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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
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.
强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/
YOLOv6: a single-stage object detection framework dedicated to industrial applications.
Reference models and tools for Cloud TPUs.
有趣的Python爬虫和Python数据分析小项目(Some interesting Python crawlers and data analysis projects)
Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
Tutorials for creating and using ONNX models
Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021, T-PAMI 2022
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
🎨 Semantic segmentation models, datasets and losses implemented in PyTorch.
《深度学习入门-基于Python的理论与实现》，包含源代码和高清PDF(带书签)；慕课网imooc《深度学习之神经网络(CNN-RNN-GAN)算法原理-实战》；《菜菜的机器学习sklearn》
mobilev2-yolov5s剪枝、蒸馏，支持ncnn，tensorRT部署。ultra-light but better performence！
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
interactive notebooks from Planet Engineering
A series of notebooks describing how to use YOLO (darkflow) in python
Better, faster, peering over yonder. Improved large scale object detection in aerial/satellite imagery.
Multi-task learning smile detection, age and gender classification on GENKI4k, IMDB-Wiki dataset.
这个项目是基于论文YOLO9000: Better, Faster, Stronger的keras（backend：tensorflow）实现
Implemented the YOLO algorithm for scene text detection in keras-tensorflow (No object detection API used) The code can be tweaked to train for a different object detection task using YOLO.
This repository is designated to detecting trucks using Sentinel-2 data.
Use of multi-temporal SAR data and optical image with the use of cGAN to create synthetise cloud free optical image