YOLOv4: Darknet 如何于 Docker 编译，及训练 COCO 子集

YOLO 算法是非常著名的目标检测算法。从其全称 You Only Look Once: Unified, Real-Time Object Detection ，可以看出它的特性：

Look Once: one-stage (one-shot object detectors) 算法，把目标检测的两个任务分类和定位一步完成。
Unified: 统一的架构，提供 end-to-end 的训练和预测。
Real-Time: 实时性，初代论文给出的指标 FPS 45 , mAP 63.4 。

YOLOv4: Optimal Speed and Accuracy of Object Detection ，于今年 4 月公布，采用了很多近些年 CNN 领域优秀的优化技巧。其平衡了精度与速度，目前在实时目标检测算法中精度是最高的。

论文地址：

YOLO: https://arxiv.org/abs/1506.02640
YOLO v4: https://arxiv.org/abs/2004.10934

源码地址：

YOLO: https://github.com/pjreddie/darknet
YOLO v4: https://github.com/AlexeyAB/darknet

本文将介绍 YOLOv4 官方 Darknet 实现，如何于 Docker 编译使用。以及从 MS COCO 2017 数据集中怎么选出部分物体，训练出模型。

主要内容有：

准备 Docker 镜像
准备 COCO 数据集
用预训练模型进行推断
准备 COCO 数据子集
训练自己的模型并推断
参考内容

准备 Docker 镜像

首先，准备 Docker ，请见：Docker: Nvidia Driver, Nvidia Docker 推荐安装步骤。

之后，开始准备镜像，从下到上的层级为：

nvidia/cuda: https://hub.docker.com/r/nvidia/cuda
OpenCV: https://github.com/opencv/opencv
Darknet: https://github.com/AlexeyAB/darknet

nvidia/cuda

准备 Nvidia 基础 CUDA 镜像。这里我们选择 CUDA 10.2 ，不用最新 CUDA 11，因为现在 PyTorch 等都还都是 10.2 呢。

拉取镜像：

docker pull nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04

测试镜像：

$ docker run --gpus all nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 nvidia-smi

Sun Aug  8 00:00:00 2020

+-----------------------------------------------------------------------------+

| NVIDIA-SMI 440.100      Driver Version: 440.100      CUDA Version: 10.2     |

|-------------------------------+----------------------+----------------------+

| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |

| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |

|===============================+======================+======================|

|   0  GeForce RTX 208...  Off  | 00000000:07:00.0  On |                  N/A |

|  0%   48C    P8    14W / 300W |    340MiB / 11016MiB |      2%      Default |

+-------------------------------+----------------------+----------------------+

|   1  GeForce RTX 208...  Off  | 00000000:08:00.0 Off |                  N/A |

|  0%   45C    P8    19W / 300W |      1MiB / 11019MiB |      0%      Default |

+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+

| Processes:                                                       GPU Memory |

|  GPU       PID   Type   Process name                             Usage      |

|=============================================================================|

+-----------------------------------------------------------------------------+

OpenCV

基于 nvidia/cuda 镜像，构建 OpenCV 的镜像：

cd docker/ubuntu18.04-cuda10.2/opencv4.4.0/

docker build \

-t joinaero/ubuntu18.04-cuda10.2:opencv4.4.0 \

--build-arg opencv_ver=4.4.0 \

--build-arg opencv_url=https://gitee.com/cubone/opencv.git \

--build-arg opencv_contrib_url=https://gitee.com/cubone/opencv_contrib.git \

.

其 Dockerfile 可见这里： https://github.com/ikuokuo/start-yolov4/blob/master/docker/ubuntu18.04-cuda10.2/opencv4.4.0/Dockerfile 。

Darknet

基于 OpenCV 镜像，构建 Darknet 镜像：

cd docker/ubuntu18.04-cuda10.2/opencv4.4.0/darknet/

docker build \

-t joinaero/ubuntu18.04-cuda10.2:opencv4.4.0-darknet \

.

