Aprendizaje automático · GitHub

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.

Pytorch implementation of paper "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing"

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".

PyTorch implementation of paper "Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes"

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

😎 A curated list of awesome lists across all machine learning topics. | 机器学习/深度学习/人工智能一切主题 (学习范式/任务/应用/模型/道德/交叉学科/数据集/框架/教程) 的资源列表汇总。

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