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UseItOrLoseIt/
clothing-segmentation-segformer-b0

Name: clothing-segmentation-segformer-b0
Availability: InStock

image-segmentation

computer-vision

clothing-segmentation

segformer

pytorch

mit

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Clothing Segmentation — SegFormer-B0

Binary clothing segmentation model fine-tuned on the iMaterialist Fashion 2020 (FGVC7) dataset. Separates worn clothing from images of people at the pixel level — built for use cases like virtual fitting rooms, fashion cataloging, and outfit-swap applications.

Full training code, technical report, and documentation: GitHub repository

Performance

Metric	Score
Dice Score	0.926
Mean IoU	0.878
Pixel Accuracy	0.976

Model Details

Architecture: SegFormer-B0 (nvidia/segformer-b0-finetuned-ade-512-512), fine-tuned with a binary (1-channel) segmentation head
Task framing: Binary segmentation (clothing vs. background) rather than multi-class, to match real-world virtual-fitting-room use cases
Loss function: Combined Dice + Binary Cross-Entropy (BCE) loss, to handle background-pixel-dominant masks
Input resolution: 512×512
Training data: 3,000-image subset of iMaterialist Fashion 2020 (FGVC7), 85/15 train/validation split
Training hardware: Kaggle free-tier T4 GPU, mixed precision training

Available Checkpoints

File	Contains	Use case
`best_model_inference_only.pth`	Model weights only	Evaluation and inference (recommended)
`best_model_full.pth`	Model weights + optimizer state	Resuming training from this checkpoint

Usage

Download the checkpoint:

wget https://huggingface.co/UseItOrLoseIt/clothing-segmentation-segformer-b0/resolve/main/best_model_inference_only.pth -O checkpoints/best_model.pth

Then, using the inference code from the GitHub repo:

python inference.py --image_path your_photo.jpg --output_path result.png

Limitations

Performance degrades in low-light or backlit scenes
Under-segments non-standard garment shapes (e.g., flared or trailing hemlines)
Minor boundary imprecision at low-contrast garment-to-garment transitions (e.g., dark pants meeting dark boots)
Edge sharpness is good but not pixel-perfect, a tradeoff of SegFormer-B0's lightweight decoder

Full analysis with annotated visual examples: report.md

Citation

If you use this model, please reference the GitHub repository.