Tensorflow 2 Object Detection Training GUI for Linux

Updated for CUDA 11 and Tensorflow 2!!!

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 with TensorBoard. You can even test your model with our built-in Inference REST API. Training with TensorFlow has never been so easy.

You can also use our BMW-Labeltool-lite to label your dataset. The images and labels can be used directly for training.

This repository is based on Tensorflow Object Detection API
The tensorflow version used is in this repo is 2.5.0
All supported networks in this project are taken from the tensorflow model zoo
All training are made using pre-trained network weights.
The pre-trained weights that you can use out of the box are based on the COCO dataset.
The app was tested with Google Chrome and it is recommended to use Chrome when training.
This repository support training on both CPU and on multiple GPU (up to 2 GPUs)

Prerequisites

Ubuntu 18.04
NVIDIA Drivers (418.x or higher)
Docker CE latest stable release
NVIDIA Docker 2
Docker-Compose

Setting Up Project Requirements Automated

This step is recommended to be able to run the solution correctly.

The setup script will check and adjust all the requirements needed based on the user input.

Run the following command

chmod +x setup_solution_parameters.sh && source setup_solution_parameters.sh
- The script will check if docker and docker-compose are installed and if not, it will install them.
- You will be prompted to choose build architecture GPU/CPU for the training solution.
  - If you chose GPU the script will check if nvidia-docker is installed and if not, it will install it.
- The script will prompt all the network interfaces that are available so you can select which interface you want to extract the ip address from it
- You will be prompted to choose if you want to set up a proxy
- You will be prompted to select all the pre-trained weights network that you want to be downloaded during the docker image build (use the up/down arrow to go up and down, space to select/unselect, enter to submit your selection and esc to quit)

Setting Up Project Requirements Manually

How to check for prerequisites

To check if you have docker-ce installed:

docker --version

To check if you have docker-compose installed:

docker-compose --version

To check if you have nvidia-docker installed:

dpkg -l | grep nvidia-docker

To check your nvidia drivers version, open your terminal and type the command nvidia-smi

Installing Prerequisites

If you don't have neither docker nor docker-compose use the following command

chmod +x install_full.sh && source install_full.sh
If you have docker ce installed and wish only to install docker-compose and perform necessary operations, use the following command

chmod +x install_compose.sh && source install_compose.sh
Install NVIDIA Drivers (418.x or higher) and NVIDIA Docker for GPU training by following the official docs
Make sure that the .gitkeep files in datasets, checkpoints, tensorboards and inference_api/models folder are deleted. (.gitkeep files are placeholder files used for git)
Make sure that the base_dir field in docker_sdk_api/assets/paths.json is correct (it must match the path of the root of the repo on your machine).
Make sure that the image_name field in docker_sdk_api/assets/paths.json is correct (it must match your chosen architecture for the training tf2_training_api_cpu or tf2_training_api_gpu).
Go to gui/src/environments/environment.ts and gui/src/environments/environment.prod.ts and change the following:
- field url: must match the IP address of your machine
- the IP field of the inferenceAPIUrl : must match the IP address of your machine (Use the ifconfig command to check your IP address . Please use your private IP which starts by either 10. or 172.16. or 192.168.)
  
  environment.ts
  
  environment.prod.ts

If you are behind a proxy:

Enter you proxy settings in the <base-dir>/proxy.json file
Enter the following command:
```
  python3 set_proxy_args.py
```

Dataset Folder Structure

The following is an example of how a dataset should be structured. Please put all your datasets in the datasets folder.

├──datasets/
    ├──sample_dataset/
        ├── images
        │   ├── img_1.jpg
        │   └── img_2.jpg
        ├── labels
        │   ├── json
        │   │   ├── img_1.json
        │   │   └── img_2.json
        │   └── pascal
        │       ├── img_1.xml
        │       └── img_2.xml
        └── objectclasses.json

PS: you don't need to have both json and pascal folders. Either one is enough

If you want to label your images, you can use our BMW-LabelTool-Lite which is a free, open-source image annotation tool. This tool supports our JSON label format

Objectclasses.json file example

You must include in your dataset an objectclasses.json file with a similar structure to the example below:

Midweight and Heavyweight Solution

Midweight: Downloading specific supported online pre-trained weights during the docker image build.
To do that, open the json file training_api/assets/networks.json and change the values of the networks you wish to download to true.

Heavyweight (default): Downloading all the supported online pre-trained weights during the docker image build.
To do that, open the json file training_api/assets/networks.json and change the value of "select_all" to true.

PS: if you don’t download the weights during the build, you won’t be able to use the network during the training unless you rebuild the solution with the proper network chosen.

All the training are made using the pre-trained network weight based on coco dataset.

Build the Solution

If you wish want to deploy the training workflow in GPU mode, please write the following command from the repository's root directory

docker-compose -f build_gpu.yml build

If you wish want to deploy the training workflow in CPU mode, please write the following command from the repository's root directory

docker-compose -f build_cpu.yml build

Run the Solution

If you wish want to deploy the training workflow in GPU mode, please write the following command

docker-compose -f run_gpu.yml up

If you wish to deploy the training workflow in CPU mode, please write the following command

docker-compose -f run_cpu.yml up

After a successful run you should see something like the following:

Usage

If the app is deployed on your machine: open your web browser and type the following: localhost:4200 or 127.0.0.1:4200
If the app is deployed on a different machine: open your web browser and type the following: <machine_ip>:4200

1- Preparing Dataset

Prepare your dataset for training

2- Specifying General Settings

Specify the general parameters for you docker container

3- Specifying Hyperparameters

Specify the hyperparameters for the training job

4- Specifying Hyperparameters advanced

Specify the advanced hyperparameters for the training job

5- Checking training logs

Check your training logs to get better insights on the progress of the training

6- Monitoring the training

Monitor the training using Tensorboard

7- Checking the status of the job

Check the status to know when the job is completed successfully

8- Downloading and test with Swagger

Download your mode and easily test it with the built-in inference API using Swagger

9- Stopping and Delete the model's container

Delete the container's job to stop an ongoing job or to remove the container of a finished job. (Finished jobs are always available to download)

10- Visualizing graphs and metrics of Deleted Jobs

Visualize graphs and metrics of Deleted Jobs with Tensorboard

Training and Tensorboard Tips

Check our tips document to have (1) (a better insight on training models based on our expertise) and (2) (a benchmark of the inference speed).

