# Yolo-v4 and Yolo-v3/v2 for Windows and Linux
### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-cmake-gui)
Paper Yolo v4: https://arxiv.org/abs/2004.10934
More details: http://pjreddie.com/darknet/yolo/
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* [Requirements (and how to install dependecies)](#requirements)
* [Pre-trained models](#pre-trained-models)
* [Explanations in issues](https://github.com/AlexeyAB/darknet/issues?q=is%3Aopen+is%3Aissue+label%3AExplanations)
* [Yolo v3 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn, TVM,...)](#yolo-v3-in-other-frameworks)
* [Datasets](#datasets)
0. [Improvements in this repository](#improvements-in-this-repository)
1. [How to use](#how-to-use-on-the-command-line)
2. How to compile on Linux
* [Using cmake](#how-to-compile-on-linux-using-cmake)
* [Using make](#how-to-compile-on-linux-using-make)
3. How to compile on Windows
* [Using CMake-GUI](#how-to-compile-on-windows-using-cmake-gui)
* [Using vcpkg](#how-to-compile-on-windows-using-vcpkg)
* [Legacy way](#how-to-compile-on-windows-legacy-way)
4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data)
5. [How to train with multi-GPU:](#how-to-train-with-multi-gpu)
6. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects)
7. [How to train tiny-yolo (to detect your custom objects)](#how-to-train-tiny-yolo-to-detect-your-custom-objects)
8. [When should I stop training](#when-should-i-stop-training)
9. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007)
10. [How to improve object detection](#how-to-improve-object-detection)
11. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files)
12. [How to use Yolo as DLL and SO libraries](#how-to-use-yolo-as-dll-and-so-libraries)
| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) | ![readme](https://user-images.githubusercontent.com/4096485/80213782-5f1e3480-8642-11ea-8fdf-0e6b9a6b5f4c.png) AP50:95 / AP50 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2004.10934 |
|---|---|
* Yolo v4 Full comparison: [map_fps](https://user-images.githubusercontent.com/4096485/80213824-6e9d7d80-8642-11ea-94a6-0be90c7d7cd5.png)
* CSPNet: [map_fps](https://user-images.githubusercontent.com/4096485/71702416-6645dc00-2de0-11ea-8d65-de7d4b604021.png) [paper](https://arxiv.org/abs/1911.11929) Comparison: https://github.com/WongKinYiu/CrossStagePartialNetworks
* Yolo v3 on MS COCO: [Speed / Accuracy (mAP@0.5) chart](https://user-images.githubusercontent.com/4096485/52151356-e5d4a380-2683-11e9-9d7d-ac7bc192c477.jpg)
* Yolo v3 on MS COCO (Yolo v3 vs RetinaNet) - Figure 3: https://arxiv.org/pdf/1804.02767v1.pdf
* Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg
* Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg
#### How to evaluate AP of YOLOv4 on the MS COCO evaluation server
1. Download and unzip test-dev2017 dataset from MS COCO server: http://images.cocodataset.org/zips/test2017.zip
2. Download list of images for Detection taks and replace the paths with yours: https://raw.githubusercontent.com/AlexeyAB/darknet/master/scripts/testdev2017.txt
3. Download `yolov4.weights` file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT
4. Content of the file `cfg/coco.data` should be
```
classes= 80
train = <replace with your path>/trainvalno5k.txt
valid = <replace with your path>/testdev2017.txt
names = data/coco.names
backup = backup
eval=coco
```
5. Create `/results/` folder near with `./darknet` executable file
6. Run validation: `./darknet detector valid cfg/coco.data cfg/yolov4.cfg yolov4.weights`
7. Rename the file `/results/coco_results.json` to `detections_test-dev2017_yolov4_results.json` and compress it to `detections_test-dev2017_yolov4_results.zip`
8. Submit file `detections_test-dev2017_yolov4_results.zip` to the MS COCO evaluation server for the `test-dev2019 (bbox)`
#### How to evaluate FPS of YOLOv4 on GPU
1. Compile Darknet with `GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1` in the `Makefile` (or use the same settings with Cmake)
2. Download `yolov4.weights` file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT
3. Get any .avi/.mp4 video file (preferably not more than 1920x1080 to avoid bottlenecks in CPU performance)
4. Run one of two commands and look at the AVG FPS:
* include video_capturing + NMS + drawing_bboxes:
`./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -dont_show -ext_output`
* exclude video_capturing + NMS + drawing_bboxes:
