👉 Mask RCNN Implementation on LabelMe Annotations Data 👈

Prepare Data

DataSet Folder Structure

Labelme Annotations tools

- Data_folder
    - train
        - img1.jpg
        - img1.json
        - img2.jpg
        - img2.json
        ...
    - val
        - img3.jpg
        - img2.json
        - img4.jpg
        - img4.json
        ...

Training

# Configuration
# Adjust according to your Dataset and GPU

IMAGES_PER_GPU = 2 # 1
 
# Number of classes (including background)
NUM_CLASSES = 1 + 1 # Background

# typically after labeled, class can be set from Dataset class
# if you want to test your model, better set it corectly based on your trainning dataset

# Number of training steps per epoch
STEPS_PER_EPOCH = 100

The easiest way to get started is to simply try out on Colab:

Training the model on Custom Data

python customTrain.py train --dataset=path_to_Data_folder --weights=coco

ReTraining from Last Checkpoint

python customTrain.py train --dataset=path_to_Data_folder --weights=last

Requirements

• Python3.6
• Tensorflow-gpu==1.15
• keras==2.0.8

For more details check Mask RCNN Repo