This repository compares the performance of three convolutional neural networks on segmentation network.
- DeepLabV3Plus
- UNET
- SegNet
What You Need to Know
If you want to check the superimposed results along with all the graphs in one place, you need to check the notebook file DL3vsSEGvsUNET.ipynb - Colaboratory.pdf available in this repository.
How to setup the environment
Tested on Python v3.7.6, Run this on command to create a python virtual environment with the name CVSeg
IMPORTANT: On Windows use "py" instead of "python3"
python3 -m venv CVSeg
Shift your current environment to CVSeg by running this command
On macOS and Linux:
source CVSeg/bin/activate
On Windows:
.\CVSeg\Scripts\activate
if ".../Activate.ps1 cannot be loaded because running scripts is disabled on this system" error occurs then run
Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy Unrestricted
.\CVSeg\Scripts\activate
Install the required python libraries
pip3 install -r requirements.txt
About the Dataset
Images that are provided in dataset have the dimensions 1000x1000 which are too big. We need to divide the images in multiple patches of size 128x128 for which we first need to resize the image to 1024x1024 and then we need to extract 64 128x128 patches from 1024x1024 image and then we need to save this images data to cache folder so that these cache images can be used later. Images are also rotated to 90deg,180deg and 270deg to introduce varaince in data. To do all this work, run the following command.
python3 ./munge/preprocess.py
This dataset is used for Semantic segmentation of nuclei in digital histology images. Monuseg dataset available at https://monuseg.grand-challenge.org/Data/
How to train
Loss Fn: Hybrid loss function is used in which 30% weightage is given to dice loss and 70% to binary cross entropy
In order to train UNET, you need to run the following command.
python3 trainUNet.py
In order to train SegNet, you need to run the following command.
python3 trainSegNet.py
In order to train DeepLab V3+ , you need to run the following command.
python3 trainDeepLabV3p.py
Following commands will save the trained models to trainedModels folder with the file extensions .h5 and the training graphs will be stored in graphs folder that will show you how model acquired accuracy after each epoch.
How to Evaluate the saved models
In order to Evaluate UNET, you need to run the following command.
python3 evaluateUNet.py
In order to Evaluate SegNet, you need to run the following command.
python3 evaluateSegNet.py
In order to Evaluate DeepLab V3+ , you need to run the following command.
python3 evaluateDeepLabV3p.py
Following commands will evaluate the trained models on the basis of following metrics.
- Loss
- Accuracy
- F1 Accuracy
- IOU (Intersection over Union Coefficient)
- DICE Coefficient
Segmentation results of original UNET and SegNet model.
