langchain-ChatGLM, local knowledge based ChatGLM with langchain ｜ 基于本地知识库的 ChatGLM 问答

CVPR 2022 论文和开源项目合集

thor: C++ helper library, for deep learning purpose

The Crosswalk Detection (Zebra Crossing Detection) Network based on YOLOv5

🔥🔥🔥🔥 YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥

yolov5 prune，Support V2, V3, V4 and V6 versions of yolov5

Official Tensorflow implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection" (AAAI 2022)

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

YOLOX is a high-performance anchor-free YOLO. Exceeding yolov3~v5 with ONNX, TensorRT, ncnn, and OpenVINO supported.

Official Implement of ICCV 2019 oral paper Bayesian Loss for Crowd Count Estimation with Point Supervision

A trusty face recognition research platform developed by Tencent Youtu Lab

Face Analysis Project on PyTorch and MXNet

You Only Look at One Sequence (https://arxiv.org/abs/2106.00666)

implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)

Neural Network Blocks - Collect all kinds of fancy model blocks for you to build more powerful neural network model.

Two-stage CenterNet

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

OpenMMLab Video Perception Toolbox. It supports Single Object Tracking (SOT), Multiple Object Tracking (MOT), Video Object Detection (VID) with a unified framework.

Differentiate images in python - get a ratio or percentage difference, and generate a diff image

Awesome multilingual OCR toolkits based on PaddlePaddle （practical ultra lightweight OCR system, provide data annotation and synthesis tools, support training and deployment among server, mobile, e…

OneNet: End-to-End One-Stage Object Detection

Pytorch implementation of "AutoAssign: Differentiable Label Assignment for Dense Object Detection"

李宏毅《深度强化学习》笔记，在线阅读地址：https://datawhalechina.github.io/leedeeprl-notes/

⚡Super fast and lightweight anchor-free object detection model. 🔥Only 1.8mb and run 97FPS on cellphone🔥

Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).