The Robust Deep Learning Library
Photo by AuthorsCalibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold. 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.
Model Independent: Use models from timm library or whatever you want.
Data Independent: Most cases work for any type of media (e.g., image, text, audio, and others).
Large-Scale Models and Data: Train using large-scale models and data (e.g., ImageNet).
Efficient Inferences: The trained models are as efficient as the ones trained using the cross-entropy loss.
Hyperparameter-Free: There is no hyperparameter to tune. “You only train once” (YOTO).
Standard Interface: Use the same API to train models with improved robustness using different losses.
No Need for Additional Data: The losses used in this library do not require collecting or using additional data.
Temperature Calibration: Calculate the Uncertainty Estimation and update the temperature of the output last layer.
Scalability: More data, Bigger Models, Better Results! Entropic losses perform better and better as the size of the data and model increase.
Threshold Computation: Compute the threshold for deciding regarding out-of-distribution examples.
Scores Computation: Compute the scores opting from a set of many different types available.
Detect Out-of-Distribution: Detect out-of-distribution examples using the computed scores.
State-of-the-art: SOTA results for out-of-distribution detection and uncertainty estimation.
