👉 Mask RCNN Implementation on LabelMe Annotations Data 👈

## Prepare Data ### DataSet Folder Structure [Labelme](https://github.com/wkentaro/labelme/) Annotations tools ``` - Data_folder - train - img1.jpg - img1.json - img2.jpg - img2.json ... - val - img3.jpg - img2.json - img4.jpg - img4.json ... ``` ## Training ```python # 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: [](https://colab.research.google.com/drive/142qQPGuzz7AemMVDl8iKw0fEe-hnIL1p?usp=sharing) ### Training the model on Custom Data ```bash python customTrain.py train --dataset=path_to_Data_folder --weights=coco ``` ### ReTraining from Last Checkpoint ```bash 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](https://github.com/matterport/Mask_RCNN)