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akon-fiber's Projects

abandoned-objects icon abandoned-objects

Project that will identify abandoned and removed objects in a video sequence using OpenCV

aspl icon aspl

Active Self-Paced Learning for Cost-Effective and Progressive Face Identification

bgslibrary icon bgslibrary

A C++ Background Subtraction Library with wrappers for Python, MATLAB, Java and GUI on QT

captainblackboard icon captainblackboard

船长关于机器学习、计算机视觉和工程技术的总结和分享

centernet icon centernet

Object detection, 3D detection, and pose estimation using center point detection:

chinese-clip icon chinese-clip

Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.

detectandtrack icon detectandtrack

基于Tracking-by-dtetction的多目标跟踪算法,检测算法用的是Yolov3, 关联算法是匈牙利算法

examples icon examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

iccv19_attribute icon iccv19_attribute

Code for the paper "Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization", ICCV 2019, http://arxiv.org/abs/1910.04562.

milvus icon milvus

Vector database for scalable similarity search and AI applications.

nb icon nb

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

objleft icon objleft

:handbag: Abandoned object detection for visual surveillance

opencv_tutorial icon opencv_tutorial

基于OpenCV4.0 C++/Python SDK的案例代码演示程序与效果图像

opencv_tutorials icon opencv_tutorials

Opencv4.0 with python (English&中文), and will keep the update ! 👊

paddleclas icon paddleclas

A treasure chest for visual recognition powered by PaddlePaddle

playground icon playground

A central hub for gathering and showcasing amazing projects that extend OpenMMLab with SAM and other exciting features.

py-faster-rcnn icon py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version

pytorch-handbook icon pytorch-handbook

pytorch handbook是一本开源的书籍,目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门,其中包含的Pytorch教程全部通过测试保证可以成功运行

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