Roboflow Universe Carparts Segmentation Dataset

The Roboflow Carparts Segmentation Dataset 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.

Watch: Carparts Instance Segmentation Using Ultralytics HUB

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.

# Ultralytics YOLO 🚀, AGPL-3.0 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 (132 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: ../datasets/carparts-seg # dataset root dir
train: train/images # train images (relative to 'path') 3516 images
val: valid/images # val images (relative to 'path') 276 images
test: test/images # 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://ultralytics.com/assets/carparts-seg.zip

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 page.

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)

# 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:

• 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:

   @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.

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