177 lines
11 KiB
Markdown
Executable File
177 lines
11 KiB
Markdown
Executable File
---
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comments: true
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description: Explore the Carparts Segmentation Dataset for automotive AI applications. Enhance your segmentation models with rich, annotated data using Ultralytics YOLO.
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keywords: Carparts Segmentation Dataset, computer vision, automotive AI, vehicle maintenance, Ultralytics, YOLO, segmentation models, deep learning, object segmentation
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---
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# Carparts Segmentation Dataset
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<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-carparts-segmentation-dataset.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open Carparts Segmentation Dataset In Colab"></a>
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The Carparts Segmentation Dataset is a curated collection of images and videos designed for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications, specifically focusing on [segmentation tasks](https://docs.ultralytics.com/tasks/segment/). This dataset provides a diverse set of visuals captured from multiple perspectives, offering valuable [annotated](https://www.ultralytics.com/glossary/data-labeling) examples for training and testing segmentation models.
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Whether you're working on [automotive research](https://www.ultralytics.com/solutions/ai-in-automotive), developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing the [accuracy](https://www.ultralytics.com/glossary/accuracy) and efficiency of your projects using models like [Ultralytics YOLO](../../models/yolo26.md).
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<p align="center">
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<br>
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/7lZa3Yi2kbo"
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title="YouTube video player" frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> Carparts <a href="https://www.ultralytics.com/glossary/instance-segmentation">Instance Segmentation</a> with Ultralytics YOLO26.
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</p>
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## Dataset Structure
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The data distribution within the Carparts Segmentation Dataset is organized as follows:
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- **Training set**: Includes 3156 images, each accompanied by its corresponding annotations. This set is used for [training](https://www.ultralytics.com/glossary/training-data) the [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) [model](https://www.ultralytics.com/glossary/foundation-model).
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- **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](https://www.ultralytics.com/glossary/test-data).
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- **Validation set**: Consists of 401 images, each having corresponding annotations. This set is used during training to tune [hyperparameters](https://docs.ultralytics.com/guides/hyperparameter-tuning/) and prevent [overfitting](https://www.ultralytics.com/glossary/overfitting) using [validation data](https://www.ultralytics.com/glossary/validation-data).
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## Applications
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Carparts Segmentation finds applications in various domains including:
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- **Automotive Quality Control**: Identifying defects or inconsistencies in car parts during manufacturing ([AI in Manufacturing](https://www.ultralytics.com/solutions/ai-in-manufacturing)).
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- **Auto Repair**: Assisting mechanics in identifying parts for repair or replacement.
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- **E-commerce Cataloging**: Automatically tagging and categorizing car parts in online stores for [e-commerce](https://en.wikipedia.org/wiki/E-commerce) platforms.
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- **Traffic Monitoring**: Analyzing vehicle components in traffic surveillance footage.
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- **Autonomous Vehicles**: Enhancing the perception systems of [self-driving cars](https://www.ultralytics.com/blog/ai-in-self-driving-cars) to better understand surrounding vehicles.
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- **Insurance Processing**: Automating damage assessment by identifying affected car parts during insurance claims.
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- **Recycling**: Sorting vehicle components for efficient recycling processes.
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- **Smart City Initiatives**: Contributing data for urban planning and traffic management systems within [Smart Cities](https://en.wikipedia.org/wiki/Smart_city).
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By accurately identifying and categorizing different vehicle components, carparts segmentation streamlines processes and contributes to increased efficiency and automation across these industries.
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## Dataset YAML
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A [YAML](https://www.ultralytics.com/glossary/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](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/carparts-seg.yaml). You can learn more about the YAML format at [yaml.org](https://yaml.org/).
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!!! example "ultralytics/cfg/datasets/carparts-seg.yaml"
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```yaml
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--8<-- "ultralytics/cfg/datasets/carparts-seg.yaml"
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```
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## Usage
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To train an [Ultralytics YOLO26](../../models/yolo26.md) model on the Carparts Segmentation dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, use the following code snippets. Refer to the model [Training guide](../../modes/train.md) for a comprehensive list of available arguments and explore [model training tips](https://docs.ultralytics.com/guides/model-training-tips/) for best practices.
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!!! example "Train Example"
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a pretrained segmentation model like YOLO26n-seg
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model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
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# Train the model on the Carparts Segmentation dataset
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results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
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# After training, you can validate the model's performance on the validation set
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results = model.val()
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# Or perform prediction on new images or videos
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results = model.predict("path/to/your/image.jpg")
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```
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=== "CLI"
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```bash
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# Start training from a pretrained *.pt model using the Command Line Interface
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# Specify the dataset config file, model, number of epochs, and image size
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yolo segment train data=carparts-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
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# Validate the trained model using the validation set
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yolo segment val data=carparts-seg.yaml model=path/to/best.pt
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# Predict using the trained model on a specific image source
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yolo segment predict model=path/to/best.pt source=path/to/your/image.jpg
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```
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## Sample Data and Annotations
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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:
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- The image demonstrates [object segmentation](https://docs.ultralytics.com/tasks/segment/) within a car image sample. Annotated [bounding boxes](https://www.ultralytics.com/glossary/bounding-box) with masks highlight the identified car parts (e.g., headlights, grille).
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- 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.
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- This example underscores the dataset's complexity and the importance of [high-quality data](https://www.ultralytics.com/blog/the-importance-of-high-quality-computer-vision-datasets) for computer vision tasks, especially in specialized domains like automotive component analysis. Techniques like [data augmentation](https://www.ultralytics.com/glossary/data-augmentation) can further enhance model generalization.
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## Citations and Acknowledgments
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If you utilize the Carparts Segmentation dataset in your research or development efforts, please cite the original source:
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!!! quote ""
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=== "BibTeX"
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```bibtex
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@misc{ car-seg-un1pm_dataset,
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title = { car-seg Dataset },
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type = { Open Source Dataset },
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author = { Gianmarco Russo },
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url = { https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm },
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year = { 2023 },
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month = { nov },
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note = { visited on 2024-01-24 },
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}
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```
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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](https://docs.ultralytics.com/datasets/).
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## FAQ
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### What is the Carparts Segmentation Dataset?
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The Carparts Segmentation Dataset is a specialized collection of images and videos for training computer vision models to perform [segmentation](https://docs.ultralytics.com/tasks/segment/) on car parts. It includes diverse visuals with detailed annotations, suitable for automotive AI applications.
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### How can I use the Carparts Segmentation Dataset with Ultralytics YOLO26?
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You can train an [Ultralytics YOLO26](../../models/yolo26.md) segmentation model using this dataset. Load a pretrained model (e.g., `yolo26n-seg.pt`) and initiate training using the provided Python or CLI examples, referencing the `carparts-seg.yaml` configuration file. Check the [Training Guide](../../modes/train.md) for detailed instructions.
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!!! example "Train Example Snippet"
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolo26n-seg.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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yolo segment train data=carparts-seg.yaml model=yolo26n-seg.pt epochs=100 imgsz=640
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```
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### What are some applications of Carparts Segmentation?
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Carparts Segmentation is useful in:
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- **Automotive Quality Control**: Ensuring parts meet standards ([AI in Manufacturing](https://www.ultralytics.com/solutions/ai-in-manufacturing)).
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- **Auto Repair**: Identifying parts needing service.
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- **E-commerce**: Cataloging parts online.
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- **Autonomous Vehicles**: Improving vehicle perception ([AI in Automotive](https://www.ultralytics.com/solutions/ai-in-automotive)).
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- **Insurance**: Assessing vehicle damage automatically.
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- **Recycling**: Sorting parts efficiently.
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### Where can I find the dataset configuration file for Carparts Segmentation?
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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](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/carparts-seg.yaml).
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### Why should I use the Carparts Segmentation Dataset?
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This dataset offers rich, annotated data crucial for developing accurate [segmentation models](https://docs.ultralytics.com/tasks/segment/) 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.
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