This repository contains implementations of various semantic segmentation models applied to a car segmentation dataset. The dataset comprises images of cars with their segmentation masks, allowing for the evaluation of these models' ability to accurately segment cars in images.
- Dataset: Contains images of cars with their segmentation masks.
- Models: Implements SegNet, U-Net, and a pre-trained model using
segmentation_models.pytorch
. - Evaluation: Includes metrics for evaluating model performance, such as pixel accuracy and mean IoU.
- Python 3.x
- PyTorch
segmentation-models-pytorch
Clone the repository and open in google colab.
Download the dataset from the provided link and place it in the appropriate directories (images
and masks
)
- Description of SegNet architecture and its relevance to semantic segmentation.
- Description of U-Net architecture and its relevance to semantic segmentation.
- Description of the pre-trained model using
segmentation_models.pytorch
and its benefits.
Include usage instructions and code snippets for loading models, preparing data, and performing predictions.
Explain the evaluation metrics used and how to calculate them using the provided functions.