# The Robust Deep Learning Library

Calibrate 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.