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---
pipeline_tag: image-segmentation
---
tags:
- image-segmentation
---
# Custom U-Net Model for Semantic Segmentation
## Overview
This repository contains a custom U-Net model for semantic segmentation tasks using TensorFlow/Keras. The model is designed to perform pixel-wise classification on images, such as distinguishing between different classes in medical images or segmenting objects in natural images.
## Model Architecture
The model architecture is a standard U-Net with custom convolutional blocks in the encoder and decoder. The encoder consists of four downsampling blocks, and the decoder consists of four upsampling blocks. The bottleneck layer in the middle has a larger number of filters.
### Encoder
- **Blocks**: 4
- **Filters**: [64, 128, 256, 512]
### Bottleneck
- **Filters**: 1024
### Decoder
- **Blocks**: 4
- **Filters**: [512, 256, 128, 64]
## Training Details
- **Loss Function**: Categorical Crossentropy
- **Optimizer**: Adam
- **Learning Rate**: 0.001
- **Batch Size**: 16
- **Epochs**: 50
- **Validation Split**: 20%
## Usage
### Installation
Ensure you have the necessary dependencies installed. You can install them using pip:
```bash
pip install tensorflow |