`./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -benchmark`
#### Pre-trained models
There are weights-file for different cfg-files (trained for MS COCO dataset):
FPS on RTX 2070 (R) and Tesla V100 (V):
* [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT) paper [Yolo v4](https://arxiv.org/abs/2004.10934)
* `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops
* `width=512 height=512` in cfg: **64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS** - 91.1 BFlops
* `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops
* `width=320 height=320` in cfg: **60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops
* [yolov3-tiny-prn.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 370(R) FPS** - 3.5 BFlops - 18.8 MB: [yolov3-tiny-prn.weights](https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing)
* [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 55(R) FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view)
* [yolov3-openimages.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg) - 247 MB - 18(R) FPS - OpenImages dataset: [yolov3-openimages.weights](https://pjreddie.com/media/files/yolov3-openimages.weights)
<details><summary><b>CLICK ME</b> - Yolo v3 models</summary>
* [csresnext50-panet-spp-original-optimal.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp-original-optimal.cfg) - **65.4% mAP@0.5 (43.2% AP@0.5:0.95) - 32(R) FPS** - 100.5 BFlops - 217 MB: [csresnext50-panet-spp-original-optimal_final.weights](https://drive.google.com/open?id=1_NnfVgj0EDtb_WLNoXV8Mo7WKgwdYZCc)
* [yolov3-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg) - **60.6% mAP@0.5 - 38(R) FPS** - 141.5 BFlops - 240 MB: [yolov3-spp.weights](https://pjreddie.com/media/files/yol
没有合适的资源?快使用搜索试试~ 我知道了~
win10+cuda10.2+VS2019+YOLOV4.zip
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"win10+cuda10.2+VS2019+YOLOV4.zip" 提供了一个在Windows 10操作系统上搭建YOLOv4深度学习环境的详细配置,其中涉及到的主要组件包括CUDA 10.2、Visual Studio 2019(VS2019)以及OpenCV 3.4.0。这个压缩包文件包含了名为“darknet-master”的源代码目录,是YOLOv4的实现。 YOLO(You Only Look Once)是一种实时目标检测系统,以其快速高效而著名。YOLOv4是该系列的最新版本,在YOLOv3的基础上进行了多方面的优化,提升了检测性能和精度,同时保持了较高的运行速度。 CUDA是NVIDIA推出的GPU编程接口,用于加速计算密集型任务,如深度学习。CUDA 10.2版本支持多种计算功能,并且与许多深度学习框架兼容,包括YOLO。在Windows 10上安装CUDA,你需要下载并运行安装程序,确保选择正确的驱动和CUDA工具包版本,然后按照提示进行安装。 Visual Studio 2019是Microsoft的旗舰级集成开发环境,对于C++和CUDA编程提供了很好的支
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win10+cuda10.2+VS2019+YOLOV4.zip (2000个子文件)
libpthreadGC2.a 91KB
gemm.c 102KB
parser.c 81KB
detector.c 72KB
data.c 66KB
convolutional_layer.c 59KB
conv_lstm_layer.c 47KB
image.c 47KB
network.c 44KB
classifier.c 44KB
yolo_layer.c 36KB
gaussian_yolo_layer.c 35KB
box.c 29KB
go.c 26KB
lstm_layer.c 25KB
utils.c 22KB
region_layer.c 22KB
darknet.c 19KB
dark_cuda.c 16KB
batchnorm_layer.c 16KB
blas.c 16KB
gru_layer.c 15KB
coco.c 14KB
connected_layer.c 14KB
maxpool_layer.c 14KB
rnn.c 14KB
crnn_layer.c 14KB
demo.c 14KB
getopt.c 13KB
shortcut_layer.c 12KB
yolo.c 12KB
layer.c 12KB
detection_layer.c 12KB
captcha.c 11KB
compare.c 11KB
activations.c 11KB
rnn_layer.c 10KB
nightmare.c 9KB
kmeansiou.c 9KB
local_layer.c 9KB
cifar.c 8KB
matrix.c 8KB
rnn_vid.c 7KB
deconvolutional_layer.c 6KB
normalization_layer.c 6KB
route_layer.c 5KB
scale_channels_layer.c 5KB
voxel.c 5KB
tag.c 4KB
writing.c 4KB
cost_layer.c 4KB
softmax_layer.c 4KB
im2col.c 4KB
super.c 4KB
tree.c 4KB
dice.c 4KB
col2im.c 4KB
sam_layer.c 3KB
option_list.c 3KB
upsample_layer.c 3KB
reorg_layer.c 3KB
reorg_old_layer.c 3KB
dropout_layer.c 3KB
crop_layer.c 3KB
swag.c 2KB
cpu_gemm.c 2KB
avgpool_layer.c 2KB
list.c 2KB
activation_layer.c 2KB
art.c 2KB
gettimeofday.c 1KB
deconvolutional_kernels.cu.cache 1KB
avgpool_layer_kernels.cu.cache 1KB
maxpool_layer_kernels.cu.cache 1KB
convolutional_kernels.cu.cache 1KB
dropout_layer_kernels.cu.cache 1KB
activation_kernels.cu.cache 1KB
crop_layer_kernels.cu.cache 1KB
network_kernels.cu.cache 1KB
col2im_kernels.cu.cache 1KB
im2col_kernels.cu.cache 1KB
blas_kernels.cu.cache 1KB
resnet152_trident.cfg 23KB
densenet201_yolo.cfg 20KB
densenet201.cfg 19KB
resnext152-32x4d.cfg 16KB
resnet152_yolo.cfg 15KB
resnet152.cfg 15KB
efficientnet-lite3.cfg 12KB
enet-coco.cfg 12KB
yolov4.cfg 12KB
efficientnet_b0.cfg 11KB
csresnext50-panet-spp-original-optimal.cfg 11KB
yolov3_5l.cfg 10KB
resnet101.cfg 10KB
yolov3-voc.yolov3-giou-40.cfg 8KB
Gaussian_yolov3_BDD.cfg 8KB
yolov3.coco-giou-12.cfg 8KB
yolov3-spp.cfg 8KB
yolov3.cfg 8KB
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