README_hf.md

metadata

title: FracAtlas YOLACT+
emoji: 🦴
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: >-
  Fracture detection and segmentation on X-ray images using YOLACT+ with
  ResNet-18 backbone, trained on the FracAtlas dataset.

FracAtlas Fracture Detection — YOLACT+ (ResNet-18)

Instance segmentation model for bone fracture detection on X-ray images.

Model

• Architecture: YOLACT+ with ResNet-18 backbone
• Neck: Feature Pyramid Network (FPN, 5 levels)
• Prototypes: 32 mask prototypes
• Input size: 550×550

Dataset

FracAtlas

Split	Fractured	Non-fractured	Total
Train	500	500	1000
Val	100	100	200
Test	100	100	200

Training

• Epochs: 200
• Optimizer: AdamW (lr=5e-5, weight_decay=5e-4)
• Scheduler: Cosine decay with 5-epoch warmup
• Loss: Focal classification + SmoothL1 box + BCE mask

Results (Validation Set)

Metric	Value
Precision	0.5328
Recall	0.5422
F1 Score	0.5374
Avg IoU	0.9405

Author

Muhammad Adil — MS Data Science, Information Technology University (ITU) Lahore, Pakistan
GitHub: Adil6312

License

Apache 2.0