hila-chefer/Transformer-MM-Explainability
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
912
Stars
116
Forks
41.7
Radar Score
Stable
Momentum
Scoring Breakdown
Impact59.8
Velocity25.9
Health35.0
Growth Metrics
7-Day Stars
+0
30-Day Stars
+0
Acceleration
x
Language
Jupyter Notebook
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🛡️ Enterprise Readiness & Commercial Safety
Organic Growth
95%
High Confidence
Growth pattern looks organic.
License
Permissive (Commercial Friendly)
Safe for commercial use, modification, and distribution.
Health Grade
C
Based on maintenance, issue response, and release cadence.
Radar Score
41.7
Composite of Impact, Velocity, and Health.
Disclaimer: This is an automated heuristic assessment based on public GitHub signals. Always perform your own security audit, dependency review, and legal compliance check before production deployment.
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