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

Current state of supervised and unsupervised depth completion methods

DeepLab-ResNet rebuilt in TensorFlow

Largest list of models for Core ML (for iOS 11+)

LeetCode Solutions https://t.me/tenvlad

Supplementary materials to "Spin-optomechanical quantum interface enabled by an ultrasmall mechanical and optical mode volume cavity" by H. Raniwala, S. Krastanov, M. Eichenfield, and D. R. Englund, 2022

Evaluation framework for testing segmentation networks in PyTorch

LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

DanceTrack: Multiple Object Tracking in Uniform Appearance and Diverse Motion

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

An Efficient Implementation of Analytic Mesh Algorithm for 3D Iso-surface Extraction from Neural Networks

Supervised Contrastive Learning for Product Matching

Medical Image Segmentation using Squeeze-and-Expansion Transformers

Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering

A copy of Ares that costs 30 fucking dollars.

Class-Attentive Diffusion Network for Semi-Supervised Classification [AAAI'21] (official implementation)

Code base for the paper "Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation"

EqGAN - Improving GAN Equilibrium by Raising Spatial Awareness

A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)

Algorithm to texture 3D reconstructions from multi-view stereo images

ByteTrack超详细教程！训练自己的数据集&&摄像头实时检测跟踪