Optimize Docs images (#15900)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar
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@ -53,7 +53,7 @@ To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the
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The Caltech-101 dataset contains high-quality color images of various objects, providing a well-structured dataset for object recognition tasks. Here are some examples of images from the dataset:
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The example showcases the variety and complexity of the objects in the Caltech-101 dataset, emphasizing the significance of a diverse dataset for training robust object recognition models.
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@ -64,7 +64,7 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
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The Caltech-256 dataset contains high-quality color images of various objects, providing a comprehensive dataset for object recognition tasks. Here are some examples of images from the dataset ([credit](https://ml4a.github.io/demos/tsne_viewer.html)):
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The example showcases the diversity and complexity of the objects in the Caltech-256 dataset, emphasizing the importance of a varied dataset for training robust object recognition models.
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@ -67,7 +67,7 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size
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The CIFAR-10 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
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The example showcases the variety and complexity of the objects in the CIFAR-10 dataset, highlighting the importance of a diverse dataset for training robust image classification models.
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@ -56,7 +56,7 @@ To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size
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The CIFAR-100 dataset contains color images of various objects, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
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The example showcases the variety and complexity of the objects in the CIFAR-100 dataset, highlighting the importance of a diverse dataset for training robust image classification models.
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@ -81,7 +81,7 @@ To train a CNN model on the Fashion-MNIST dataset for 100 epochs with an image s
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The Fashion-MNIST dataset contains grayscale images of Zalando's article images, providing a well-structured dataset for image classification tasks. Here are some examples of images from the dataset:
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The example showcases the variety and complexity of the images in the Fashion-MNIST dataset, highlighting the importance of a diverse dataset for training robust image classification models.
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@ -66,7 +66,7 @@ To train a deep learning model on the ImageNet dataset for 100 epochs with an im
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The ImageNet dataset contains high-resolution images spanning thousands of object categories, providing a diverse and extensive dataset for training and evaluating computer vision models. Here are some examples of images from the dataset:
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The example showcases the variety and complexity of the images in the ImageNet dataset, highlighting the importance of a diverse dataset for training robust computer vision models.
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@ -52,7 +52,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
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The ImageNet10 dataset contains a subset of images from the original ImageNet dataset. These images are chosen to represent the first 10 classes in the dataset, providing a diverse yet compact dataset for quick testing and evaluation.
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 The example showcases the variety and complexity of the images in the ImageNet10 dataset, highlighting its usefulness for sanity checks and quick testing of computer vision models.
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 The example showcases the variety and complexity of the images in the ImageNet10 dataset, highlighting its usefulness for sanity checks and quick testing of computer vision models.
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## Citations and Acknowledgments
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@ -54,7 +54,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
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The ImageNette dataset contains colored images of various objects and scenes, providing a diverse dataset for image classification tasks. Here are some examples of images from the dataset:
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The example showcases the variety and complexity of the images in the ImageNette dataset, highlighting the importance of a diverse dataset for training robust image classification models.
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@ -89,7 +89,7 @@ It's important to note that using smaller images will likely yield lower perform
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The ImageWoof dataset contains colorful images of various dog breeds, providing a challenging dataset for image classification tasks. Here are some examples of images from the dataset:
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The example showcases the subtle differences and similarities among the different dog breeds in the ImageWoof dataset, highlighting the complexity and difficulty of the classification task.
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@ -91,7 +91,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 epochs with an
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The African wildlife dataset comprises a wide variety of images showcasing diverse animal species and their natural habitats. Below are examples of images from the dataset, each accompanied by its corresponding annotations.
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- **Mosaiced Image**: Here, we present a training batch consisting of mosaiced dataset images. Mosaicing, a training technique, combines multiple images into one, enriching batch diversity. This method helps enhance the model's ability to generalize across different object sizes, aspect ratios, and contexts.
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@ -70,7 +70,7 @@ To train a YOLOv8n model on the Argoverse dataset for 100 epochs with an image s
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The Argoverse dataset contains a diverse set of sensor data, including camera images, LiDAR point clouds, and HD map information, providing rich context for autonomous driving tasks. Here are some examples of data from the dataset, along with their corresponding annotations:
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- **Argoverse 3D Tracking**: This image demonstrates an example of 3D object tracking, where objects are annotated with 3D bounding boxes. The dataset provides LiDAR point clouds and camera images to facilitate the development of models for this task.
