presentation/visuals/README.md

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

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