tahaz122/weak-segmentation

U-Net Image Segmentation

A U-Net based image segmentation model built with PyTorch for pixel-level image segmentation.

Status: Experimental Model quality: Baseline / weak model

Overview

This project implements a U-Net architecture for image segmentation.

The model takes an input image and predicts a segmentation mask, assigning each pixel to the target segmentation class.

The project was created as an experimental computer vision project to understand the practical workflow of training and evaluating a segmentation model.

Architecture

The model follows the standard U-Net encoder-decoder architecture.

Input Image
     │
     ▼
Encoder
     │
     ├── Feature Extraction
     ├── Downsampling
     └── Increasing Feature Depth
     │
     ▼
Bottleneck
     │
     ▼
Decoder
     │
     ├── Upsampling
     ├── Skip Connections
     └── Feature Reconstruction
     │
     ▼
Segmentation Head
     │
     ▼
Segmentation Mask

U-Net uses skip connections between the encoder and decoder to preserve spatial information that can be lost during downsampling.

Model

Architecture: U-Net

Task: Image segmentation

Framework: PyTorch

Input: RGB image

Output: Pixel-level segmentation mask

The released model artifact contains the trained model weights.

Dataset

The model was trained using an image segmentation dataset containing input images and corresponding segmentation masks.

The dataset details can be expanded as the project develops.

Relevant information includes:

• Dataset name
• Number of images
• Image resolution
• Number of segmentation classes
• Training/validation split
• Mask format

Training

The training pipeline consists of:

1. Loading input images and segmentation masks.
2. Preprocessing and resizing the images.
3. Feeding images through the U-Net encoder.
4. Processing features through the bottleneck.
5. Reconstructing spatial features through the decoder.
6. Producing the final segmentation mask.
7. Comparing predictions with the ground-truth masks.
8. Updating the model using backpropagation.

The current implementation is intended as a baseline rather than a highly optimized segmentation system.

Results

The current model produces segmentation predictions but should be considered a weak baseline.

It has not been extensively optimized or compared against more advanced segmentation architectures.

When available, the following metrics can be reported:

Metric	Score
IoU	—
Dice Score	—
Pixel Accuracy	—

These values should be updated with the actual evaluation results from the released model.

Usage

A typical inference workflow is:

import torch

model = torch.load("model.pt")

model.eval()

with torch.no_grad():
    prediction = model(image)

The exact loading and preprocessing procedure depends on the model implementation and released artifact.

Limitations

The current model has several limitations:

• Segmentation quality is relatively weak.
• The model has not been extensively tuned.
• Performance may vary significantly depending on the input images.
• The model may struggle with small or ambiguous objects.
• Generalization to datasets different from the training data has not been extensively evaluated.
• More advanced architectures and training strategies may provide better results.

Future Work

Possible improvements include:

• Better data augmentation
• Hyperparameter tuning
• Improved loss functions
• Higher-resolution training
• Transfer learning
• More extensive validation
• Class balancing
• Comparison with modern segmentation architectures
• Improved post-processing

Project Structure

The project may contain:

.
├── model.py
├── assets/
├── README.md
├── requirements.txt
└── project.yml

The exact project structure may vary.

License

See the repository license for usage and distribution information.

Source

This model is automatically published from the original GitHub project.

The GitHub repository is the source of truth for the model implementation, documentation, and releases.

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Automatically published by propublisher.