README.md

metadata

title: Aegis-Safe-Work Fall Detector
colorFrom: yellow
colorTo: red
sdk: gradio
sdk_version: 5.23.3
app_file: app.py
pinned: false
license: cc-by-nc-nd-4.0

Aegis-Safe-Work: Fall Detector

Fall detection over short video clips using EfficientNet-Lite0 combined with a temporal attention mechanism (Attention MLP) and a binary classifier. This Space loads the trained checkpoint from beaunix/aegis-fall-detector and runs it on ZeroGPU.

How it works

1. The uploaded video (max 45 seconds) is opened and 16 frames are sampled uniformly across its full duration (not a sliding window).
2. Each frame is letterboxed (aspect-ratio preserved, black padding) to 224x224 and normalized with ImageNet statistics, matching the training ETL exactly.
3. All 16 frames are processed in a single GPU forward pass: EfficientNet-Lite0 extracts per-frame features, temporal attention weights and pools them, and the MLP classifier outputs a single fall probability for the clip.
4. The report shows the per-frame attention weights as a bar chart, the frame with peak attention overlaid with the verdict, and a metrics summary table.

Model performance (validation set)

Metric	Value
Accuracy	0.9762
F1	0.9730
Precision	0.9574
Recall	0.9890
Threshold	0.65

Notes

• Videos longer than 45 seconds are rejected to protect ZeroGPU quota.
• This Space demonstrates the fall classifier in isolation, independent of the other Safe-Work models (PPE detection, fire/smoke detection).

License

CC BY-NC-ND 4.0 (Attribution - NonCommercial - NoDerivatives). See https://creativecommons.org/licenses/by-nc-nd/4.0/