Transformers
Safetensors
PyTorch
English
SegformerForSemanticSegmentation
semantic-segmentation
segformer
agricultural-cv
Eval Results (legacy)
Instructions to use mujerry/nematode-segformer-b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mujerry/nematode-segformer-b3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mujerry/nematode-segformer-b3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Log Segformer metrics curves, class-wise parameters for segformer-b3_run_2026-06-29_00-06_sample_50
Browse files- README.md +9 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
README.md
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@@ -23,6 +23,15 @@ model-index:
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- type: root_iou
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value: 0.8987028323778692
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name: Best Val Root-Class IoU
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---
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# Segformer Corm & Damage Segmentation Model (Ablation: 50.0%)
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- type: root_iou
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value: 0.8987028323778692
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name: Best Val Root-Class IoU
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- type: mean_accuracy
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value: 0.937641067753777
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name: Best Val Mean IoU
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- type: damage_accuracy
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value: 0.8839718280719298
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name: Best Val Damage-Class IoU
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- type: root_accuracy
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value: 0.9737251750602065
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name: Best Val Root-Class IoU
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---
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# Segformer Corm & Damage Segmentation Model (Ablation: 50.0%)
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:e94ac217f475156d7a5fa8b764f12751940d3fbbe0b5791ace8525a04914f503
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size 188982852
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:52686aa0d192acaf557bc15dbd51c9bd7c4f5ddca9b60b78a68a125f51590bec
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size 5265
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