Implementation of U-Net and SegNet for building segmentation

Last update: Dec 07, 2022

Overview

Specialized project

Created by Katrine Nguyen and Martin Wangen-Eriksen as a part of our specialized project at Norwegian University of Science and Technology (NTNU).

Models

Most of our code and the U-net model is significantly inspired by this project Unet-for-Person-Segmentation. The SegNet model we created on our own based on other implementations of SegNet in Tensorflow.

Data

The model is trained and tested on Massachusetts Buildings Dataset from Kaggle. The original images where 1500X1500 pixels each over an area of 1500x1500 meters (1mx1m resolution). The original 137 images were cropped into 64x64 pixels and images without building were filtered out.

To make the masks compatible with our model the masks was changed from white (255,255,255) labels to greyscale with value 1. This is done in image_fix.py found in the repo.

Folder structure

Images and masks are saved in local directories and used in data.py and test.py. This is of course possible to change, however if you want to use the exact same code you can follow this folder structure.


.
├── ...
├── building-segmentation                # Directory for all images
│   ├── Images                           # Directory for raw images
│   │   ├── cropped_images_train_64      # Directory for cropped images where number specifies resolution, containg .jpg
│   │   ├── cropped_images_train_128     # Directory for cropped images where number specifies resolution, containg .jpg 
│   │   └── ...                          # More directories with other resolutions
│   ├── Masks                            # Directory for all maskes
│   │   ├── cropped_masks_train_64       # Directory for cropped masks where number specifies resolution, containg .jpg
│   │   ├── cropped_masks_train_128      # Directory for cropped masks where number specifies resolution, containg .jpg 
│   │   └── ...                          # More directories with other resolutions
│   └── Test                             # Miscellaneous information
│       ├── test_64                      # Directory for images where number specifies resolution, containing .jpg
│       └── ...                          # More directories with other resolutions
└── ...

# data.py
    images = glob(os.path.join(dataset_path, "images/cropped_images_train_64/*"))
    masks = glob(os.path.join(dataset_path, "masks/cropped_masks_train_64/*"))
    
    # In main:
        dataset_path = "building-segmentation"
    
# test.py
    test_images = glob("building-segmentation/test/test_64/*")

Implementation of U-Net and SegNet for building segmentation

Related tags

Overview

Specialized project

Models

Data

Folder structure

Running the project

Requirements

Training

Testing

Owner

Martin.w-e

A python script to convert images to animated sus among us crewmate twerk jifs as seen on r/196

paper list in the area of reinforcenment learning for recommendation systems

Regulatory Instruments for Fair Personalized Pricing.

Deeprl - Standard DQN and dueling network for simple games

Generalized Data Weighting via Class-level Gradient Manipulation

This script runs neural style transfer against the provided content image.

Deep Learning for humans

A toolkit for document-level event extraction, containing some SOTA model implementations

Code for "Unsupervised Layered Image Decomposition into Object Prototypes" paper

Soomvaar is the repo which 🏩 contains different collection of 👨‍💻🚀code in Python and 💫✨Machine 👬🏼 learning algorithms📗📕 that is made during 📃 my practice and learning of ML and Python✨💥

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

[CVPR 2021 Oral] ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis

Computer Vision application in the web

Code for: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.

Source code for EquiDock: Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (ICLR 2022)

Predicting the duration of arrival delays for commercial flights.

Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

PyTorch implementation code for the paper MixCo: Mix-up Contrastive Learning for Visual Representation