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# Carparts Segmentation Dataset

The Carparts Segmentation Dataset, available on Roboflow Universe, is a curated collection of images and videos designed for computer vision applications, specifically focusing on segmentation tasks. Hosted on Roboflow Universe, 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 the accuracy and efficiency of your projects using models like Ultralytics YOLO.

Watch: Carparts Instance Segmentation with Ultralytics YOLO11.

## Dataset Structure

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

- Training set: Includes 3156 images, each accompanied by its corresponding annotations. This set is used for training the deep learning model.
- Testing set: Comprises 276 images, with each one paired with its respective annotations. This set is used to evaluate the model's performance after training using test data.
- Validation set: Consists of 401 images, each having corresponding annotations. This set is used during training to tune hyperparameters and prevent overfitting using validation data.

## Applications

Carparts Segmentation finds applications in various domains including:

- Automotive Quality Control: Identifying defects or inconsistencies in car parts during manufacturing (AI in Manufacturing).
- Auto Repair: Assisting mechanics in identifying parts for repair or replacement.
- E-commerce Cataloging: Automatically tagging and categorizing car parts in online stores for e-commerce platforms.
- Traffic Monitoring: Analyzing vehicle components in traffic surveillance footage.
- Autonomous Vehicles: Enhancing the perception systems of self-driving cars to better understand surrounding vehicles.
- Insurance Processing: Automating damage assessment by identifying affected car parts during insurance claims.
- Recycling: Sorting vehicle components for efficient recycling processes.
- Smart City Initiatives: Contributing data for urban planning and traffic management systems within Smart Cities.

By accurately identifying and categorizing different vehicle components, carparts segmentation streamlines processes and contributes to increased efficiency and automation across these industries.

## Dataset YAML

A YAML (Yet Another Markup Language) file defines the dataset configuration, including paths, class names, and other essential details. For the Carparts Segmentation dataset, the `carparts-seg.yaml` file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/carparts-seg.yaml. You can learn more about the YAML format at yaml.org.

ultralytics/cfg/datasets/carparts-seg.yaml

    # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

    # Carparts-seg dataset by Ultralytics
    # Documentation: https://docs.ultralytics.com/datasets/segment/carparts-seg/
    # Example usage: yolo train data=carparts-seg.yaml
    # parent
    # ├── ultralytics
    # └── datasets
    #     └── carparts-seg ← downloads here (133 MB)

    # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
    path: carparts-seg # dataset root dir
    train: images/train # train images (relative to 'path') 3516 images
    val: images/val # val images (relative to 'path') 276 images
    test: images/test # test images (relative to 'path') 401 images

    # Classes
    names:
      0: back_bumper
      1: back_door
      2: back_glass
      3: back_left_door
      4: back_left_light
      5: back_light
      6: back_right_door
      7: back_right_light
      8: front_bumper
      9: front_door
      10: front_glass
      11: front_left_door
      12: front_left_light
      13: front_light
      14: front_right_door
      15: front_right_light
      16: hood
      17: left_mirror
      18: object
      19: right_mirror
      20: tailgate
      21: trunk
      22: wheel

    # Download script/URL (optional)
    download: https://github.com/ultralytics/assets/releases/download/v0.0.0/carparts-seg.zip

## Usage

To train an Ultralytics YOLO11 model on the Carparts Segmentation dataset for 100 epochs with an image size of 640, use the following code snippets. Refer to the model Training guide for a comprehensive list of available arguments and explore model training tips for best practices.

Train Example

Python CLI

    from ultralytics import YOLO

    # Load a pretrained segmentation model like YOLO11n-seg
    model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)

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

    # After training, you can validate the model's performance on the validation set
    results = model.val()

    # Or perform prediction on new images or videos
    results = model.predict("path/to/your/image.jpg")

    # Start training from a pretrained *.pt model using the Command Line Interface
    # Specify the dataset config file, model, number of epochs, and image size
    yolo segment train data=carparts-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

    # Validate the trained model using the validation set
    # yolo segment val model=path/to/best.pt

    # Predict using the trained model on a specific image source
    # yolo segment predict model=path/to/best.pt source=path/to/your/image.jpg

## Sample Data and Annotations

The Carparts Segmentation dataset includes a diverse array of images and videos captured from various perspectives. Below are examples showcasing the data and its corresponding annotations:

- The image demonstrates object segmentation within a car image sample. Annotated bounding boxes with masks highlight the identified car parts (e.g., headlights, grille).
- The dataset features a variety of images captured under different conditions (locations, lighting, object densities), providing a comprehensive resource for training robust car part segmentation models.
- This example underscores the dataset's complexity and the importance of high-quality data for computer vision tasks, especially in specialized domains like automotive component analysis. Techniques like data augmentation can further enhance model generalization.

## Citations and Acknowledgments

If you utilize the Carparts Segmentation dataset in your research or development efforts, please cite the original source:

BibTeX

       @misc{ car-seg-un1pm_dataset,
            title = { car-seg Dataset },
            type = { Open Source Dataset },
            author = { Gianmarco Russo },
            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 acknowledge the contribution of Gianmarco Russo and the Roboflow team in creating and maintaining this valuable dataset for the computer vision community. For more datasets, visit the Ultralytics Datasets collection.

## FAQ

### What is the Carparts Segmentation Dataset?

The Carparts Segmentation Dataset is a specialized collection of images and videos for training computer vision models to perform segmentation on car parts. It includes diverse visuals with detailed annotations, suitable for automotive AI applications.

### How can I use the Carparts Segmentation Dataset with Ultralytics YOLO11?

You can train an Ultralytics YOLO11 segmentation model using this dataset. Load a pretrained model (e.g., `yolo11n-seg.pt`) and initiate training using the provided Python or CLI examples, referencing the `carparts-seg.yaml` configuration file. Check the Training Guide for detailed instructions.

Train Example Snippet

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)

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

    yolo segment train data=carparts-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

### What are some applications of Carparts Segmentation?

Carparts Segmentation is useful in:

- Automotive Quality Control: Ensuring parts meet standards (AI in Manufacturing).
- Auto Repair: Identifying parts needing service.
- E-commerce: Cataloging parts online.
- Autonomous Vehicles: Improving vehicle perception (AI in Automotive).
- Insurance: Assessing vehicle damage automatically.
- Recycling: Sorting parts efficiently.

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

The dataset configuration file, `carparts-seg.yaml`, which contains details about the dataset paths and classes, is located in the Ultralytics GitHub repository: carparts-seg.yaml.

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

This dataset offers rich, annotated data crucial for developing accurate segmentation models for automotive applications. Its diversity helps improve model robustness and performance in real-world scenarios like automated vehicle inspection, enhancing safety systems, and supporting autonomous driving technology. Using high-quality, domain-specific datasets like this accelerates AI development.

📅 Created 1 year ago ✏️ Updated 5 months ago

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