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Kale's Projects

bytetrack icon bytetrack

[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box

cvat icon cvat

Powerful and efficient Computer Vision Annotation Tool (CVAT)

deep-high-resolution-net.pytorch icon deep-high-resolution-net.pytorch

The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

deeplearning-500-questions icon deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

deepo icon deepo

Set up deep learning environment in a single command line.

higherhrnet-human-pose-estimation icon higherhrnet-human-pose-estimation

This is an official implementation of our CVPR 2020 paper "HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation" (https://arxiv.org/abs/1908.10357)

leetcode icon leetcode

leetcode 顺序刷题，详细通俗题解，with JAVA

mediapipe icon mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

mmpose icon mmpose

OpenMMLab Pose Estimation Toolbox and Benchmark.

mmtracking icon mmtracking

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

ncnn icon ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

opencv icon opencv

Open Source Computer Vision Library

paddledetection icon paddledetection

Object detection and instance segmentation toolkit based on PaddlePaddle.

simple-hrnet icon simple-hrnet

Multi-person Human Pose Estimation with HRNet in Pytorch

udp-pose icon udp-pose

Official code of The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation

yolox icon yolox

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

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