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
File size: 1,266 Bytes
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language: en
license: mit
tags:
- semantic-segmentation
- pytorch
- transformers
- segformer
- agricultural-cv
model-index:
- name: segformer-b3_run_2026-06-29_00-06_sample_50
results:
- task:
type: semantic-segmentation
name: Corm Segmentation
metrics:
- type: mean_iou
value: 0.8843405635722065
name: Best Val Mean IoU
- type: damage_iou
value: 0.7566256108147862
name: Best Val Damage-Class IoU
- type: root_iou
value: 0.8987028323778692
name: Best Val Root-Class IoU
- type: mean_accuracy
value: 0.937641067753777
name: Best Val Mean Accuracy
- type: damage_accuracy
value: 0.8839718280719298
name: Best Val Damage-Class Accuracy
- type: root_accuracy
value: 0.9737251750602065
name: Best Val Root-Class Accuracy
---
# Segformer Corm & Damage Segmentation Model (Ablation: 70.0%)
This repository contains logs for experiment `segformer-b3_run_2026-06-29_00-06_sample_50`.
## Model Summary
- **Architecture Type:** Segformer (`nvidia/segformer-b3-finetuned-ade-512-512`)
- **Train Sample Fraction:** `70.0% - 533/762 images`
- **Val Sample Fraction:** `60.0% - 186/310 images`
## Metric Curves
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