其 Dockerfile 可见这里： https://github.com/ikuokuo/start-yolov4/blob/master/docker/ubuntu18.04-cuda10.2/opencv4.4.0/darknet/Dockerfile 。

上述镜像已上传 Docker Hub 。如果 Nvidia 驱动能够支持 CUDA 10.2 ，那可以直接拉取该镜像：

docker pull joinaero/ubuntu18.04-cuda10.2:opencv4.4.0-darknet

准备 COCO 数据集

MS COCO 2017 下载地址: http://cocodataset.org/#download

图像，包括：

2017 Train images [118K/18GB]
- http://images.cocodataset.org/zips/train2017.zip
2017 Val images [5K/1GB]
- http://images.cocodataset.org/zips/val2017.zip
2017 Test images [41K/6GB]
- http://images.cocodataset.org/zips/test2017.zip
2017 Unlabeled images [123K/19GB]
- http://images.cocodataset.org/zips/unlabeled2017.zip

标注，包括：

2017 Train/Val annotations [241MB]
- http://images.cocodataset.org/annotations/annotations_trainval2017.zip
2017 Stuff Train/Val annotations [1.1GB]
- http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip
2017 Panoptic Train/Val annotations [821MB]
- http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip
2017 Testing Image info [1MB]
- http://images.cocodataset.org/annotations/image_info_test2017.zip
2017 Unlabeled Image info [4MB]
- http://images.cocodataset.org/annotations/image_info_unlabeled2017.zip

用预训练模型进行推断

预训练模型 yolov4.weights ，下载地址 https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights 。

运行镜像：

xhost +local:docker

docker run -it --gpus all \

-e DISPLAY \

-e QT_X11_NO_MITSHM=1 \

-v /tmp/.X11-unix:/tmp/.X11-unix \

-v $HOME/.Xauthority:/root/.Xauthority \

--name darknet \

--mount type=bind,source=$HOME/Codes/devel/datasets/coco2017,target=/home/coco2017 \

--mount type=bind,source=$HOME/Codes/devel/models/yolov4,target=/home/yolov4 \

joinaero/ubuntu18.04-cuda10.2:opencv4.4.0-darknet

进行推断：

./darknet detector test cfg/coco.data cfg/yolov4.cfg /home/yolov4/yolov4.weights \

-thresh 0.25 -ext_output -show -out /home/coco2017/result.json \

/home/coco2017/test2017/000000000001.jpg

推断结果：

准备 COCO 数据子集

MS COCO 2017 数据集有 80 个物体标签。我们从中选取自己关注的物体，重组个子数据集。

首先，获取样例代码：

git clone https://github.com/ikuokuo/start-yolov4.git

scripts/coco2yolo.py: COCO 数据集转 YOLO 数据集的脚本
scripts/coco/label.py: COCO 数据集的物体标签有哪些
cfg/coco/coco.names: 编辑我们想要的那些物体标签

之后，准备数据集：

cd start-yolov4/

pip install -r scripts/requirements.txt

export COCO_DIR=$HOME/Codes/devel/datasets/coco2017

# train

python scripts/coco2yolo.py \

--coco_img_dir $COCO_DIR/train2017/ \

--coco_ann_file $COCO_DIR/annotations/instances_train2017.json \

--yolo_names_file ./cfg/coco/coco.names \

--output_dir ~/yolov4/coco2017/ \

--output_name train2017 \

--output_img_prefix /home/yolov4/coco2017/train2017/

# valid

python scripts/coco2yolo.py \

--coco_img_dir $COCO_DIR/val2017/ \

--coco_ann_file $COCO_DIR/annotations/instances_val2017.json \

--yolo_names_file ./cfg/coco/coco.names \

--output_dir ~/yolov4/coco2017/ \

--output_name val2017 \

--output_img_prefix /home/yolov4/coco2017/val2017/

数据集，内容如下：

~/yolov4/coco2017/

├── train2017/

│   ├── 000000000071.jpg

│   ├── 000000000071.txt

│   ├── ...

│   ├── 000000581899.jpg

│   └── 000000581899.txt

├── train2017.txt

├── val2017/

│   ├── 000000001353.jpg

│   ├── 000000001353.txt

│   ├── ...

