Bug fixes: Fix the compatibility of ONNX conversion with torch 1.11+ Fix the docker publication Fix Pre-Prediciton callback override bug Fix dataloader multiprocessing (for Mac OS and Windows) Fix strict_load not being passed in evaluate_from_recipe Resolve many potential circular imports Correct albumentations import log Fix incorrect tensor type & device when entire batch has no targets Fix documentation format in many files

Features: New documentation for many features (EMA, AMP, QAT, Knowledge-Distillation, Dataloaders, Optimizer, Schedulers…) Added Lion optimizer Added registration for SG Logger (to allow custom-defined loggers to be used from YAML) DDRNet 39 pre-trained segmentation model Mapillary dataset RoboFlow100 benchmark and all 100 datasets QATTrainer for training with QAT QAT from recipe script PP-YoloE pre-trained models Pose Estimation models loss functions Added Auxiliary heads to Unet Channel wise distillation ONNX Simplifier on ONNX conversion Unet and Loss function for binary segmentation New Transforms: DetectionPadToSize, DetectionImagePermute Allow activation factory type to take resolved type as inp New strict_load mode: key_matching.