Our tensorboard document helps you find your way more easily while navigating tensorboard

Guidelines

In advanced configuration mode, be careful while making the changes because it can cause errors while training. If that happens, stop the job and try again.
- the paths in fine_tune_checkpoint should be entered manually when you choose train from checkpoint
- Scroll down to find fine_tune_checkpoint and replace with your network-name and checkpoint name:
  - fine_tune_checkpoint :
```
fine_tune_checkpoint: "/checkpoints/<network-name>/<name-of-the-checkpoint>/ckpt-0"
```
In general settings, choose carefully the container name because choosing a name used by another container will cause errors.
When you try to monitor the job using tensorboard the page may not open, wait for some seconds and refresh the page.
When you leave tensorboard open for a long time, it might freeze. When encountered with such issue simply closing tensorboard tab in the browser and reopening it will solve the problem.

Change Docker-sdk default port

To change the docker-sdk default port 2222 to any other port of your choice:

change the uvicorn port inside docker_sdk_api/docker/Dockerfile to the port of your choice
rebuild the docker-sdk image using the following commend in the root of the repo:
- docker-compose -f build.yml build docker_sdk
change the baseEndPoint : <port-of-your-choice inside gui/src/environments/environment.ts and gui/src/environments/environment.prod.ts
rebuild the GUI image using the following comment in the root of the repo:
- docker-compose -f build.yml build user_interface
after this you can run the solution as following: docker-compose -f run.yml up

Know Issues

You might face some errors in some cases during the training. Most common ones are:

The running container has no RepoTag please kill to proceed: Container ID: <id-container> This issue is caused by some container not having a name, in that case you should rename that container or kill (make sure it is safe to remove this container) it via docker kill <id-container>.
Job Not Started: 404 Client Error Not Found("pull access denied for <image-name>, repository does not exists or may require 'docker login' ...) this issue is cause when you are trying to run a training docker image that you don't have. The main reason of this is not properly building the training_api or not setting up project requirements please refer to Setting Up Project Requirements section in the documentation.
Dataset Not Valid this error means that you dataset structure is not valid or the images/labels formate are not supported.
Training job not started after general settings step: One of the main reason is that the paths are not adjusted in docker_sdk_api/assets/paths.json field base_dir. You can solve this issue by running ./setup_solution_parameters.sh and choosing the training version you want to use GPU/CPU.

Citing

If you use this repository in your research, consider citing it using the following Bibtex entry:

@misc{bmwtrainingtool,
  author = {BMW TechOffice MUNICH},
  title = {TensorFlow Training GUI},
  howpublished = {\url{https://github.com/BMW-InnovationLab/BMW-TensorFlow-Training-GUI}},
  year = {2022},
}

Acknowledgments

Hadi Koubeissy, inmind.ai, Beirut, Lebanon
Ismail Shehab, inmind.ai, Beirut, Lebanon
Joe Sleiman, inmind.ai, Beirut, Lebanon
Jimmy Tekli, BMW Innovation Lab, Munich, Germany
Chafic Abou Akar, BMW TechOffice, Munich, Germany

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README.md

README.md

Tensorflow 2 Object Detection Training GUI for Linux

Prerequisites

Setting Up Project Requirements Automated

Setting Up Project Requirements Manually

How to check for prerequisites

Installing Prerequisites

Dataset Folder Structure

Objectclasses.json file example

Midweight and Heavyweight Solution

Build the Solution

Run the Solution

Usage

1- Preparing Dataset

2- Specifying General Settings

3- Specifying Hyperparameters

4- Specifying Hyperparameters advanced

5- Checking training logs

6- Monitoring the training

7- Checking the status of the job

8- Downloading and test with Swagger

9- Stopping and Delete the model's container

10- Visualizing graphs and metrics of Deleted Jobs

Training and Tensorboard Tips

Guidelines

Change Docker-sdk default port

Know Issues

Citing

Acknowledgments

Files

README.md

Latest commit

History

README.md

File metadata and controls

Tensorflow 2 Object Detection Training GUI for Linux

Prerequisites

Setting Up Project Requirements Automated

Setting Up Project Requirements Manually

How to check for prerequisites

Installing Prerequisites

Dataset Folder Structure

Objectclasses.json file example

Midweight and Heavyweight Solution

Build the Solution

Run the Solution

Usage

1- Preparing Dataset

2- Specifying General Settings

3- Specifying Hyperparameters

4- Specifying Hyperparameters advanced

5- Checking training logs

6- Monitoring the training

7- Checking the status of the job

8- Downloading and test with Swagger

9- Stopping and Delete the model's container

10- Visualizing graphs and metrics of Deleted Jobs

Training and Tensorboard Tips

Guidelines

Change Docker-sdk default port

Know Issues

Citing

Acknowledgments