity_rust.pt" |