NUS ISS CA Project Assignment 1

• Contributor: Chad Ng LinkedIn
• Adapted from github https://github.com/matterport/Mask_RCNN

MaskRCNN

This project is adapted and show case how to handle MaskRCNN for 2 classes

It demonstrate the use of the following main tools:

• library imgaug, opencv
• tensorflow 1.14, keras 2.2.4
• skimage
• matplotlib, numpy, scipy
• logging, shutil, warnings, re, json

Prepare the data images using the online tool:

• Tool Name: VGG Image Annotator

• URL: http://www.robots.ox.ac.uk/~vgg/software/via/via-1.0.6.html

Tool use to generate augmented images from a fixed set of base images

• Tool Name: library imgaug
• Jupyter Notebook: image_augmentation.ipynb

The model weights required are:

• File: mask_rcnn_coco.h5:
  Link: https://drive.google.com/file/d/1bjypFhACIiwSacWv_LN-DPEwrnEYMaH_/view?usp=sharing

• File: mask_rcnn_worktool.h5
  Link: https://drive.google.com/file/d/1_Xqaq5vrySeGGvUAJB17ulRaRkNJiZD2/view?usp=sharing

• File: mask_rcnn_worktool_v2.h5
  Link: https://drive.google.com/file/d/1me5Ig0UCFx-4t_ylPgsD0qppqwNhz4l7/view?usp=sharing
  Version 2 model is improved with some hyper-parameters adjustments and with using imgaug library during training

To check the dataset you have prepared, open the jupyter notebook:

• inspect_worktool_data.ipynb

To train the Mask-RCNN model for worktool dataset, open the jupyter notebook:

• train_worktool_model_colab_v2.ipynb
• the notebook train_worktool_model_colab is the training script with a diiferent set of configuration parameters. Use train_worktool_model_colab_v2.ipynb for better results.

Train the Mask-RCNN model with Google's Colab tool:

Steps:

• Upload this github folder structure (contents of MaskRCNN) into your google drive, make sure the root folder is VSE/CA1
• Download the coco model weights file mask_rcnn_coco.h5 and upload it to VSE/CA1
• Open the jupyter notebook file "train_worktool_model_colab.ipynb" in Google Colab
• Run the scripts to create the model

To test your trained model open the jupyter notebook:

• inspect_worktool_model.ipynb