/ Machine Learning

Object Detection: YOLO, MobileNetv3 and EfficientDet

Object Detection: YOLO, MobileNetv3 and EfficientDet

Object-Detection_MobileNetv3-EfficientDet-YOLO

Object detection using OpenCv and Tensroflow with a serverless API on Google Cloud Run.

  • Live version: https://vision.imadelhanafi.com/predict/v1?model=MODEL_NAME&image_url=URL where MODEL_NAME is yolo or mobilenet.

    example:

    # NB : you can use services like https://imgbbb.com/ to get a direct link
    #Example1 of a detection request - MobileNet
    https://vision.imadelhanafi.com/predict/v1?model=mobilenet&image_url=https://imadelhanafi.com/data/draft/random/img4.jpg
    #Returns:
    [{"bbox":[114,17,186,222],"confidence":0.853282630443573,"label":"bear"}]
    
    #Example2 of a detection request - YOLO
    https://vision.imadelhanafi.com/predict/v1?model=yolo&image_url=https://imadelhanafi.com/data/draft/random/img2.jpg
    #Returns:
    [{"bbox":[137.0,187.0,96,144],"confidence":0.9843610525131226,"label":"cat"}]
    

Run Notebooks

Clone the repo and run

docker run --rm -it -p 8888:8888 -v $(pwd):/app  imadelh/opencv_tf:base jupyter lab --ip 0.0.0.0 --no-browser --allow-root

Jupyter Lab will be accessible at http://127.0.0.1:8888 and you can run notebooks, available in the artifacts folder, for inference for each model.

Run API

To run the API for object detection, you have to use the docker image that contains the pre-trained weights (imadelh/opencv_tf:full).

docker run --rm -it -p 8080:8080 imadelh/opencv_tf:full

The API can be used as follows

http://0.0.0.0:8080/predict/v1?model=NAME-OF-MODEL&image_url=IMAGE-URL

Where NAME-OF-MODEL is: yolo, mobilenet or efficientdet and IMAGE-URL is a direct URL to an image

Example:

http://0.0.0.0:8080/predict/v1?model=yolo&image_url=https://imadelhanafi.com/data/draft/random/img2.jpg

Returns:
[{"bbox":[137.0,187.0,96,144],"confidence":0.9843610525131226,"label":"cat"}]

This docker image (imadelh/opencv_tf:full) can be deployed to a cloud instance or serverless container services like Google Cloud Run.
Steps are explained in details here: https://github.com/imadelh/NLP-news-classification#serverless-deployement---google-run

Serverless version may suffer from cold-start if the service does not receive requests for a long time.


Imad El

GitHub

Comments