https://github.com/razamehar/semantic-image-segmentation-u-net-vs-segnet
This project implements semantic image segmentation using two popular convolutional neural network architectures: U-Net and SegNet. Semantic image segmentation involves partitioning an image into multiple segments, each representing a different class.
https://github.com/razamehar/semantic-image-segmentation-u-net-vs-segnet
image-segmentation python3 segnet semantic-image-segmentation tensowflow u-net
Last synced: 2 months ago
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This project implements semantic image segmentation using two popular convolutional neural network architectures: U-Net and SegNet. Semantic image segmentation involves partitioning an image into multiple segments, each representing a different class.
- Host: GitHub
- URL: https://github.com/razamehar/semantic-image-segmentation-u-net-vs-segnet
- Owner: razamehar
- License: mit
- Created: 2024-05-15T12:46:37.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-06T23:34:46.000Z (about 1 year ago)
- Last Synced: 2024-08-07T02:43:26.381Z (about 1 year ago)
- Topics: image-segmentation, python3, segnet, semantic-image-segmentation, tensowflow, u-net
- Language: Jupyter Notebook
- Homepage:
- Size: 7.29 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Semantic Image Segmentation U Net vs. SegNet
## Project Overview
This project focuses on semantic image segmentation, specifically targeting images of cats and dogs. Semantic image segmentation involves classifying each pixel in an image into a specific class, allowing for detailed understanding and analysis of image contents. In this project, I implement two popular architectures, U-Net and SegNet, for semantic segmentation and compared their performance.
## Project Inspiration
The inspiration for this project originated from François Chollet's image segmentation classifier as presented in the book "Deep Learning with Python". Expanding upon this concept, I aimed to employ architectures like to U-Net and SegNet for image segmentation, intending to compare their performance.
## Salient Features of U-Net Architecture:
- Symmetrical architecture with equal downsampling and upsampling layers.
- Skip connections preserve spatial information.
## Salient Features of SegNet Architecture:
- Encoder-decoder architecture.
- Uses max-pooling indices for upsampling, reducing complexity.
- Designed for efficiency, suitable for real-time and resource-constrained scenarios.
## Models' Performance
After 30 epochs, the U-Net-like architecture achieved a training loss of 0.2429 and a validation loss of 0.3614. On the other hand, the SegNet-like architecture had a training loss of 0.4011 and a validation loss of 0.4176.
### Performance Evaluation: U-Net vs. SegNet Across 30 Epochs
U-Net
SegNet
## Potential Improvements
- Increase the number of images and their corresponding annotations.
- Deepen the network by adding more layers and narrow it by reducing the number of neurons.
- Introduce batch normalization layers after convolutional layers to enhance performance.
## Data Sources:
- http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
- http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
## License:
This project is licensed under the Raza Mehar License. See the LICENSE.md file for details.
## Contact:
For any questions or clarifications, please contact Raza Mehar at [raza.mehar@gmail.com].