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@ -90,7 +90,7 @@ To train a YOLOv8n model on the brain tumor dataset for 100 epochs with an image
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The brain tumor dataset encompasses a wide array of images featuring diverse object categories and intricate scenes. Presented below are examples of images from the dataset, accompanied by their respective annotations
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- **Mosaiced Image**: Displayed here is a training batch comprising mosaiced dataset images. Mosaicing, a training technique, consolidates multiple images into one, enhancing batch diversity. This approach aids in improving the model's capacity to generalize across various object sizes, aspect ratios, and contexts.
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@ -87,7 +87,7 @@ To train a YOLOv8n model on the COCO dataset for 100 epochs with an image size o
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The COCO dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations:
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- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
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@ -62,7 +62,7 @@ To train a YOLOv8n model on the COCO8 dataset for 100 epochs with an image size
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Here are some examples of images from the COCO8 dataset, along with their corresponding annotations:
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<img src="https://user-images.githubusercontent.com/26833433/236818348-e6260a3d-0454-436b-83a9-de366ba07235.jpg" alt="Dataset sample image" width="800">
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<img src="https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch-1.avif" alt="Dataset sample image" width="800">
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- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
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@ -65,7 +65,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an
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The Global Wheat Head Dataset contains a diverse set of outdoor field images, capturing the natural variability in wheat head appearances, environments, and conditions. Here are some examples of data from the dataset, along with their corresponding annotations:
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- **Wheat Head Detection**: This image demonstrates an example of wheat head detection, where wheat heads are annotated with bounding boxes. The dataset provides a variety of images to facilitate the development of models for this task.
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@ -34,15 +34,15 @@ names:
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Labels for this format should be exported to YOLO format with one `*.txt` file per image. If there are no objects in an image, no `*.txt` file is required. The `*.txt` file should be formatted with one row per object in `class x_center y_center width height` format. Box coordinates must be in **normalized xywh** format (from 0 to 1). If your boxes are in pixels, you should divide `x_center` and `width` by image width, and `y_center` and `height` by image height. Class numbers should be zero-indexed (start with 0).
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<p align="center"><img width="750" src="https://user-images.githubusercontent.com/26833433/91506361-c7965000-e886-11ea-8291-c72b98c25eec.jpg" alt="Example labelled image"></p>
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<p align="center"><img width="750" src="https://github.com/ultralytics/docs/releases/download/0/two-persons-tie.avif" alt="Example labelled image"></p>
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The label file corresponding to the above image contains 2 persons (class `0`) and a tie (class `27`):
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<p align="center"><img width="428" src="https://user-images.githubusercontent.com/26833433/112467037-d2568c00-8d66-11eb-8796-55402ac0d62f.png" alt="Example label file"></p>
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<p align="center"><img width="428" src="https://github.com/ultralytics/docs/releases/download/0/two-persons-tie-1.avif" alt="Example label file"></p>
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When using the Ultralytics YOLO format, organize your training and validation images and labels as shown in the [COCO8 dataset](coco8.md) example below.
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<p align="center"><img width="800" src="https://github.com/IvorZhu331/ultralytics/assets/26833433/a55ec82d-2bb5-40f9-ac5c-f935e7eb9f07" alt="Example dataset directory structure"></p>
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<p align="center"><img width="800" src="https://github.com/ultralytics/docs/releases/download/0/two-persons-tie-2.avif" alt="Example dataset directory structure"></p>
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## Usage
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@ -20,7 +20,7 @@ The [LVIS dataset](https://www.lvisdataset.org/) is a large-scale, fine-grained
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</p>
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<p align="center">
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<img width="640" src="https://github.com/ultralytics/ultralytics/assets/26833433/40230a80-e7bc-4310-a860-4cc0ef4bb02a" alt="LVIS Dataset example images">
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<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/lvis-dataset-example-images.avif" alt="LVIS Dataset example images">
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</p>
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## Key Features
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@ -83,7 +83,7 @@ To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size o
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The LVIS dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations:
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- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
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@ -154,6 +154,6 @@ Ultralytics YOLO models, including the latest YOLOv8, are optimized for real-tim
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Yes, the LVIS dataset includes a variety of images with diverse object categories and complex scenes. Here is an example of a sample image along with its annotations:
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This mosaiced image demonstrates a training batch composed of multiple dataset images combined into one. Mosaicing increases the variety of objects and scenes within each training batch, enhancing the model's ability to generalize across different contexts. For more details on the LVIS dataset, explore the [LVIS dataset documentation](#key-features).
