The dataset that used for our experiments is the M2NIST Dataset. The dataset was created in order to teach the basics of semantic segmentation with convolutional neural networks without requiring the use of complex architechtures that take long to train. More details can be found at the above link.
Matlab 2020a
Toolboxes utilized:
- Deep Learning Toolbox
- Computer Vision Toolbox
- Parallel Computing Toolbox
Python Packages:
- keras
- Numpy
- OpenCV
- pathlib
If you don't have the above python packages. They can be installed using pip.
$ pip install numpy
$ pip install opencv-python
$ pip install pathlib
$ pip install keras
The experiments located in this respository were conducted using a machine with the following specifications:
OS: Ubuntu 16.04.6 LTS (Xenial Xerus), 4.15.0-45-generic x86_64
GPU(s): 1 GeForce GTX 1080
To reproduce the results:
- Download the M2NIST Dataset .npy files from the following link and place them in the dataset folder.
- Run setup.sh.
chmod u+x setup.sh && ./setup.sh
The training scripts used to train the segmentation models are located in the matlab directory. Execute each of them to train segmentation models on the datasets generated above.