segface_hair_khairstyle

Personal Hugging Face model repository for a custom PyTorch hair-only segmentation checkpoint trained on a K-Hairstyle based AIHub subset.

Model Summary

• Backbone: swin_base
• Input size: 512x512
• Freeze backbone: True
• LoRA: rank=8, alpha=16.0, dropout=0.05
• Threshold used during validation: 0.5
• Train / Val split used for this run: 50,000 / 5,000

Validation Metrics

Validation metrics are recorded during training. Additional hold-out test metrics are listed below when available.

Metric	Value
Best epoch	7
Val IoU	0.9487
Val Dice	0.9736
Val Precision	0.9723
Val Recall	0.9751
Epochs completed	10
Avg epoch time (sec)	3546.45

Hold-out Test Metrics

Split	IoU	Dice	Precision	Recall
Fine-tuned test	0.9368	0.9674	0.9651	0.9697
Fine-tuned test_hard	0.9393	0.9687	0.9663	0.9711
Baseline SegFace test	0.1360	0.2394	0.1360	1.0000
Baseline SegFace test_hard	0.1803	0.3055	0.1803	1.0000

Comparison Notes

• Test IoU improved from 0.1360 to 0.9368 (+0.8008).
• Test Dice improved from 0.2394 to 0.9674 (+0.7280).
• Hard test IoU improved from 0.1803 to 0.9393 (+0.7590).
• Hard test Dice improved from 0.3055 to 0.9687 (+0.6632).
• The pretrained baseline tended to over-predict hair regions, showing high recall but very low precision.

Test Split Summary

• train / val / test / test_hard: 50,000 / 5,000 / 921 / 150
• source overlap train-test: 0
• source overlap val-test: 0

Qualitative Comparison

Test

• Selection: top-improved, previews: 12
• Representative sample ad5c2098-485e-48d7-979b-6190bb01e7a2: baseline IoU 0.0481 -> fine-tuned IoU 0.9751 (+0.9270).

Test Hard

• Selection: top-improved, previews: 4
• Representative sample ccfa01f0-1b0a-4a39-a1c9-9d5c883f3fb2: baseline IoU 0.0707 -> fine-tuned IoU 0.9546 (+0.8839).

Bundle Contents

• best.pt: inference checkpoint
• config.json: training-time model config
• training_run_summary.json: run summary and validation metrics
• reports/*.json: hold-out test and baseline comparison reports when available
• reports/mask_comparison/**: baseline vs fine-tuned qualitative comparison sheets and sample previews when available
• docs/test_split_summary.json: hold-out split summary when available
• inference.py: local / Hub inference example
• requirements.txt: minimal runtime dependencies
• hair_mask_dataset/, models/: custom model code required to load the checkpoint

Inference

Run locally from the root of this model bundle:

python -m pip install -r requirements.txt
python inference.py --checkpoint best.pt --input path/to/input.jpg --output-mask output_mask.png --output-overlay output_overlay.png

You can also load directly from the Hugging Face Hub after uploading:

python -m pip install -r requirements.txt
python inference.py --repo-id siik/segface_hair_khairstyle --input path/to/input.jpg --output-mask output_mask.png --output-overlay output_overlay.png

Inputs and Outputs

• Input: RGB portrait image file such as .jpg or .png
• Output mask: single-channel binary hair mask PNG
• Output overlay: optional RGB overlay image for quick visual inspection

Notes

• This repo contains custom code and a raw PyTorch checkpoint, not a Transformers-format model.
• Preprocessing expects RGB input, resize to 512, ImageNet normalization, and sigmoid threshold 0.5.
• Before making the repository public, verify whether your AIHub / K-Hairstyle data usage terms allow public redistribution of derived model weights.

Training Artifacts