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@ -65,7 +65,7 @@ To train a YOLOv8n model on the Objects365 dataset for 100 epochs with an image
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The Objects365 dataset contains a diverse set of high-resolution images with objects from 365 categories, providing rich context for object detection tasks. Here are some examples of the images in the dataset:
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- **Objects365**: This image demonstrates an example of object detection, where objects are annotated with bounding boxes. The dataset provides a wide range of images to facilitate the development of models for this task.
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@ -29,7 +29,7 @@ keywords: Open Images V7, Google dataset, computer vision, YOLOv8 models, object
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
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## Key Features
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Illustrations of the dataset help provide insights into its richness:
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- **Open Images V7**: This image exemplifies the depth and detail of annotations available, including bounding boxes, relationships, and segmentation masks.
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@ -9,7 +9,7 @@ keywords: Roboflow 100, Ultralytics, object detection, dataset, benchmarking, ma
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Roboflow 100, developed by [Roboflow](https://roboflow.com/?ref=ultralytics) and sponsored by Intel, is a groundbreaking [object detection](../../tasks/detect.md) benchmark. It includes 100 diverse datasets sampled from over 90,000 public datasets. This benchmark is designed to test the adaptability of models to various domains, including healthcare, aerial imagery, and video games.
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<p align="center">
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<img width="640" src="https://user-images.githubusercontent.com/15908060/202452898-9ca6b8f7-4805-4e8e-949a-6e080d7b94d2.jpg" alt="Roboflow 100 Overview">
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<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/roboflow-100-overview.avif" alt="Roboflow 100 Overview">
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</p>
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## Key Features
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Roboflow 100 consists of datasets with diverse images and videos captured from various angles and domains. Here's a look at examples of annotated images in the RF100 benchmark.
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<p align="center">
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<img width="640" src="https://blog.roboflow.com/content/images/2022/11/image-2.png" alt="Sample Data and Annotations">
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<img width="640" src="https://github.com/ultralytics/docs/releases/download/0/sample-data-annotations.avif" alt="Sample Data and Annotations">
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</p>
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The diversity in the Roboflow 100 benchmark that can be seen above is a significant advancement from traditional benchmarks which often focus on optimizing a single metric within a limited domain.
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@ -79,7 +79,7 @@ To train a YOLOv8n model on the signature detection dataset for 100 epochs with
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The signature detection dataset comprises a wide variety of images showcasing different document types and annotated signatures. Below are examples of images from the dataset, each accompanied by its corresponding annotations.
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- **Mosaiced Image**: Here, we present a training batch consisting of mosaiced dataset images. Mosaicing, a training technique, combines multiple images into one, enriching batch diversity. This method helps enhance the model's ability to generalize across different signature sizes, aspect ratios, and contexts.
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<strong>Watch:</strong> How to Train YOLOv10 on SKU-110k Dataset using Ultralytics | Retail Dataset
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</p>
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## Key Features
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The SKU-110k dataset contains a diverse set of retail shelf images with densely packed objects, providing rich context for object detection tasks. Here are some examples of data from the dataset, along with their corresponding annotations:
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- **Densely packed retail shelf image**: This image demonstrates an example of densely packed objects in a retail shelf setting. Objects are annotated with bounding boxes and SKU category labels.
