Welcome to ModelTrace
ModelTrace is a research-oriented framework for analyzing, debugging, and evaluating machine learning models.
Use the links above to explore:
- Documentation: Overview, working process, future work, and academic relevance.
- Experiments: All eight experiments with methodology.
- API Reference: ModelInspector class and methods.
- README: Installation, usage, folder structure, and example outputs.
Quick Start
git clone https://github.com/MANISH-K-07/ModelTrace.git
cd ModelTrace
pip install -r requirements.txt
python setup.py install
# Run all experiments
python examples/run_all_experiments.py
Example Output
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Running ALL ModelTrace Experiments
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[Experiment 1] Sparsity Analysis
Conv sparsity (avg): 0.0000
FC sparsity (avg): 0.0000
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[Experiment 2] Activation Statistics
conv1: mean=-0.0926, std=0.6074, max=1.5994
conv2: mean=0.0355, std=0.2371, max=0.8379
fc: mean=0.0229, std=0.1267, max=0.1856
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[Experiment 3] Distribution Shift Test
Normal mean activation magnitude: 0.2797
Shifted mean activation magnitude: 0.3642
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[Experiment 4] Activation Drift
Drift score: 0.1093
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[Experiment 5] Model Regression Test
Regression drift score: 0.3458
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[Experiment 6] Layer Sensitivity Analysis
conv1: sensitivity=0.1984
conv2: sensitivity=0.0725
fc: sensitivity=0.0292
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[Experiment 7] Failure Attribution
Output shift magnitude: 0.0257
Layer-wise contribution:
conv1: 0.6609
conv2: 0.2325
fc: 0.1066
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[Experiment 8] Model Robustness Score
FINAL ROBUSTNESS SCORE: 0.0646
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✅ All experiments completed successfully.
Author
Manish Krishna Kandrakota – Final year B.Tech CSE student at Sreenidhi Institute of Science and Technology, Hyderabad.
GitHub: https://github.com/MANISH-K-07