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
- The uploaded video (max 45 seconds) is opened and 16 frames are sampled uniformly across its full duration (not a sliding window).
- Each frame is letterboxed (aspect-ratio preserved, black padding) to 224x224 and normalized with ImageNet statistics, matching the training ETL exactly.
- 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.
- 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 |
License
CC BY-NC-ND 4.0 (Attribution - NonCommercial - NoDerivatives). See https://creativecommons.org/licenses/by-nc-nd/4.0/
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