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The VisDrone dataset contains a diverse set of images and videos captured by drone-mounted cameras. Here are some examples of data from the dataset, along with their corresponding annotations:
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- **Task 1**: Object detection in images - This image demonstrates an example of object detection in images, where objects are annotated with bounding boxes. The dataset provides a wide variety of images taken from different locations, environments, and densities to facilitate the development of models for this task.
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The VOC dataset contains a diverse set of images with various object categories and complex scenes. Here are some examples of images from the dataset, along with their corresponding annotations:
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- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
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The xView dataset contains high-resolution satellite images with a diverse set of objects annotated using bounding boxes. Here are some examples of data from the dataset, along with their corresponding annotations:
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- **Overhead Imagery**: This image demonstrates an example of object detection in overhead imagery, where objects are annotated with bounding boxes. The dataset provides high-resolution satellite images to facilitate the development of models for this task.
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@ -9,7 +9,7 @@ keywords: Ultralytics Explorer GUI, semantic search, vector similarity, SQL quer
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Explorer GUI is like a playground build using [Ultralytics Explorer API](api.md). It allows you to run semantic/vector similarity search, SQL queries and even search using natural language using our ask AI feature powered by LLMs.
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<p>
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<img width="1709" alt="Explorer Dashboard Screenshot 1" src="https://github.com/ultralytics/ultralytics/assets/15766192/feb1fe05-58c5-4173-a9ff-e611e3bba3d0">
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<img width="1709" alt="Explorer Dashboard Screenshot 1" src="https://github.com/ultralytics/docs/releases/download/0/explorer-dashboard-screenshot-1.avif">
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</p>
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<p align="center">
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@ -41,19 +41,19 @@ Semantic search is a technique for finding similar images to a given image. It i
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For example:
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In this VOC Exploration dashboard, user selects a couple airplane images like this:
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<p>
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<img width="1710" alt="Explorer Dashboard Screenshot 2" src="https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/3becdc1d-45dc-43b7-88ff-84ff0b443894">
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<img width="1710" alt="Explorer Dashboard Screenshot 2" src="https://github.com/ultralytics/docs/releases/download/0/explorer-dashboard-screenshot-2.avif">
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</p>
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On performing similarity search, you should see a similar result:
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<p>
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<img width="1710" alt="Explorer Dashboard Screenshot 3" src="https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/aeea2e16-bc2b-41bb-9aef-4a33bfa1a800">
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<img width="1710" alt="Explorer Dashboard Screenshot 3" src="https://github.com/ultralytics/docs/releases/download/0/explorer-dashboard-screenshot-3.avif">
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</p>
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## Ask AI
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This allows you to write how you want to filter your dataset using natural language. You don't have to be proficient in writing SQL queries. Our AI powered query generator will automatically do that under the hood. For example - you can say - "show me 100 images with exactly one person and 2 dogs. There can be other objects too" and it'll internally generate the query and show you those results. Here's an example output when asked to "Show 10 images with exactly 5 persons" and you'll see a result like this:
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<p>
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<img width="1709" alt="Explorer Dashboard Screenshot 4" src="https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/55a67181-3b25-4d2f-b786-2a6a08a0cb6b">
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<img width="1709" alt="Explorer Dashboard Screenshot 4" src="https://github.com/ultralytics/docs/releases/download/0/explorer-dashboard-screenshot-4.avif">
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</p>
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|
||||
Note: This works using LLMs under the hood so the results are probabilistic and might get things wrong sometimes
|
||||
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|
@ -67,7 +67,7 @@ WHERE labels LIKE '%person%' AND labels LIKE '%dog%'
|
|||
```
|
||||
|
||||
<p>
|
||||
<img width="1707" alt="Explorer Dashboard Screenshot 5" src="https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/14fbb237-0b2d-4b7c-8f62-2fca4e6cc26f">
|
||||
<img width="1707" alt="Explorer Dashboard Screenshot 5" src="https://github.com/ultralytics/docs/releases/download/0/explorer-dashboard-screenshot-5.avif">
|
||||
</p>
|
||||
|
||||
This is a Demo build using the Explorer API. You can use the API to build your own exploratory notebooks or scripts to get insights into your datasets. Learn more about the Explorer API [here](api.md).
