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alphafive

alphaGo版本的五子棋(gobang, gomoku)

aria2.sh

Aria2 一键安装管理脚本 增强版

binaryvit

BinaryViT: Pushing Binary Vision Transformers Towards Convolutional Models

bmxnet-v2

BMXNet 2: An Open-Source Binary Neural Network Implementation Based on MXNet

caffe

Caffe for Sparse and Low-rank Deep Neural Networks

caffe_ssd

cocoapi

COCO API - Dataset @ http://cocodataset.org/

cornernet

cornernet-lite

crossstagepartialnetworks

Cross Stage Partial Networks

csp

High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection, CVPR, 2019

darknet

Windows and Linux version of Darknet Yolo v3 & v2 Neural Networks for object detection (Tensor Cores are used)

deep-high-resolution-net.pytorch

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

deepcompression-pytorch

Learning both Weights and Connections for Efficient Neural Networks https://arxiv.org/abs/1506.02626

faster_rcnn_for_dota

Code used for training Faster R-CNN on DOTA

hpt

HPT - Open Multimodal LLMs from HyperGAI

ir-net

This project is the PyTorch implementation of our accepted CVPR 2020 paper : forward and backward information retention for accurate binary neural networks.

mgan

Mask-Guided Attention Network for Occluded Pedestrian Detection. (ICCV'19)

mmdetection-v2.0

OpenMMLab Detection Toolbox and Benchmark

mobilenet-caffe

Caffe Implementation of Google's MobileNets (v1 and v2)

mobilenetv2-yolov3

use mobilenetv2 replace darknet53 for yolov3 detection

ncnn

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

pytorch-yolov3

Minimal PyTorch implementation of YOLOv3

reinspect

End to end detection in crowded scenes

rrc_detection

Accurate Single Stage Detector Using Recurrent Rolling Convolution

senet

Squeeze-and-Excitation Networks

shufflenet

This is a fast caffe implementation of ShuffleNet.

siammask

[CVPR2019] Fast Online Object Tracking and Segmentation: A Unifying Approach

sift

slimming

Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.