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evalify¶
Evaluate Biometric Authentication Models Literally in Seconds.
Installation¶
Stable release:¶
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Bleeding edge:¶
1 |
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Used for¶
Evaluating all biometric authentication models, where the model output is a high-level embeddings known as feature vectors for visual or behaviour biometrics or d-vectors for auditory biometrics.
Usage¶
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How it works¶
- When you run an experiment, evalify tries all the possible combinations between individuals for authentication based on the
X
andy
parameters and returns the results including FPR, TPR, FNR, TNR and ROC AUC.X
is an array of embeddings andy
is an array of corresponding targets. - Evalify can find the optimal threshold based on your agreed FPR and desired similarity or distance metric.
Documentation:¶
Features¶
- Blazing fast implementation for metrics calculation through optimized einstein sum and vectorized calculations.
- Many operations are dispatched to canonical BLAS, cuBLAS, or other specialized routines.
- Smart sampling options using direct indexing from pre-calculated arrays with total control over sampling strategy and sampling numbers.
- Supports most evaluation metrics:
cosine_similarity
pearson_similarity
cosine_distance
euclidean_distance
euclidean_distance_l2
minkowski_distance
manhattan_distance
chebyshev_distance
- Computation time for 4 metrics 4.2 million samples experiment is 24 seconds vs 51 minutes if looping using
scipy.spatial.distance
implemntations.
TODO¶
- Safer memory allocation. I did not have issues but if you ran out of memory please manually set the
batch_size
argument.
Contribution¶
- Contributions are welcomed, and they are greatly appreciated! Every little bit helps, and credit will always be given.
- Please check CONTRIBUTING.md for guidelines.
Citation¶
- If you use this software, please cite it using the metadata from CITATION.cff