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andrew-ng-meachine-learning

coursera中Andrew Ng的meachine learning的所有编程测验的原文件

cityscapesscripts

README and scripts for the Cityscapes Dataset

darknet

Convolutional Neural Networks

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

deeplabv3

deeplabv3_pytorch

deeplearning_lhy21_notes

深度学习 李宏毅 2021 学习笔记

fcn

flexible-yolov5

More readable and flexible yolov5 with more backbone(resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer) and (cbam，dcn and so on), and tensorrt

mlofandrew-ng

吴恩达机器学习课程的讲义，欢迎大家一起学习

mobilenet-yolov4-lite-pytorch

这是一个mobilenet-yolov4-lite的库，把yolov4主干网络修改成了mobilenet，修改了Panet的卷积组成，使参数量大幅度缩小。

models

Models and examples built with TensorFlow

myfcn

myyolov5

python-100-days

Python - 100天从新手到大师

pytorch-handbook-chinese

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

smart_construction

Head Person Helmet Detection on Construction Sites，基于目标检测工地安全帽和禁入危险区域识别系统，🚀😆附 YOLOv5 训练自己的数据集超详细教程🚀😆2021.3新增可视化界面❗❗

tensorrt_pro

C++ library based on tensorrt integration

tf2-yolov4

yolov4 base on keras of tf2+, tf2 support perfectly

torch-template-for-deep-learning

Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

tph-yolov5

trt_pose

Real-time pose estimation accelerated with NVIDIA TensorRT

unet

yolo-tensorrt

TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.

yoloair

🔥🔥🔥YOLOv5, YOLOv6, YOLOv7, PPYOLOE, YOLOX, YOLOR, YOLOv4, YOLOv3, PPYOLO, PPYOLOv2, Transformer, Attention, TOOD and Improved-YOLOv5-YOLOv7... Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀

yolov2_xilinx_fpga

A demo for accelerating YOLOv2 in xilinx's fpga pynq/zedboard

yolov34-cpp-opencv-dnn

4种YOLO目标检测，C++和Python两个版本的实现

yolov4-mobilenet-pytorch

这是一个简化后的YOLOv4模型，主干网络使用的是MobileNet系列。将简化后的模型应用在英伟达Jetson AGX Xavier上，测得视频流检测的速度达到12FPS。

yolov4-pytorch

这是一个YoloV4-pytorch的源码，可以用于训练自己的模型。

yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite