Cards Detection Using FASTER RCNN

End to end object detection project using Faster RCNN.
The training is done using TFOD1.14(Tensorflow object detection) framework.
The application is served as an REST API using Flask.
Faster RCNN paper can be found here.

Steps to run the application

Setting up virtual environment.

What is Virtual Environment in python ?
Create virtual environment in python
Create virtual environment Anaconda
create a virtual environment and install requirements.txt

pip install -r requirements.txt

After setting up the virtual environment download the trained weights from here.
After downloading the trained weights place it under the directory services/card_detector/application/ai/weights/exported_inference_graph/
After performing the above steps go to services/card_detector/api and run app.py

python app.py

After running the app.py the web app can be accessed at http://127.0.0.1:9000/ copy this url and paste it in your browser.
The UI will look like the following.

The picture can be uploaded using the upload button and after uploading the image click on predict to perform inference.
Sample Input

Sample Output

The application logs can also be found here.