# Yolo-v3 and Yolo-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)
[![CircleCI](https://circleci.com/gh/AlexeyAB/darknet.svg?style=svg)](https://circleci.com/gh/AlexeyAB/darknet)
0. [Improvements in this repository](#improvements-in-this-repository)
1. [How to use](#how-to-use)
2. [How to compile on Linux](#how-to-compile-on-linux)
3. [How to compile on Windows](#how-to-compile-on-windows)
4. [How to train (Pascal VOC Data)](#how-to-train-pascal-voc-data)
5. [How to train (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects)
6. [When should I stop training](#when-should-i-stop-training)
7. [How to calculate mAP on PascalVOC 2007](#how-to-calculate-map-on-pascalvoc-2007)
8. [How to improve object detection](#how-to-improve-object-detection)
9. [How to mark bounded boxes of objects and create annotation files](#how-to-mark-bounded-boxes-of-objects-and-create-annotation-files)
10. [Using Yolo9000](#using-yolo9000)
11. [How to use Yolo as DLL](#how-to-use-yolo-as-dll)
| ![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png) | ![map_fps](https://hsto.org/webt/pw/zd/0j/pwzd0jb9g7znt_dbsyw9qzbnvti.jpeg) mAP (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf |
|---|---|
* YOLOv3-spp (is not indicated) better than YOLOv3 - mAP = 60.6%, FPS = 20: https://pjreddie.com/darknet/yolo/
* Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.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
# "You Only Look Once: Unified, Real-Time Object Detection (versions 2 & 3)"
A Yolo cross-platform Windows and Linux version (for object detection). Contributtors: https://github.com/pjreddie/darknet/graphs/contributors
This repository is forked from Linux-version: https://github.com/pjreddie/darknet
More details: http://pjreddie.com/darknet/yolo/
This repository supports:
* both Windows and Linux
* both OpenCV 2.x.x and OpenCV <= 3.4.0 (3.4.1 and higher isn't supported)
* both cuDNN v5-v7
* CUDA >= 7.5
* also create SO-library on Linux and DLL-library on Windows
##### Requires:
* **Linux GCC>=4.9 or Windows MS Visual Studio 2015 (v140)**: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409 (or offline [ISO image](https://go.microsoft.com/fwlink/?LinkId=615448&clcid=0x409))
* **CUDA 9.1**: https://developer.nvidia.com/cuda-91-download-archive
* **OpenCV 3.3.0**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.3.0/opencv-3.3.0-vc14.exe/download
* **or OpenCV 2.4.13**: https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.4.13/opencv-2.4.13.2-vc14.exe/download
- OpenCV allows to show image or video detection in the window and store result to file that specified in command line `-out_filename res.avi`
* **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality):
* `yolov3-openimages.cfg` (247 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-openimages.weights
* `yolov3-spp.cfg` (240 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-spp.weights
* `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights
* `yolov3-tiny.cfg` (34 MB COCO **Yolo v3 tiny**) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-tiny.weights
* `yolov2.cfg` (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights
* `yolo-voc.cfg` (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights
* `yolov2-tiny.cfg` (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights
* `yolov2-tiny-voc.cfg` (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights
* `yolo9000.cfg` (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights
Put it near compiled: darknet.exe
You can get cfg-files by path: `darknet/cfg/`
##### Examples of results:
[![Everything Is AWESOME](http://img.youtube.com/vi/VOC3huqHrss/0.jpg)](https://www.youtube.com/watch?v=VOC3huqHrss "Everything Is AWESOME")
Others: https://www.youtube.com/channel/UC7ev3hNVkx4DzZ3LO19oebg
### Improvements in this repository
* added support for Windows
* improved binary neural network performance **2x-4x times** for Detection on CPU and GPU if you trained your own weights by using this XNOR-net model (bit-1 inference) : https://github.com/AlexeyAB/darknet/blob/master/cfg/yolov3-tiny_xnor.cfg
* improved neural network performance **~7%** by fusing 2 layers into 1: Convolutional + Batch-norm
* improved neural network performance Detection **3x times**, Training **2 x times** on GPU Volta (Tesla V100, Titan V, ...) using Tensor Cores if `CUDNN_HALF` defined in the `Makefile` or `darknet.sln`
* improved performance **~1.2x** times on FullHD, **~2x** times on 4K, for detection on the video (file/stream) using `darknet detector demo`...
* improved performance **3.5 X times** of data augmentation for training (using OpenCV SSE/AVX functions instead of hand-written functions) - removes bottleneck for training on multi-GPU or GPU Volta
* improved performance of detection and training on Intel CPU with AVX (Yolo v3 **~85%**, Yolo v2 ~10%)
* fixed usage of `[reorg]`-layer
* optimized memory allocation during network resizing when `random=1`
* optimized initialization GPU for detection - we use batch=1 initially instead of re-init with batch=1
* added correct calculation of **mAP, F1, IoU, Precision-Recall** using command `darknet detector map`...
