Log hyper-parameters, metric curves, and model state for nematode-deeplabv3plus_512-run_2026-06-28_17-57_sample_50
Browse files- .gitattributes +1 -0
- README.md +41 -0
- checkpoint-7906/model.safetensors +3 -0
- checkpoint-7906/optimizer.pt +3 -0
- checkpoint-7906/rng_state.pth +3 -0
- checkpoint-7906/scheduler.pt +3 -0
- checkpoint-7906/trainer_state.json +0 -0
- checkpoint-7906/training_args.bin +3 -0
- checkpoint-9313/model.safetensors +3 -0
- checkpoint-9313/optimizer.pt +3 -0
- checkpoint-9313/rng_state.pth +3 -0
- checkpoint-9313/scheduler.pt +3 -0
- checkpoint-9313/trainer_state.json +0 -0
- checkpoint-9313/training_args.bin +3 -0
- checkpoint-9380/model.safetensors +3 -0
- checkpoint-9380/optimizer.pt +3 -0
- checkpoint-9380/rng_state.pth +3 -0
- checkpoint-9380/scheduler.pt +3 -0
- checkpoint-9380/trainer_state.json +0 -0
- checkpoint-9380/training_args.bin +3 -0
- config.json +10 -0
- performance_curves.png +3 -0
- pytorch_model.bin +3 -0
- training_metrics.csv +0 -0
.gitattributes
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README.md
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---
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language: en
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license: mit
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tags:
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- semantic-segmentation
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- pytorch
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- deeplabv3plus
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- resnet34
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- agricultural-cv
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model-index:
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- name: nematode-deeplabv3plus_512-run_2026-06-28_17-57_sample_50
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results:
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- task:
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type: semantic-segmentation
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name: Corm Segmentation
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metrics:
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- type: mean_iou
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value: 0.8735190836786705
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name: Best Val Mean IoU
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- type: damage_iou
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value: 0.7381499204526462
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name: Best Val Damage-Class IoU
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- type: root_iou
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value: 0.8856007365241481
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name: Best Val Root-Class IoU
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---
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# DeepLabV3+ Corm & Damage Semantic Segmentation Model (Ablation: 70.0%)
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This repository contains weights, performance curves, and hyperparameter logs for experiment `nematode-deeplabv3plus_512-run_2026-06-28_17-57_sample_50`.
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## Model Summary
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- **Architecture Type:** DeepLabV3+ (SMP wrapper)
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- **Backbone Encoder:** `resnet34`
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- **Training Resolution:** `512x512`
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- **Loss Optimizer configuration:** `CrossEntropy`
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- **Dataset Sample Fraction:** `70.0%`
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## Metrics Curves
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Below are the training performance plots generated for this run:
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{
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"model_type": "DeepLabV3Plus",
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"encoder_name": "resnet34",
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"encoder_weights": "imagenet",
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"in_channels": 3,
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"classes": 3,
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"input_resolution": "512x512",
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"loss_type": "CrossEntropy",
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"train_sample_fraction": 0.7
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}
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performance_curves.png
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pytorch_model.bin
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