Person Segmentation

This demo shows how to train and run a Semantic Segmentation network for the "Person vs. Background" task. The model is trained on a subset of the COCO segmentation dataset (http://cocodataset.org/), containing "person" class. The network architecture is made from scratch and is inspired be U-Net and DeepLab v3 (ASPP) architectures

Jupyter notebooks

1. person-segmentation.ipynb
   • All in one notebook: load data, create model, run training, run inference
2. post-processing.ipynb
   • Playground for segmentation post processing (applications)
   • Emulating DOF effect and background switch
3. kaggle_submission.ipynb
   • Run all test images through a model
   • Generate a kaggle submission file (convers all segmentation maps to CSV using RLE)

Python scripts

The scripts contain approximatelly the same code, as in person-segmentation.ipynb

1. data.py
   • Dataset classes for preparation fo training and validation data pipelines
2. model.py
   • Definition of the Model class, defining the network architecture
3. train.py
   • Execute this script to run the training procedure
4. inference.py
   • Execute this script to run the inference on a trained model