* added drawing of chart of average loss during training
* added calculation of anchors for training
* added example of Detection and Tracking objects: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp
* fixed code for use Web-cam on OpenCV 3.x
* run-time tips and warnings if you use incorrect cfg-file or dataset
* many other fixes of code...
And added manual - [How to train Yolo v3/v2 (to detect your custom objects)](#how-to-train-to-detect-your-custom-objects)
Also, you might be interested in using a simplified repository where is implemented INT8-quantization (+30% speedup and -1% mAP reduced): https://github.com/AlexeyAB/yolo2_light
### How to use:
##### Example of usage in cmd-files from `build\darknet\x64\`:
* `darknet_yolo_v3.cmd` - initialization with 236 MB **Yolo v3** COCO-model yolov3.weights & yolov3.cfg and show detection on the image: dog.jpg
* `darknet_voc.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and waiting for entering the name of the image file
* `darknet_demo_voc.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4
* `darknet_demo_store.cmd` - initialization with 194 MB VOC-model yolo-voc.weights & yolo-voc.cfg and play your video file which you must rename to: test.mp4, and store result to: res.avi
* `darknet_net_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from network video-camera mjpeg-stream (also from you phone)
* `darknet_web_cam_voc.cmd` - initialization with 194 MB VOC-model, play video from Web-Camera number #0
* `darknet_coco_9000.cmd` - initialization with 186 MB Yolo9000 COCO-model, and show detection on the image: dog.jpg
* `darknet_coco_9000_demo.cmd` - initialization with 186 MB Yolo9000 COCO-model, and show detection on the video (if it is present): street4k.mp4, and st
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编译好的darknet win7版本
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png:1520个
obj:153个
cfg:114个
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编译好的darknet win7版本 (2239个子文件)
libpthreadGC2.a 91KB
libpthreadGCE2.a 91KB
libpthreadGC2.a 91KB
gemm.c 83KB
image.c 59KB
detector.c 47KB
parser.c 41KB
getopt.c 39KB
classifier.c 37KB
data.c 37KB
convolutional_layer.c 34KB
go.c 26KB
network.c 24KB
region_layer.c 22KB
utils.c 16KB
yolo_layer.c 15KB
darknet.c 15KB
gru_layer.c 14KB
rnn.c 13KB
coco.c 13KB
yolo.c 12KB
detection_layer.c 12KB
demo.c 11KB
captcha.c 11KB
connected_layer.c 11KB
compare.c 11KB
batchnorm_layer.c 10KB
crnn_layer.c 10KB
box.c 9KB
nightmare.c 9KB
kmeansiou.c 9KB
rnn_layer.c 9KB
blas.c 9KB
local_layer.c 9KB
cifar.c 8KB
rnn_vid.c 7KB
deconvolutional_layer.c 6KB
normalization_layer.c 5KB
cuda.c 5KB
maxpool_layer.c 5KB
voxel.c 5KB
layer.c 5KB
tag.c 4KB
writing.c 4KB
cost_layer.c 4KB
route_layer.c 4KB
softmax_layer.c 4KB
matrix.c 4KB
activations.c 4KB
super.c 4KB
dice.c 4KB
tree.c 3KB
upsample_layer.c 3KB
reorg_layer.c 3KB
option_list.c 3KB
reorg_old_layer.c 3KB
shortcut_layer.c 3KB
crop_layer.c 3KB
swag.c 3KB
cpu_gemm.c 2KB
art.c 2KB
avgpool_layer.c 2KB
activation_layer.c 2KB
list.c 2KB
dropout_layer.c 2KB
gettimeofday.c 1KB
col2im.c 1KB
im2col.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
deconvolutional_kernels.cu.cache 1KB
activation_kernels.cu.cache 1KB
crop_layer_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
network_kernels.cu.cache 1KB
im2col_kernels.cu.cache 1KB
col2im_kernels.cu.cache 1KB
activation_kernels.cu.cache 1KB
crop_layer_kernels.cu.cache 1KB
blas_kernels.cu.cache 1KB
network_kernels.cu.cache 1KB
im2col_kernels.cu.cache 1KB
col2im_kernels.cu.cache 1KB
blas_kernels.cu.cache 1KB
densenet201_yolo.cfg 20KB
densenet201.cfg 19KB
densenet201.cfg 19KB
resnext152-32x4d.cfg 16KB
resnext152-32x4d.cfg 16KB
msr_152.cfg 16KB
msr_152.cfg 16KB
resnet152_yolo.cfg 15KB
resnet152.cfg 15KB
resnet152.cfg 15KB
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