This project is based on Mask R-CNN implementation.
The model generates bounding boxes and segmentation masks for each instance of a kayaker in the image.
Model is trained on custom dataset and it's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
The custom dataset consists of 65 kayaker images with pixel-by-pixel polygon annotations.

Input	Output

• Python 3.7

1. Clone this repository

 git clone https://github.com/ndakic/kayaker-detection

2. Download yolov3 weights (237 MB) from here and add it to your model folder.
3. Install the requirements using pip and venv

pip install -r requirements.txt

4. Clone and install the Mask R-CNN Library

git clone https://github.com/matterport/Mask_RCNN

cd Mask_RCNN
python setup.py install

5. Run the following command in the command line

 python application.py