Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

这是TensorRT相关工具的代码，目前仅实现了ONNX转TensorRT的模型转换工具。

🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~

🔥🔥🔥专注于改进YOLOv8模型，NEW - YOLOv8 🚀 RT-DETR 🥇 in PyTorch >, Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀

A tool to modify ONNX models in a visualization fashion, based on Netron and Flask.

Collection of public available person re-identification datasets

🔥🔥🔥 专注于YOLOv5，YOLOv7、YOLOv8、YOLOv9改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀

🔥🔥🔥 专注于YOLOv5，YOLOv7、YOLOv8、YOLOv9改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules🚀

这是各种目标检测数据集预处理相关工具脚本集合

YOLOv3、YOLOv4、YOLOv5、YOLOv5-Lite、YOLOv6-v1、YOLOv6-v2、YOLOv7、YOLOX、YOLOX-Lite、PP-YOLOE、PP-PicoDet-Plus、YOLO-Fastest v2、FastestDet、YOLOv5-SPD、TensorRT、NCNN、Tengine、OpenVINO

Ultralytics YOLO11 🚀

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

这是YOLO系列算法在不同部署框架下的Python部署项目

🌟Change the world, it will become a better place. | 以科研和竞赛为导向的最好的YOLO实践框架!

分别使用OpenCV、ONNXRuntime部署YOLOV7目标检测，一共包含14个onnx模型，依然是包含C++和Python两个版本的程序

Simulated masked face with dataset CelebA

Convert face dataset to masked dataset

《CUDA编程基础与实践》一书的代码

通用深度学习推理工具，可在生产环境中快速上线由TensorFlow、PyTorch、Caffe框架训练出的深度学习模型。

A best practice for deep learning project template architecture.

This repository deploys YOLOv4 as an optimized TensorRT engine to Triton Inference Server

基于 Goland + Gin + Logrus+ OpenCv 4 + WebSocket + YoloV4 + Redis + Vue 3 的直播流（支持RTSP、RTMP、FLV、HLS）在线场景智能识别系统

yoloface大礼包 使用pytroch实现的基于yolov3的轻量级人脸检测（包含关键点）

Support Yolov5(4.0)/Yolov5(5.0)/YoloR/YoloX/Yolov4/Yolov3/CenterNet/CenterFace/RetinaFace/Classify/Unet. use darknet/libtorch/pytorch/mxnet to onnx to tensorrt

TensorRT-7 Network Lib 包括常用目标检测、关键点检测、人脸检测、OCR等 可训练自己数据

2020-2021年计算机视觉综述论文分方向整理

Solving Pickup and Delivery Problem by Optimization Software, Exact Algorithm, Heuristics, and Metaheuristics with Python.

🌍 针对小白的算法训练 | 包括四部分：①.大厂面经 ②.力扣图解 ③.千本开源电子书 ④.百张技术思维导图（项目花了上百小时，希望可以点 star 支持，🌹感谢~）推荐免费ChatGPT使用网站

这是论文Unsupervised Domain Adaptation by Backpropagation的复现tf2代码，并完成了MNIST与MNIST-M数据集之间的迁移训练