YOLOv3 Training Automation API for Linux

This repository allows you to get started with training a state-of-the-art Deep Learning model with little to no configuration needed! You provide your labeled dataset and you can start the training right away and monitor it in many different ways like TensorBoard or a custom REST API and GUI. Training with YOLOv3 has never been so easy.

Prerequisites

• Ubuntu 18.04 [16.04 could work, but not tested]
• Install dependencies:

chmod +x scripts/install_dependencies.sh && source scripts/install_dependencies.sh

• Install docker:

chmod +x scripts/install_docker.sh && source scripts/install_docker.sh

• Install NVIDIA Drivers and NVIDIA Docker for GPU training by following the official docs

At a glance

Prepare the docker image with all weights for GPU usage

sudo docker build -f docker/Dockerfile -t darknet_yolo_gpu:1 --build-arg GPU=1 --build-arg CUDNN=1 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 --build-arg DOWNLOAD_ALL=1 .

By default we include everything you will need inside this build! If you want to manually adjust the weights you can go to Preparing weights

After this step, jump directly to Preparing your dataset

Preparing Docker images

Once your environment is ready, you can prepare the docker images needed. The environment is dockerized to run on GPU or CPU. For GPU, you need to build the image in the following way:

sudo docker build -f docker/Dockerfile -t darknet_yolo_gpu:1 --build-arg GPU=1 --build-arg CUDNN=1 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 .

For CPU only, you can run the same command while setting GPU=0 CUDNN=0 and naming it darknet_yolo_cpu:1 for clarity.

sudo docker build -f docker/Dockerfile -t darknet_yolo_cpu:1 --build-arg GPU=0 --build-arg CUDNN=0 --build-arg CUDNN_HALF=0 --build-arg OPENCV=1 .

Preparing your dataset

We provided a sample_dataset to show how your data should be structured in order to start the training seemlesly. The train_config.json file found in sample_dataset is a copy of the template config/train_config.json.template with needed modifications. The template can as well be copied as is while making sure to remove the '.template' from the name. You can also provide your own train.txt and test.txt to specify which images will be used for training and which ones are for testing. If not provided, the dataset will be split according to the data/train_ratio (by default 80% train 20% test)

Starting the training

To start the training on GPU, make sure to add the execute permission on the .sh scripts (needed once only) by running

chmod +x *.sh
./run_docker_linux_gpu.sh

This will ask for 2 main inputs:

• The absolute path for the dataset
• The name of the container to run (which will be also a prefix for the training output) Once given, the training will start and you can stop it at any time by pressing CTRL+C inside the open terminal. Closing the terminal will result in stopping the running container.

Training output

Inside trainings you can find a folder with the naming convention <container_name>_<training_start_time>. For example it can be dogs-dataset_20191110_14:21:41. Inside this folder you will have the following structure.

dogs-dataset_20191110_14:21:41
├── config
│   ├── obj.data
│   ├── obj.names
│   └── yolov3.cfg
├── test.txt
├── train.txt
├── weights
│   ├── initial.weights
│   ├── yolov3_10000.weights
│   ├── yolov3_1000.weights
│   ├── yolov3_2000.weights
│   ├── yolov3_3000.weights
│   ├── yolov3_4000.weights
│   ├── yolov3_5000.weights
│   ├── yolov3_6000.weights
│   ├── yolov3_7000.weights
│   ├── yolov3_8000.weights
│   ├── yolov3_9000.weights
│   ├── yolov3_best.weights
│   └── yolov3_last.weights
├── yolo_events.log
└── yolo_events.log.1

Which shows the .cfg file and weights used for the training along with all checkpoints and the normal yolo log output inside yolo_events files.

Monitoring the training

You have 3 ways of monitoring the training.

Custom API

One REST API with its Swagger API is also started during the training so you can get the YOLO output log in a structured JSON format as well as test custom images on the latest saved weights. This can be accessed through port 8000 (or a custom port you can set inside training/custom_api/port)

TensorboardX

The loss and mAP can be visualized through Tensorboard which can be accessed on port 6006 (or a custom port you can set inside training/tensorboard/port)

AlexeyAB provided web_ui

This can be enabled by setting training/web_ui/enable to true in the train_config.json you provide during the training. It can later on be access through port 8090 (or a custom port you can set inside training/web_ui/port) and looks like the following:

Training config: Meaning

An explanation of different fields can be found in the json schema of the provided config, which can be found at config/train_config_schema.json Some of the elements are specific to YOLO itself like saturation, hue, rotation, max_batches and so on. Those are greatly explained by AlexeyAB in their darknet fork.

Benchmark

	OS
	Windows	Ubuntu
	CPU	CPU		GPU
	Intel Xeon CPU 2.3 GHz	Intel Xeon CPU 2.3 GHz	Intel Core i9-7900 3.3 GHz	GeForce GTX 1080
pascalvoc_dataset	0.793 second/image	0.885 second/image	0.295 second/image	0.0592 second/image

Preparing weights

Default yolo weights are provided on the official website. To download the different flavors, please use the following commands:

Change your current working directory to be inside the repo. The following steps will download

yolov3.weights

wget https://pjreddie.com/media/files/yolov3.weights -P config/darknet/yolo_default_weights

yolov3-tiny.weights

wget https://pjreddie.com/media/files/yolov3-tiny.weights -P config/darknet/yolo_default_weights

darknet53.conv.74

wget https://pjreddie.com/media/files/darknet53.conv.74 -P config/darknet/yolo_default_weights

Known Issues

Issue related to darknet itself can be filed in the correct repo. We did not make any changes to the darknet code itself.

• If you chose to build with GPU but in the configuration file did not provide "gpus" field, the training will run on gpu 0 by default
• If during training you see nan values for avg (loss) field - then training goes wrong, but if nan is in some other lines - then training goes well.
• If error Out of memory occurs then you should try increasing subdivisions to 16, 32 or 64 or have a smaller image size.
• If training finishes immediately without any error you should decrease batch size and subdivisions.

Acknowledgements

robotron.de

inmind.ai

Lynn Nassif, Beirut, Lebanon

Nour Azzi, Beirut, Lebanon