---
comments: true
description: Explore the Roboflow Carparts Segmentation Dataset for automotive AI applications. Enhance your segmentation models with rich, annotated data.
keywords: Carparts Segmentation Dataset, Roboflow, computer vision, automotive AI, vehicle maintenance, Ultralytics
---

# Roboflow Universe Carparts Segmentation Dataset

The [Roboflow](https://roboflow.com/?ref=ultralytics) [Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm) is a curated collection of images and videos designed for computer vision applications, specifically focusing on segmentation tasks related to car parts. This dataset provides a diverse set of visuals captured from multiple perspectives, offering valuable annotated examples for training and testing segmentation models.

Whether you're working on automotive research, developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing accuracy and efficiency in your projects.

<p align="center">
  <br>
  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/eHuzCNZeu0g"
    title="YouTube video player" frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
    allowfullscreen>
  </iframe>
  <br>
  <strong>Watch:</strong> Carparts Instance Segmentation Using Ultralytics HUB
</p>

## Dataset Structure

The data distribution within the Carparts Segmentation Dataset is organized as outlined below:

- **Training set**: Includes 3156 images, each accompanied by its corresponding annotations.
- **Testing set**: Comprises 276 images, with each one paired with its respective annotations.
- **Validation set**: Consists of 401 images, each having corresponding annotations.

## Applications

Carparts Segmentation finds applications in automotive quality control, auto repair, e-commerce cataloging, traffic monitoring, autonomous vehicles, insurance processing, recycling, and smart city initiatives. It streamlines processes by accurately identifying and categorizing different vehicle components, contributing to efficiency and automation in various industries.

## Dataset YAML

A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the Package Segmentation dataset, the `carparts-seg.yaml` file is maintained at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/carparts-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/carparts-seg.yaml).

!!! Example "ultralytics/cfg/datasets/carparts-seg.yaml"

    ```yaml
    --8<-- "ultralytics/cfg/datasets/carparts-seg.yaml"
    ```

## Usage

To train Ultralytics YOLOv8n model on the Carparts Segmentation dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.

!!! Example "Train Example"

    === "Python"

        ```python
        from ultralytics import YOLO

        # Load a model
        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)

        # Train the model
        results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
        ```

    === "CLI"

        ```bash
        # Start training from a pretrained *.pt model
        yolo segment train data=carparts-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
        ```

## Sample Data and Annotations

The Carparts Segmentation dataset includes a diverse array of images and videos taken from various perspectives. Below, you'll find examples of data from the dataset along with their corresponding annotations:

![Dataset sample image](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/55da8284-a637-4858-aa1c-fc22d33a9c43)

- This image illustrates object segmentation within a sample, featuring annotated bounding boxes with masks surrounding identified objects. The dataset consists of a varied set of images captured in various locations, environments, and densities, serving as a comprehensive resource for crafting models specific to this task.
- This instance highlights the diversity and complexity inherent in the dataset, emphasizing the crucial role of high-quality data in computer vision tasks, particularly in the realm of car parts segmentation.

## Citations and Acknowledgments

If you integrate the Carparts Segmentation dataset into your research or development projects, please make reference to the following paper:

!!! Quote ""

    === "BibTeX"

        ```bibtex
           @misc{ car-seg-un1pm_dataset,
                title = { car-seg Dataset },
                type = { Open Source Dataset },
                author = { Gianmarco Russo },
                howpublished = { \url{ https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm } },
                url = { https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm },
                journal = { Roboflow Universe },
                publisher = { Roboflow },
                year = { 2023 },
                month = { nov },
                note = { visited on 2024-01-24 },
            }
        ```

We extend our thanks to the Roboflow team for their dedication in developing and managing the Carparts Segmentation dataset, a valuable resource for vehicle maintenance and research projects. For additional details about the Carparts Segmentation dataset and its creators, please visit the [CarParts Segmentation Dataset Page](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm).

## FAQ

### What is the Roboflow Carparts Segmentation Dataset?

The [Roboflow Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm) is a curated collection of images and videos specifically designed for car part segmentation tasks in computer vision. This dataset includes a diverse range of visuals captured from multiple perspectives, making it an invaluable resource for training and testing segmentation models for automotive applications.

### How can I use the Carparts Segmentation Dataset with Ultralytics YOLOv8?

To train a YOLOv8 model on the Carparts Segmentation dataset, you can follow these steps:

!!! Example "Train Example"

    === "Python"
    
        ```python
        from ultralytics import YOLO

        # Load a model
        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)

        # Train the model
        results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
        ```

    === "CLI"

        ```bash
        # Start training from a pretrained *.pt model
        yolo segment train data=carparts-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
        ```

For more details, refer to the [Training](../../modes/train.md) documentation.

### What are some applications of Carparts Segmentation?

Carparts Segmentation can be widely applied in various fields such as:
- **Automotive quality control**
- **Auto repair and maintenance**
- **E-commerce cataloging**
- **Traffic monitoring**
- **Autonomous vehicles**
- **Insurance claim processing**
- **Recycling initiatives**
- **Smart city projects**

This segmentation helps in accurately identifying and categorizing different vehicle components, enhancing the efficiency and automation in these industries.

### Where can I find the dataset configuration file for Carparts Segmentation?

The dataset configuration file for the Carparts Segmentation dataset, `carparts-seg.yaml`, can be found at the following location: [carparts-seg.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/carparts-seg.yaml).

### Why should I use the Carparts Segmentation Dataset?

The Carparts Segmentation Dataset provides rich, annotated data essential for developing high-accuracy segmentation models in automotive computer vision. This dataset's diversity and detailed annotations improve model training, making it ideal for applications like vehicle maintenance automation, enhancing vehicle safety systems, and supporting autonomous driving technologies. Partnering with a robust dataset accelerates AI development and ensures better model performance. 

For more details, visit the [CarParts Segmentation Dataset Page](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm).
