/flask_detector

Primary LanguagePython

Project description

This is a capstone project for advanced track of the MIPT Deep Learning School (Fall-2020).
Flask-detector is a web application for object detection consisting of following components

  • Flask
  • Redis server
  • RQ (Redis Queue)
  • Pretrained MMdetection model Mask-RCNN-R50

'Front_page'

A user uploads a picture and gets back a detected image. The result can be downloaded.

Deploymnet

  • Clone the repository with git clone https://github.com/biomlds/flask_detector.git and cd flask_detector/

  • Docker on a local computer

    1. Install Docker
    2. docker build --rm -t flask-detector .
    3. docker run -p 5000:5000 -it flask-detector
    4. Open http://0.0.0.0:5000

  • Heroku

    1. heroku create -a flask-detector
    2. heroku container:push -a flask-detector web
    3. Set a secret key: heroku config:set -a flask-detector SECRET_KEY=Your_Secret_Key
    4. heroku container:release -a flask-detector web
    5. heroku open or open https://flask-detector.herokuapp.com

  • Live server

NOTE:

  • As a prove of concept, the app was deployed on a free heroku dyno: https://flask-detector.herokuapp.com.
  • Due to 500 Mb RAM limit of the free heroku instance it tends to crash. For stable work either use the docker container or deploy on a server with 1Gb+ of RAM.
  • flask-detector was tested in a local docker container.

MMDetection

MMDetection is an open-source object detection toolbox based on PyTorch. It comes with a zoo of model pretrained on the COCO dataset. This dataset includes 80 categories captured from everyday scenes. The full list of labels is available here.

Object detection results

This App make use of pretrained Mask R-CNN R-50 FPN model pretrained on the COCO dataset. As a result, there is no big surprise it performs well on pictures taken in a daily setting, while poorly in less common scenes (e.g., zoo or diving).

Test

Following pictures contained objects from the COCO dataset and were successfully identified:

  • apartment apartment
  • park park
  • oval office oval_office
  • street street

When it comes to unusual surroudings, the model performance suffers. Let's consider following examples:

  • diving1 diving1
  • diving2 diving2

While the model is able to recognize a diver, it fails to detect a fish (e.g. diving1) or even misidentify it as a bird (diving2)

Sometimes the network produces conflicting result for the same class of objects it never saw. For example, lemurs are classified as a cat, dog and bird as those examples were in the training dataset:

  • zoo zoo

Referred images are in the test_images subfolder.

All in all, it is clear that fine tuning is required to adapt the model to a specific domain. For example, to improve performance of the neural net on the above mentioned pictures, it shold be trained on a dataset of animals.