dlmacedo/robust-deep-learning
A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.
This tool helps machine learning engineers and researchers build more reliable deep learning models. It takes your existing deep learning model and training data to produce a model that can better identify data points that are different from what it was trained on, and provide more accurate assessments of its own confidence. The output is a robustly trained or fine-tuned model ready for deployment in real-world scenarios.
No commits in the last 6 months. Available on PyPI.
Use this if you need your deep learning models to accurately detect unusual or unexpected data and provide trustworthy confidence scores for their predictions, without needing extra data or complex hyperparameter tuning.
Not ideal if you are working with simple models or datasets where out-of-distribution detection and uncertainty estimation are not critical concerns.
Stars
17
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Nov 26, 2022
Commits (30d)
0
Dependencies
12
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