Car Segmentation Project

Semantic segmentation pipeline focused on identifying and segmenting cars in images. This repository contains Jupyter notebooks for data preprocessing/augmentation, training from scratch, and leveraging a pretrained model. The dataset is confidential and therefore not included; the notebooks are provided to demonstrate the approach and code.

Why This Is Useful

• End-to-end flow: Covers preprocessing, augmentation, training, and evaluation.
• Two training paths: From-scratch model and pretrained baseline for faster iteration.
• Reproducible notebooks: Clear, modular steps that can be adapted to new datasets.

Getting Started

Prerequisites

• Python 3.9+ recommended
• GPU with CUDA (optional, but recommended for training)
• Common ML libraries (e.g., PyTorch or TensorFlow, OpenCV, NumPy, Matplotlib)

Note: Exact package versions are not pinned here because the project is organized as exploratory notebooks. Install the common packages used in segmentation workflows and adjust imports if needed.

Setup

1. Clone the repository:

git clone https://github.com/HarjodhsGitHub/Car-Segmentation-Project.git
cd Car-Segmentation-Project

2. (Optional) Create and activate a virtual environment:

python -m venv .venv
.\.venv\Scripts\activate

3. Install typical dependencies for computer vision segmentation workflows (adjust to your framework of choice):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install numpy matplotlib opencv-python scikit-image scikit-learn albumentations tqdm

If using TensorFlow/Keras instead of PyTorch:

pip install tensorflow keras numpy matplotlib opencv-python scikit-image scikit-learn albumentations tqdm

Data Placement

Since the dataset is confidential, place your own dataset locally and update paths in the notebooks. A typical structure:

data/
	images/
	masks/

Update any path variables within the notebooks to point to your dataset.

Running the Notebooks

• Preprocessing & Augmentation: see preprocess_and_augment.ipynb
• Train From Scratch: see model_scratch.ipynb
• Pretrained Model Approach: see pretrained_model.ipynb

Open each notebook and execute cells in order, adjusting configuration blocks and file paths where indicated.

Usage Examples

Inside the notebooks, you will typically:

• Define dataset paths and loaders
• Apply augmentations (e.g., flips, rotations, color jitter)
• Configure model architecture and training hyperparameters
• Train and evaluate segmentation performance (e.g., IoU, Dice)

Example snippet (PyTorch-style, illustrative):

import torch
from torch.utils.data import DataLoader

# Define dataset & loader
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)

# Define model
model = YourUNetVariant().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
criterion = DiceLoss()

# Training loop (simplified)
for epoch in range(num_epochs):
		model.train()
		for images, masks in train_loader:
				images, masks = images.to(device), masks.to(device)
				preds = model(images)
				loss = criterion(preds, masks)
				optimizer.zero_grad()
				loss.backward()
				optimizer.step()

Getting Help

• Open an issue for questions or support: Issues
• Refer to notebook markdown cells for guidance within each workflow.

Contributing & Maintenance

• Maintainer: @HarjodhsGitHub
• Contributions are welcome via pull requests.
• Please follow standard open-source practices: descriptive PRs, small focused changes, and clear commit messages.

For contribution guidelines and development notes, add or refer to:

• docs/CONTRIBUTING.md (recommended)
• docs/DEVELOPMENT.md (recommended)

Project Structure

• preprocess_and_augment.ipynb: Data cleaning, visualization, and augmentation steps.
• model_scratch.ipynb: Train a segmentation model from scratch.
• pretrained_model.ipynb: Use a pretrained backbone/model for transfer learning.
• .github/prompts/create-readme.prompt.md: Prompt used to generate this README.

License

This project likely has licensing constraints due to the dataset. If a license file is added, it will be referenced here. Please check for a LICENSE file in the repository root.

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