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ keywords: Ultralytics Explorer, CV datasets, semantic search, SQL queries, vecto
|
|||
# Ultralytics Explorer
|
||||
|
||||
<p>
|
||||
<img width="1709" alt="Ultralytics Explorer Screenshot 1" src="https://github.com/ultralytics/ultralytics/assets/15766192/feb1fe05-58c5-4173-a9ff-e611e3bba3d0">
|
||||
<img width="1709" alt="Ultralytics Explorer Screenshot 1" src="https://github.com/ultralytics/docs/releases/download/0/explorer-dashboard-screenshot-1.avif">
|
||||
</p>
|
||||
|
||||
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
||||
|
|
@ -56,7 +56,7 @@ yolo explorer
|
|||
You can set it like this - `yolo settings openai_api_key="..."`
|
||||
|
||||
<p>
|
||||
<img width="1709" alt="Ultralytics Explorer OpenAI Integration" src="https://github.com/AyushExel/assets/assets/15766192/1b5f3708-be3e-44c5-9ea3-adcd522dfc75">
|
||||
<img width="1709" alt="Ultralytics Explorer OpenAI Integration" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-explorer-openai-integration.avif">
|
||||
</p>
|
||||
|
||||
## FAQ
|
||||
|
|
|
|||
|
|
@ -24,7 +24,7 @@ Ultralytics provides support for various datasets to facilitate computer vision
|
|||
Create embeddings for your dataset, search for similar images, run SQL queries, perform semantic search and even search using natural language! You can get started with our GUI app or build your own using the API. Learn more [here](explorer/index.md).
|
||||
|
||||
<p>
|
||||
<img alt="Ultralytics Explorer Screenshot" src="https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/d2ebaffd-e065-4d88-983a-33cb6f593785">
|
||||
<img alt="Ultralytics Explorer Screenshot" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-explorer-screenshot.avif">
|
||||
</p>
|
||||
|
||||
- Try the [GUI Demo](explorer/index.md)
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ keywords: DOTA dataset, object detection, aerial images, oriented bounding boxes
|
|||
|
||||
[DOTA](https://captain-whu.github.io/DOTA/index.html) stands as a specialized dataset, emphasizing object detection in aerial images. Originating from the DOTA series of datasets, it offers annotated images capturing a diverse array of aerial scenes with Oriented Bounding Boxes (OBB).
|
||||
|
||||
|
||||
|
||||
|
||||
## Key Features
|
||||
|
||||
|
|
@ -126,7 +126,7 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
|
|||
|
||||
Having a glance at the dataset illustrates its depth:
|
||||
|
||||
|
||||
|
||||
|
||||
- **DOTA examples**: This snapshot underlines the complexity of aerial scenes and the significance of Oriented Bounding Box annotations, capturing objects in their natural orientation.
|
||||
|
||||
|
|
|
|||
|
|
@ -51,7 +51,7 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image s
|
|||
|
||||
Here are some examples of images from the DOTA8 dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://github.com/Laughing-q/assets/assets/61612323/965d3ff7-5b9b-4add-b62e-9795921b60de" alt="Dataset sample image" width="800">
|
||||
<img src="https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch.avif" alt="Dataset sample image" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ class_index x1 y1 x2 y2 x3 y3 x4 y4
|
|||
|
||||
Internally, YOLO processes losses and outputs in the `xywhr` format, which represents the bounding box's center point (xy), width, height, and rotation.
|
||||
|
||||
<p align="center"><img width="800" src="https://user-images.githubusercontent.com/26833433/259471881-59020fe2-09a4-4dcc-acce-9b0f7cfa40ee.png" alt="OBB format examples"></p>
|
||||
<p align="center"><img width="800" src="https://github.com/ultralytics/docs/releases/download/0/obb-format-examples.avif" alt="OBB format examples"></p>
|
||||
|
||||
An example of a `*.txt` label file for the above image, which contains an object of class `0` in OBB format, could look like:
|
||||
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ keywords: COCO-Pose, pose estimation, dataset, keypoints, COCO Keypoints 2017, Y
|
|||
|
||||
The [COCO-Pose](https://cocodataset.org/#keypoints-2017) dataset is a specialized version of the COCO (Common Objects in Context) dataset, designed for pose estimation tasks. It leverages the COCO Keypoints 2017 images and labels to enable the training of models like YOLO for pose estimation tasks.
