| language: en | |
| license: mit | |
| tags: | |
| - semantic-segmentation | |
| - pytorch | |
| - unet | |
| - resnet34 | |
| - agricultural-cv | |
| model-index: | |
| - name: unet-nematode-256_run_2026-06-27_18-49 | |
| results: | |
| - task: | |
| type: semantic-segmentation | |
| name: Root Segmentation | |
| metrics: | |
| - type: mean_iou | |
| value: 0.8652376012720507 | |
| name: Best Val Mean IoU | |
| - type: damage_iou | |
| value: 0.7211209142612824 | |
| name: Best Val Damage-Class IoU | |
| - type: root_iou | |
| value: 0.8781060367890001 | |
| name: Best Val Root-Class IoU | |
| # UNet Root & Nematode Semantic Segmentation Model | |
| This repository contains weights, performance curves, and hyperparameter logs for experiment `unet-nematode-256_run_2026-06-27_18-49`. | |
| ## Model Summary | |
| - **Architecture Type:** UNet (SMP wrapper) | |
| - **Backbone Encoder:** `resnet34` | |
| - **Pretrained Weights:** `imagenet` | |
| - **Input Channels:** 3 | |
| - **Number of Classes:** 3 (0: background, 1: damage, 2: root) | |
| - **Training Resolution:** `256x256` | |
| - **Loss Optimizer configuration:** `CrossEntropy` | |
| ## Metrics Curves | |
| Below are the training performance plots generated for this run: | |
|  |