Darknet,YOLO 对象检测框架.zip

# Table of Contents * [Darknet Object Detection Framework and YOLO](#darknet-object-detection-framework-and-yolo) * [Papers](#papers) * [General Information](#general-information) * [Darknet Version](#darknet-version) * [MSCOCO Pre-trained Weights](#mscoco-pre-trained-weights) * [Building](#building) * [Google Colab](#google-colab) * [WSL](#wsl) * [Linux CMake Method](#linux-cmake-method) * [Windows CMake Method](#windows-cmake-method) * [Using Darknet](#using-darknet) * [CLI](#cli) * [Training](#training) * [Other Tools and Links](#other-tools-and-links) * [Roadmap](#roadmap) * [Short-term goals](#short-term-goals) * [Mid-term goals](#mid-term-goals) * [Long-term goals](#long-term-goals) # Darknet Object Detection Framework and YOLO  Darknet is an open source neural network framework written in C, C++, and CUDA. YOLO (You Only Look Once) is a state-of-the-art, real-time, object detection system, which runs in the Darknet framework. * Read how **[Hank.ai is helping the Darknet/YOLO community](https://hank.ai/darknet-welcomes-hank-ai-as-official-sponsor-and-commercial-entity/)** * Announcing **[Darknet V3 "Jazz"](https://hank.ai/announcing-darknet-v3-a-quantum-leap-in-open-source-object-detection/)** * See the **[Darknet/YOLO web site](https://darknetcv.ai/)** * Please read through the **[Darknet/YOLO FAQ](https://www.ccoderun.ca/programming/darknet_faq/)** * Join the **[Darknet/YOLO discord server](https://discord.gg/zSq8rtW)** # Papers * Paper **[YOLOv7](https://arxiv.org/abs/2207.02696)** * Paper **[Scaled-YOLOv4](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html)** * Paper **[YOLOv4](https://arxiv.org/abs/2004.10934)** * Paper **[YOLOv3](https://arxiv.org/abs/1804.02767)** # General Information The Darknet/YOLO framework continues to be both **faster** and **more accurate** than other frameworks and versions of YOLO. This framework is both **completely free** and **open source**. You can incorporate Darknet/YOLO into existing projects and products -- including commercial ones -- without a license or paying a fee. Darknet V3 ("Jazz") released in October 2024 can accurately run the LEGO dataset videos at up to **1000 FPS** when using a NVIDIA RTX 3090 GPU, meaning each video frame is read, resized, and processed by Darknet/YOLO in 1 millisecond or less. Please join the Darknet/YOLO Discord server if you need help or you want to discuss Darknet/YOLO: https://discord.gg/zSq8rtW The CPU version of Darknet/YOLO can run on simple devices such as Raspberry Pi, cloud &amp; colab servers, desktops, laptops, and high-end training rigs. The GPU version of Darknet/YOLO requires a CUDA-capable GPU from NVIDIA. Darknet/YOLO is known to work on Linux, Windows, and Mac. See the [building instructions](#building) below. ## Darknet Version * The original Darknet tool written by Joseph Redmon in 2013-2017 did not have a version number. We consider this version 0.x. * The next popular Darknet repo maintained by Alexey Bochkovskiy between 2017-2021 also did not have a version number. We consider this version 1.x. * The Darknet repo sponsored by Hank.ai and maintained by Stéphane Charette starting in 2023 was the first one with a `version` command. From 2023 until late 2024, it returned version 2.x "OAK". * The goal was to try and break as little of the existing functionality while getting familiar with the codebase. * Re-wrote the build steps so we have 1 unified way to build using CMake on both Windows and Linux. * Converted the codebase to use the C++ compiler. * Enhanced chart.png while training. * Bug fixes and performance-related optimizations, mostly related to cutting down the time it takes to train a network. * The last branch of this codebase is version 2.1 in the `v2` branch. * The next phase of development started in mid-2024 and was released in October 2024. The `version` command now returns 3.x "JAZZ". * Removed many old and unmaintained commands. * You can always do a checkout of the previous `v2` branch if you need to run one of these commands. Let us know so we can investigate adding back any missing commands. * Many performance optimizations, both when training and during inference. * Legacy C API was modified; applications that use the original Darknet API will need minor modifications: https://darknetcv.ai/api/api.html * New Darknet V3 C and C++ API: https://darknetcv.ai/api/api.html * New apps and sample code in `src-examples`: https://darknetcv.ai/api/files.html # MSCOCO Pre-trained Weights Several popular versions of YOLO were pre-trained for convenience on the [MSCOCO dataset](https://cocodataset.org/). This dataset has 80 classes, which can be seen in the text file [`cfg/coco.names`](cfg/coco.names). > There are several other simpler datasets and pre-trained weights available for testing Darknet/YOLO, such as LEGO Gears and Rolodex. See <a target="_blank" href="https://www.ccoderun.ca/programming/yolo_faq/#datasets">the Darknet/YOLO FAQ</a> for details. The MSCOCO pre-trained weights can be downloaded from several different locations, and are also available for download from this repo: * YOLOv2, November 2016 * [YOLOv2-tiny](https://github.com/hank-ai/darknet/issues/21#issuecomment-1807469361) * [YOLOv2-full](https://github.com/hank-ai/darknet/issues/21#issuecomment-1807478865) * YOLOv3, May 2018 * [YOLOv3-tiny](https://github.com/hank-ai/darknet/issues/21#issuecomment-1807479419) * [YOLOv3-full](https://github.com/hank-ai/darknet/issues/21#issuecomment-1807480139) * YOLOv4, May 2020 * [YOLOv4-tiny](https://github.com/hank-ai/darknet/issues/21#issuecomment-1807480542) * [YOLOv4-full](https://github.com/hank-ai/darknet/issues/21#issuecomment-1807481315) * YOLOv7, August 2022 * [YOLOv7-tiny](https://github.com/hank-ai/darknet/issues/21#issuecomment-1807483279) * [YOLOv7-full](https://github.com/hank-ai/darknet/issues/21#issuecomment-1807483787) The MSCOCO pre-trained weights are provided for demo-purpose only. The corresponding `.cfg` and `.names` files for MSCOCO are in [the cfg directory](cfg/). Example commands: ```sh wget --no-clobber https://github.com/hank-ai/darknet/releases/download/v2.0/yolov4-tiny.weights darknet_02_display_annotated_images coco.names yolov4-tiny.cfg yolov4-tiny.weights image1.jpg darknet_03_display_videos coco.names yolov4-tiny.cfg yolov4-tiny.weights video1.avi DarkHelp coco.names yolov4-tiny.cfg yolov4-tiny.weights image1.jpg DarkHelp coco.names yolov4-tiny.cfg yolov4-tiny.weights video1.avi ``` Note that people are expected to [train their own networks](#training). MSCOCO is normally used to confirm that everything is working correctly. # Building The various build methods available in the past (pre-2023) have been merged together into a single unified solution. Darknet requires C++17 or newer, OpenCV, and uses CMake to generate the necessary project files. **You do not need to know C++ to build, install, nor run Darknet/YOLO, the same way you don't need to be a mechanic to drive a car.** * [Google Colab](#google-colab) * [WSL](#wsl) * [Linux](#linux-cmake-method) * [Windows](#windows-cmake-method) **Beware if you are following old tutorials with more complicated build steps, or build steps that don't match what is in this readme.** The new build steps as described below started in August 2023. Software developers are encouraged to visit https://darknetcv.ai/ to get information on the internals of the Darknet/YOLO object detection framework. ## Google Colab The Google Colab instructions are the same as the [Linux](#linux-cmake-method) instructions. Several Jupyter notebooks are available showing how to do certain tasks, such as training a new network. See the notebooks in the `colab` subdirectory, and/or follow the Linux instructions below. ## WSL If you have a modern version of