marcellodebernardi/loss-landscapes

Capture every activation and gradient of any PyTorch model — forward and backward — with automatic graph visualization, rich metadata, and live interventions. Works on any architecture, including dynamic and recurrent ones.

Efficient PyTorch Hessian eigendecomposition tools!

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM, Finer-CAM, LeGrad, RefineCAM)

👋 Xplique is a Neural Networks Explainability Toolbox

Seamless analysis of your PyTorch models (RAM usage, FLOPs, MACs, receptive field, etc.)

VisualTorch aims to help visualize Torch-based neural network architectures.

Model interpretability and understanding for PyTorch

{KFAC,EKFAC,Diagonal,Implicit} Fisher Matrices and finite width NTKs in PyTorch

optimizer & lr scheduler & loss function collections in PyTorch