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File size: 1,417 Bytes
7330a0e 7ec3e35 d1aa2fe 7ec3e35 7330a0e 7ec3e35 7330a0e d1aa2fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | # Presentation Visuals
Generated slide-ready visuals for BridgeLink ASL.
## Files
- `bridgelink_pipeline.svg`: methodology / system diagram
- `project_scope_board.svg`: what the team trained versus what was reused
- `cnn_vs_vlm_comparison.svg`: final comparison numbers for the presentation
- `metrics-summary.json`: source values used to render the comparison slide
- `how2sign_dataset_constraint.(png|svg)`: explains why the full 31k How2Sign clips cannot be used as sentence classes
- `how2sign_subset_benchmark.(png|svg)`: Top-12, Top-25, and Top-25 normalized benchmark growth and accuracy
- `how2sign_top25_class_distribution.(png|svg)`: class imbalance view for the normalized repeated-sentence subset
- `how2sign_top25_experiment_comparison.(png|svg)`: comparison of the Top-25 baseline, normalized checkpoint, continued training, and balanced-weight experiments
- `how2sign_plot_metrics.json`: source values used by the matplotlib How2Sign plots
## Current values
- Evaluation set: 36 clips
- Unique classes: 22
- CNN top-1: 25.0%
- CNN top-5: 58.3%
- Qwen rerank: 25.0%
- VLM wrapper failures: 0
## Regenerate
```powershell
python scripts\generate_presentation_visuals.py
python scripts\generate_how2sign_presentation_plots.py
```
## Current How2Sign 3D CNN values
- Top-12 test accuracy: 14.7%
- Top-25 test accuracy: 20.8%
- Top-25 normalized test accuracy: 27.8%
- Top-25 normalized classes: 21
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