Knowledge Distillation-Based Lightweight Mask RCNN

This project was inspired by Mask RCNN. This is a lightweight version of Mask R-CNN on Python 3, Keras, and TensorFlow. It utilizes a lightweight Feature Pyramid Network (LFPN) with a ResNet18 backbone.

Abstract

Depthwise Separable Convolutions was utilized to reduce the size of FPN. Additionally, Knowledge Distillation (KD) was applied to enhance the performance of the lightweight model. As a result, the proposed model size was reduced to 46% of the standard one, making it suitable for resource-constrained environments.

This project includes:

• Source code of lightweight Mask RCNN with lightweight FPN and ResNet18
• Source code of KD of the backbone network
• Training code for custom dataset or MS COCO
• Evaluation of models (mAP and mIoU)

Instuction

• Remember to follow the training steps below:
  
• train.ipynb is the main function of training and fine-tuning.
• outputs.ipynb illustrates the KD pipeline and the model separation.
• inspect_model_new.ipynb provides the model evaluation, including the critera of mAP and mIoU.
• model_new.py: the standard model implementation.
• model_final.py: the lightweight version implementation.