Machine Learning · GitHub

Official Training and Benchmarking Implementation of PDMD: Projected Distribution Matching Distillation for Video Diffusion Models

JAX/Flax neural operators for scientific machine learning, with benchmarks and pretrained weights.

PyTorch implementation of over 30 realtime semantic segmentations models, e.g. BiSeNetv1, BiSeNetv2, CGNet, ContextNet, DABNet, DDRNet, EDANet, ENet, ERFNet, ESPNet, ESPNetv2, FastSCNN, ICNet, LEDNet, LinkNet, PP-LiteSeg, SegNet, ShelfNet, STDC, SwiftNet, and support knowledge distillation, distributed training, Optuna etc.

Official implementation of “A Turbo-Inference Strategy for Object Detection and Instance Segmentation” (CVIU 2026). A training-free framework for iterative refinement between object detection and instance segmentation.

• No retraining: all refinement happens during inference.
• Plug-and-play: the shared MaskBoxRefiner module is configurable from the RoI head.

[CVPR 2022] Official implementation of the paper "Uformer: A General U-Shaped Transformer for Image Restoration".

A repository of awesome Non-Intrusive Load Monitoring(NILM) with code.

RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.

• Tokyo - NNP - S_LOCATION
• New - NNP - B_LOCATION

INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models.

[EMNLP 2020] OpenUE: An Open Toolkit of Universal Extraction from Text