│   ├── 000000579818.jpg

│   └── 000000579818.txt

└── val2017.txt

训练自己的模型并推断

准备必要文件

cfg/coco/coco.names <cfg/coco/coco.names.bak has original 80 objects>
- Edit: keep desired objects
cfg/coco/yolov4.cfg <cfg/coco/yolov4.cfg.bak is original file>
- Download yolov4.cfg, then changed:
- batch=64, subdivisions=32 <32 for 8-12 GB GPU-VRAM>
- width=512, height=512 <any value multiple of 32>
- classes=<your number of objects in each of 3 [yolo]-layers>
- max_batches=<classes*2000, but not less than number of training images and not less than 6000>
- steps=<max_batches*0.8, max_batches*0.9>
- filters=<(classes+5)x3, in the 3 [convolutional] before each [yolo] layer>
- filters=<(classes+9)x3, in the 3 [convolutional] before each [Gaussian_yolo] layer>
cfg/coco/coco.data
- Edit: train, valid to YOLO datas

csdarknet53-omega.conv.105

Download csdarknet53-omega_final.weights, then run:

docker run -it --rm --gpus all \

--mount type=bind,source=$HOME/Codes/devel/models/yolov4,target=/home/yolov4 \

joinaero/ubuntu18.04-cuda10.2:opencv4.4.0-darknet

./darknet partial cfg/csdarknet53-omega.cfg /home/yolov4/csdarknet53-omega_final.weights /home/yolov4/csdarknet53-omega.conv.105 105

训练自己的模型

运行镜像：

cd start-yolov4/

xhost +local:docker

docker run -it --gpus all \

-e DISPLAY \

-e QT_X11_NO_MITSHM=1 \

-v /tmp/.X11-unix:/tmp/.X11-unix \

-v $HOME/.Xauthority:/root/.Xauthority \

--name darknet \

--mount type=bind,source=$HOME/Codes/devel/models/yolov4,target=/home/yolov4 \

--mount type=bind,source=$HOME/yolov4/coco2017,target=/home/yolov4/coco2017 \

--mount type=bind,source=$PWD/cfg/coco,target=/home/cfg \

joinaero/ubuntu18.04-cuda10.2:opencv4.4.0-darknet

进行训练：

mkdir -p /home/yolov4/coco2017/backup

# Training command

./darknet detector train /home/cfg/coco.data /home/cfg/yolov4.cfg /home/yolov4/csdarknet53-omega.conv.105 -map

中途可以中断训练，然后这样继续：

# Continue training

./darknet detector train /home/cfg/coco.data /home/cfg/yolov4.cfg /home/yolov4/coco2017/backup/yolov4_last.weights -map

yolov4_last.weights 每迭代 100 次，会被记录。

如果多 GPU 训练，可以在 1000 次迭代后，加参数 -gpus 0,1 ，再继续：

# How to train with multi-GPU

# 1. Train it first on 1 GPU for like 1000 iterations

# 2. Then stop and by using partially-trained model `/backup/yolov4_1000.weights` run training with multigpu

./darknet detector train /home/cfg/coco.data /home/cfg/yolov4.cfg /home/yolov4/coco2017/backup/yolov4_1000.weights -gpus 0,1 -map

训练过程，记录如下：

加参数 -map 后，上图会显示有红线 mAP。

查看模型 mAP@IoU=50 精度：

$ ./darknet detector map /home/cfg/coco.data /home/cfg/yolov4.cfg /home/yolov4/coco2017/backup/yolov4_final.weights

...

Loading weights from /home/yolov4/coco2017/backup/yolov4_final.weights...

 seen 64, trained: 384 K-images (6 Kilo-batches_64)

Done! Loaded 162 layers from weights-file

 calculation mAP (mean average precision)...

 Detection layer: 139 - type = 27

 Detection layer: 150 - type = 27

 Detection layer: 161 - type = 27

160

 detections_count = 745, unique_truth_count = 190

class_id = 0, name = train, ap = 80.61%   	 (TP = 142, FP = 18)

 for conf_thresh = 0.25, precision = 0.89, recall = 0.75, F1-score = 0.81

 for conf_thresh = 0.25, TP = 142, FP = 18, FN = 48, average IoU = 75.31 %

 IoU threshold = 50 %, used Area-Under-Curve for each unique Recall

 mean average precision (mAP@0.50) = 0.806070, or 80.61 %

Total Detection Time: 4 Seconds

进行推断：

./darknet detector test /home/cfg/coco.data /home/cfg/yolov4.cfg /home/yolov4/coco2017/backup/yolov4_final.weights \

-ext_output -show /home/yolov4/coco2017/val2017/000000006040.jpg

推断结果：

参考内容

结语

为什么用 Docker ？ Docker 导出镜像，可简化环境部署。如 PyTorch 也都有镜像，可以直接上手使用。

关于 Darknet 还有什么？下回介绍 Darknet 于 Ubuntu 编译，及使用 Python 接口。

Let's go coding ~