Log hyper-parameters, metrics plots, and model state for unet_run_2026-06-23_13-16

.gitattributes

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README.md

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+---
+language: en
+license: mit
+tags:
+- semantic-segmentation
+- pytorch
+- unet
+- resnet34
+- agricultural-cv
+model-index:
+- name: unet_run_2026-06-23_13-16
+  results:
+  - task:
+      type: semantic-segmentation
+      name: Corm Segmentation
+    metrics:
+    - type: mean_iou
+      value: 0.9293187884547999
+      name: Best Val Mean IoU
+    - type: mean_accuracy
+      value: 0.9665135754918103
+      name: Best Val Mean Accuracy
+---
+
+# UNet Corm & Damage Semantic Segmentation Model
+
+This repository contains the weights, performance logs, and hyperparameters for experiment `unet_run_2026-06-23_13-16`.
+
+## Model Hyperparameters
+- **Architecture Type:** UNet (SMP wrapper)
+- **Backbone Encoder:** `UNet`
+- **Pretrained Weights:** `imagenet`
+- **Input Channels:** 3
+- **Number of Classes:** 3 (0: background, 1: damage, 2: root)
+- **Training Resolution:** `256x256`
+
+## Training Configurations
+- **Batch Size:** 8
+- **Optimizers / Lr:** AdamW / 0.0001
+- **Maximum Epochs:** 200
+- **Seed Configuration:** 42
+
+## Metrics Curves
+Below are the training performance plots generated for this run:
+![Performance Curves](performance_curves.png)

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config.json

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+    "in_channels": 3,
+    "classes": 3,
+    "input_resolution": "256x256"
+}

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