AnnoMachine
What is AnnoMachine?
AnnoMachine is an application that aims to provide a user-friendly way to visualize and interact with object detection results.
AnnoMachine fulfills that mission by:
- Running a deep learning model under the hood to get a rough estimation of the annotation
- Providing a user-friendly interface which user can easily visualize and interactively update the annotation
Development Tools:
Stack:
- Object detection model: SSD (implemented on Tensorflow 2)
- Backend: Flask
- Frontend: ReactJS (SVG for displaying image and drawing bounding boxes)
- Database: PostgreSQL
- Web server: Nginx
Unit test:
- Backend: Flask-Testing
- Frontend: Jest + Enzyme
Deployment:
- docker
- docker-compose
- docker-machine
Preparation
Clone the repo:
git clone https://github.chunml/annomachine
cd annomachine
Download the pretrained weights (trained on VOC07+12 for 120 epochs): weights
Save the weights to ./services/backend/ssd_tf2/models:
mkdir -p ./services/backend/ssd_tf2/models
mv ssd_epoch_120.h5 ./services/backend/ssd_tf2/models
Make sure the unit tests are passed:
./test.sh
Local deployment
What we need to do is to run the command below:
docker-compose up -d --build
Head to localhost and see the result!
AWS deployment
- Create your own Access Key ID and Secret Access Key from AWS
- Create a new file called ~/.aws/credentials (a file, not a folder)
- Paste the below content (replace with your keys):
[default]
aws_access_key_id=<your_access_key_id>
aws_secret_access_key=<your_secret_access_key>
- Create a new machine via
docker-machine
docker-machine create --driver amazonec2 --amazonec2-open-port 80 <your_machine_name>
This will take a while. After that, a new EC2 instance is created on AWS, using the credentials you created above.
- Check if the new machine is created properly:
docker-machine ls
- Activate the AWS machine:
eval $(docker-machine env <your_machine_name>)
It means that from now on, we are inside the environment of the AWS machine! What we're gonna see/do all happens within that environment.
- Check if we are actually in AWS machine's environment:
docker-machine ls
We should see an asterisk next to the machine's name. Let's take note the IP address.
- Make sure that there is nothing on there yet:
docker image ls
- Deploy onto the AWS machine
docker-compose -f docker-compose-prod.yml up -d --build
- Confirm that the deployment is successful:
Head to
<your_machine_IP>and enjoy the result!