|
||||
|
||||
|
||||
|
||||
|
||||
## COCO-Pose Pretrained Models
|
||||
|
||||
|
|
@ -78,7 +78,7 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 epochs with an im
|
|||
|
||||
The COCO-Pose dataset contains a diverse set of images with human figures annotated with keypoints. Here are some examples of images from the dataset, along with their corresponding annotations:
|
||||
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
|
|
|
|||
|
|
@ -51,7 +51,7 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
|
|||
|
||||
Here are some examples of images from the COCO8-Pose dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://user-images.githubusercontent.com/26833433/236818283-52eecb96-fc6a-420d-8a26-d488b352dd4c.jpg" alt="Dataset sample image" width="800">
|
||||
<img src="https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch-5.avif" alt="Dataset sample image" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
|
|
|
|||
|
|
@ -64,7 +64,7 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
|
|||
|
||||
Here are some examples of images from the Tiger-Pose dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://user-images.githubusercontent.com/62513924/272491921-c963d2bf-505f-4a15-abd7-259de302cffa.jpg" alt="Dataset sample image" width="100%">
|
||||
<img src="https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch-4.avif" alt="Dataset sample image" width="100%">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
|
|
|
|||
|
|
@ -72,7 +72,7 @@ To train Ultralytics YOLOv8n model on the Carparts Segmentation dataset for 100
|
|||
|
||||
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.
|
||||
|
|
|
|||
|
|
@ -76,7 +76,7 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag
|
|||
|
||||
COCO-Seg, like its predecessor COCO, contains a diverse set of images with various object categories and complex scenes. However, COCO-Seg introduces more detailed instance segmentation masks for each object in the images. Here are some examples of images from the dataset, along with their corresponding instance segmentation masks:
|
||||
|
||||
|
||||
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This aids the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
|
|
|
|||
|
|
@ -51,7 +51,7 @@ To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 epochs with an ima
|
|||
|
||||
Here are some examples of images from the COCO8-Seg dataset, along with their corresponding annotations:
|
||||
|
||||
<img src="https://user-images.githubusercontent.com/26833433/236818387-f7bde7df-caaa-46d1-8341-1f7504cd11a1.jpg" alt="Dataset sample image" width="800">
|
||||
<img src="https://github.com/ultralytics/docs/releases/download/0/mosaiced-training-batch-2.avif" alt="Dataset sample image" width="800">
|
||||
|
||||
- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
|
||||
|
||||
|
|
|
|||
|
|
@ -61,7 +61,7 @@ To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 epo
|
|||
|
||||
The Crack Segmentation dataset comprises a varied collection of images and videos captured from multiple perspectives. Below are instances of data from the dataset, accompanied by their respective annotations:
|
||||
|
||||
|
||||
|
||||
|
||||
- This image presents an example of image object segmentation, featuring annotated bounding boxes with masks outlining identified objects. The dataset includes a diverse array of images taken in different locations, environments, and densities, making it a comprehensive resource for developing models designed for this particular task.
|
||||
|
||||
|
|
|
|||
|
|
@ -61,7 +61,7 @@ To train Ultralytics YOLOv8n model on the Package Segmentation dataset for 100 e
|
|||
|
||||
The Package Segmentation dataset comprises a varied collection of images and videos captured from multiple perspectives. Below are instances of data from the dataset, accompanied by their respective annotations:
|
||||
|
||||
|
||||
|
||||
|
||||
- This image displays an instance of image object detection, featuring annotated bounding boxes with masks outlining recognized objects. The dataset incorporates a diverse collection of images taken in different locations, environments, and densities. It serves as a comprehensive resource for developing models specific to this task.
|
||||
- The example emphasizes the diversity and complexity present in the VisDrone dataset, underscoring the significance of high-quality sensor data for computer vision tasks involving drones.
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue