Update URLs to redirects (#16048)
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@ -6,7 +6,7 @@ keywords: ImageNet10, ImageNet, Ultralytics, CI tests, sanity checks, training p
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# ImageNet10 Dataset
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The [ImageNet10](https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenet10.zip) dataset is a small-scale subset of the [ImageNet](https://www.image-net.org/) database, developed by [Ultralytics](https://ultralytics.com) and designed for CI tests, sanity checks, and fast testing of training pipelines. This dataset is composed of the first image in the training set and the first image from the validation set of the first 10 classes in ImageNet. Although significantly smaller, it retains the structure and diversity of the original ImageNet dataset.
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The [ImageNet10](https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenet10.zip) dataset is a small-scale subset of the [ImageNet](https://www.image-net.org/) database, developed by [Ultralytics](https://www.ultralytics.com/) and designed for CI tests, sanity checks, and fast testing of training pipelines. This dataset is composed of the first image in the training set and the first image from the validation set of the first 10 classes in ImageNet. Although significantly smaller, it retains the structure and diversity of the original ImageNet dataset.
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## Key Features
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@ -8,7 +8,7 @@ keywords: YOLO, image classification, dataset structure, CIFAR-10, Ultralytics,
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### Dataset Structure for YOLO Classification Tasks
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For [Ultralytics](https://ultralytics.com) YOLO classification tasks, the dataset must be organized in a specific split-directory structure under the `root` directory to facilitate proper training, testing, and optional validation processes. This structure includes separate directories for training (`train`) and testing (`test`) phases, with an optional directory for validation (`val`).
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For [Ultralytics](https://www.ultralytics.com/) YOLO classification tasks, the dataset must be organized in a specific split-directory structure under the `root` directory to facilitate proper training, testing, and optional validation processes. This structure includes separate directories for training (`train`) and testing (`test`) phases, with an optional directory for validation (`val`).
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Each of these directories should contain one subdirectory for each class in the dataset. The subdirectories are named after the corresponding class and contain all the images for that class. Ensure that each image file is named uniquely and stored in a common format such as JPEG or PNG.
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@ -8,7 +8,7 @@ keywords: COCO8, Ultralytics, dataset, object detection, YOLOv8, training, valid
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## Introduction
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[Ultralytics](https://ultralytics.com) COCO8 is a small, but versatile object detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
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[Ultralytics](https://www.ultralytics.com/) COCO8 is a small, but versatile object detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
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<p align="center">
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<br>
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@ -21,7 +21,7 @@ keywords: COCO8, Ultralytics, dataset, object detection, YOLOv8, training, valid
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<strong>Watch:</strong> Ultralytics COCO Dataset Overview
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</p>
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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## Dataset YAML
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@ -124,7 +124,7 @@ For a comprehensive list of available arguments, refer to the model [Training](.
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### Why should I use Ultralytics HUB for managing my COCO8 training?
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Ultralytics HUB is an all-in-one web tool designed to simplify the training and deployment of YOLO models, including the Ultralytics YOLOv8 models on the COCO8 dataset. It offers cloud training, real-time tracking, and seamless dataset management. HUB allows you to start training with a single click and avoids the complexities of manual setups. Discover more about [Ultralytics HUB](https://hub.ultralytics.com) and its benefits.
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Ultralytics HUB is an all-in-one web tool designed to simplify the training and deployment of YOLO models, including the Ultralytics YOLOv8 models on the COCO8 dataset. It offers cloud training, real-time tracking, and seamless dataset management. HUB allows you to start training with a single click and avoids the complexities of manual setups. Discover more about [Ultralytics HUB](https://hub.ultralytics.com/) and its benefits.
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### What are the benefits of using mosaic augmentation in training with the COCO8 dataset?
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@ -95,7 +95,7 @@ For more ideas and inspiration on real-world applications, be sure to check out
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## Usage
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The Roboflow 100 dataset is available on both [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100).
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The Roboflow 100 dataset is available on both [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100?ref=ultralytics).
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You can access it directly from the Roboflow 100 GitHub repository. In addition, on Roboflow Universe, you have the flexibility to download individual datasets by simply clicking the export button within each dataset.
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@ -197,7 +197,7 @@ This setup allows for extensive and varied testing of models across different re
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### How do I access and download the Roboflow 100 dataset?
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The **Roboflow 100** dataset is accessible on [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100). You can download the entire dataset from GitHub or select individual datasets on Roboflow Universe using the export button.
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The **Roboflow 100** dataset is accessible on [GitHub](https://github.com/roboflow/roboflow-100-benchmark) and [Roboflow Universe](https://universe.roboflow.com/roboflow-100?ref=ultralytics). You can download the entire dataset from GitHub or select individual datasets on Roboflow Universe using the export button.
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### What should I include when citing the Roboflow 100 dataset in my research?
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@ -8,9 +8,9 @@ keywords: DOTA8 dataset, Ultralytics, YOLOv8, object detection, debugging, train
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## Introduction
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[Ultralytics](https://ultralytics.com) DOTA8 is a small, but versatile oriented object detection dataset composed of the first 8 images of 8 images of the split DOTAv1 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
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[Ultralytics](https://www.ultralytics.com/) DOTA8 is a small, but versatile oriented object detection dataset composed of the first 8 images of 8 images of the split DOTAv1 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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## Dataset YAML
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@ -8,9 +8,9 @@ keywords: COCO8-Pose, Ultralytics, pose detection dataset, object detection, YOL
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## Introduction
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[Ultralytics](https://ultralytics.com) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
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[Ultralytics](https://www.ultralytics.com/) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging object detection models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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## Dataset YAML
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@ -101,7 +101,7 @@ This section outlines the datasets that are compatible with Ultralytics YOLO for
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### COCO8-Pose
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- **Description**: [Ultralytics](https://ultralytics.com) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation.
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- **Description**: [Ultralytics](https://www.ultralytics.com/) COCO8-Pose is a small, but versatile pose detection dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation.
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- **Label Format**: Same as Ultralytics YOLO format as described above, with keypoints for human poses.
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- **Number of Classes**: 1 (Human).
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- **Keypoints**: 17 keypoints including nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles.
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@ -111,7 +111,7 @@ This section outlines the datasets that are compatible with Ultralytics YOLO for
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### Tiger-Pose
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- **Description**: [Ultralytics](https://ultralytics.com) This animal pose dataset comprises 263 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation.
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- **Description**: [Ultralytics](https://www.ultralytics.com/) This animal pose dataset comprises 263 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation.
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- **Label Format**: Same as Ultralytics YOLO format as described above, with 12 keypoints for animal pose and no visible dimension.
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- **Number of Classes**: 1 (Tiger).
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- **Keypoints**: 12 keypoints.
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@ -8,11 +8,11 @@ keywords: Ultralytics, Tiger-Pose, dataset, pose estimation, YOLOv8, training da
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## Introduction
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[Ultralytics](https://ultralytics.com) introduces the Tiger-Pose dataset, a versatile collection designed for pose estimation tasks. This dataset comprises 263 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation. It serves as an excellent resource for testing and troubleshooting pose estimation algorithm.
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[Ultralytics](https://www.ultralytics.com/) introduces the Tiger-Pose dataset, a versatile collection designed for pose estimation tasks. This dataset comprises 263 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation. It serves as an excellent resource for testing and troubleshooting pose estimation algorithm.
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Despite its manageable size of 210 images, tiger-pose dataset offers diversity, making it suitable for assessing training pipelines, identifying potential errors, and serving as a valuable preliminary step before working with larger datasets for pose estimation.
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This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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<p align="center">
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<br>
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@ -101,7 +101,7 @@ The dataset has been released available under the [AGPL-3.0 License](https://git
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### What is the Ultralytics Tiger-Pose dataset used for?
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The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a [YouTube video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0). The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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### How do I train a YOLOv8 model on the Tiger-Pose dataset?
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### What are the benefits of using the Tiger-Pose dataset for pose estimation?
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The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics), enhancing model performance and accuracy.
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The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like [Ultralytics HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics), enhancing model performance and accuracy.
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@ -6,7 +6,7 @@ keywords: Carparts Segmentation Dataset, Roboflow, computer vision, automotive A
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# Roboflow Universe Carparts Segmentation Dataset
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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.
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The [Roboflow](https://roboflow.com/?ref=ultralytics) [Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics) 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.
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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.
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}
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```
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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).
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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?ref=ultralytics).
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## FAQ
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### What is the Roboflow Carparts Segmentation Dataset?
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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.
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The [Roboflow Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics) 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.
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### How can I use the Carparts Segmentation Dataset with Ultralytics YOLOv8?
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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.
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For more details, visit the [CarParts Segmentation Dataset Page](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm).
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For more details, visit the [CarParts Segmentation Dataset Page](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics).
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## Introduction
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[Ultralytics](https://ultralytics.com) COCO8-Seg is a small, but versatile instance segmentation dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
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[Ultralytics](https://www.ultralytics.com/) COCO8-Seg is a small, but versatile instance segmentation dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com/) and [YOLOv8](https://github.com/ultralytics/ultralytics).
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## Dataset YAML
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### What is the COCO8-Seg dataset, and how is it used in Ultralytics YOLOv8?
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The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLOv8](https://github.com/ultralytics/ultralytics) and [HUB](https://hub.ultralytics.com) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
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The **COCO8-Seg dataset** is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics [YOLOv8](https://github.com/ultralytics/ultralytics) and [HUB](https://hub.ultralytics.com/) for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model [Training](../../modes/train.md) page.
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### How can I train a YOLOv8n-seg model using the COCO8-Seg dataset?
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# Roboflow Universe Crack Segmentation Dataset
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The [Roboflow](https://roboflow.com/?ref=ultralytics) [Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr) stands out as an extensive resource designed specifically for individuals involved in transportation and public safety studies. It is equally beneficial for those working on the development of self-driving car models or simply exploring computer vision applications for recreational purposes.
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The [Roboflow](https://roboflow.com/?ref=ultralytics) [Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) stands out as an extensive resource designed specifically for individuals involved in transportation and public safety studies. It is equally beneficial for those working on the development of self-driving car models or simply exploring computer vision applications for recreational purposes.
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Comprising a total of 4029 static images captured from diverse road and wall scenarios, this dataset emerges as a valuable asset for tasks related to crack segmentation. Whether you are delving into the intricacies of transportation research or seeking to enhance the accuracy of your self-driving car models, this dataset provides a rich and varied collection of images to support your endeavors.
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}
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```
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We would like to acknowledge the Roboflow team for creating and maintaining the Crack Segmentation dataset as a valuable resource for the road safety and research projects. For more information about the Crack segmentation dataset and its creators, visit the [Crack Segmentation Dataset Page](https://universe.roboflow.com/university-bswxt/crack-bphdr).
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We would like to acknowledge the Roboflow team for creating and maintaining the Crack Segmentation dataset as a valuable resource for the road safety and research projects. For more information about the Crack segmentation dataset and its creators, visit the [Crack Segmentation Dataset Page](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics).
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## FAQ
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### What is the Roboflow Crack Segmentation Dataset?
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The [Roboflow Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr) is a comprehensive collection of 4029 static images designed specifically for transportation and public safety studies. It is ideal for tasks such as self-driving car model development and infrastructure maintenance. The dataset includes training, testing, and validation sets, aiding in accurate crack detection and segmentation.
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The [Roboflow Crack Segmentation Dataset](https://universe.roboflow.com/university-bswxt/crack-bphdr?ref=ultralytics) is a comprehensive collection of 4029 static images designed specifically for transportation and public safety studies. It is ideal for tasks such as self-driving car model development and infrastructure maintenance. The dataset includes training, testing, and validation sets, aiding in accurate crack detection and segmentation.
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### How do I train a model using the Crack Segmentation Dataset with Ultralytics YOLOv8?
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@ -6,7 +6,7 @@ keywords: Roboflow, Package Segmentation Dataset, computer vision, package ident
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# Roboflow Universe Package Segmentation Dataset
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The [Roboflow](https://roboflow.com/?ref=ultralytics) [Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package) is a curated collection of images specifically tailored for tasks related to package segmentation in the field of computer vision. This dataset is designed to assist researchers, developers, and enthusiasts working on projects related to package identification, sorting, and handling.
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The [Roboflow](https://roboflow.com/?ref=ultralytics) [Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics) is a curated collection of images specifically tailored for tasks related to package segmentation in the field of computer vision. This dataset is designed to assist researchers, developers, and enthusiasts working on projects related to package identification, sorting, and handling.
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Containing a diverse set of images showcasing various packages in different contexts and environments, the dataset serves as a valuable resource for training and evaluating segmentation models. Whether you are engaged in logistics, warehouse automation, or any application requiring precise package analysis, the Package Segmentation Dataset provides a targeted and comprehensive set of images to enhance the performance of your computer vision algorithms.
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@ -89,13 +89,13 @@ If you integrate the crack segmentation dataset into your research or developmen
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}
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```
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We express our gratitude to the Roboflow team for their efforts in creating and maintaining the Package Segmentation dataset, a valuable asset for logistics and research projects. For additional details about the Package Segmentation dataset and its creators, please visit the [Package Segmentation Dataset Page](https://universe.roboflow.com/factorypackage/factory_package).
|
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We express our gratitude to the Roboflow team for their efforts in creating and maintaining the Package Segmentation dataset, a valuable asset for logistics and research projects. For additional details about the Package Segmentation dataset and its creators, please visit the [Package Segmentation Dataset Page](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics).
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## FAQ
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### What is the Roboflow Package Segmentation Dataset and how can it help in computer vision projects?
|
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|
||||
The [Roboflow Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package) is a curated collection of images tailored for tasks involving package segmentation. It includes diverse images of packages in various contexts, making it invaluable for training and evaluating segmentation models. This dataset is particularly useful for applications in logistics, warehouse automation, and any project requiring precise package analysis. It helps optimize logistics and enhance vision models for accurate package identification and sorting.
|
||||
The [Roboflow Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics) is a curated collection of images tailored for tasks involving package segmentation. It includes diverse images of packages in various contexts, making it invaluable for training and evaluating segmentation models. This dataset is particularly useful for applications in logistics, warehouse automation, and any project requiring precise package analysis. It helps optimize logistics and enhance vision models for accurate package identification and sorting.
|
||||
|
||||
### How do I train an Ultralytics YOLOv8 model on the Package Segmentation Dataset?
|
||||
|
||||
|
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Reference in a new issue