Update https://docs.ultralytics.com/models (#6513)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@ -61,7 +61,7 @@ The example showcases the variety and complexity of the objects in the Caltech-1
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If you use the Caltech-101 dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -61,7 +61,7 @@ The example showcases the diversity and complexity of the objects in the Caltech
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If you use the Caltech-256 dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -64,7 +64,7 @@ The example showcases the variety and complexity of the objects in the CIFAR-10
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If you use the CIFAR-10 dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -64,7 +64,7 @@ The example showcases the variety and complexity of the objects in the CIFAR-100
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If you use the CIFAR-100 dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -64,7 +64,7 @@ The example showcases the variety and complexity of the images in the ImageNet d
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If you use the ImageNet dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -59,7 +59,7 @@ The example showcases the variety and complexity of the images in the ImageNet10
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If you use the ImageNet10 dataset in your research or development work, please cite the original ImageNet paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -80,7 +80,7 @@ In this example, the `train` directory contains subdirectories for each class in
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## Usage
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!!! Example ""
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!!! Example
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=== "Python"
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@ -69,7 +69,7 @@ If you use the MNIST dataset in your
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research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -80,7 +80,7 @@ The example showcases the variety and complexity of the data in the Argoverse da
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If you use the Argoverse dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -76,7 +76,7 @@ The example showcases the variety and complexity of the images in the COCO datas
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If you use the COCO dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -62,7 +62,7 @@ The example showcases the variety and complexity of the images in the COCO8 data
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If you use the COCO dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -75,7 +75,7 @@ The example showcases the variety and complexity of the data in the Global Wheat
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If you use the Global Wheat Head Dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -48,7 +48,7 @@ When using the Ultralytics YOLO format, organize your training and validation im
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Here's how you can use these formats to train your model:
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!!! Example ""
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!!! Example
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=== "Python"
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@ -93,7 +93,7 @@ If you have your own dataset and would like to use it for training detection mod
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You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
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!!! Example ""
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!!! Example
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=== "Python"
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@ -75,7 +75,7 @@ The example showcases the variety and complexity of the data in the Objects365 d
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If you use the Objects365 dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -94,7 +94,7 @@ Researchers can gain invaluable insights into the array of computer vision chall
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For those employing Open Images V7 in their work, it's prudent to cite the relevant papers and acknowledge the creators:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -77,7 +77,7 @@ The example showcases the variety and complexity of the data in the SKU-110k dat
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If you use the SKU-110k dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -73,7 +73,7 @@ The example showcases the variety and complexity of the data in the VisDrone dat
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If you use the VisDrone dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -77,7 +77,7 @@ The example showcases the variety and complexity of the images in the VOC datase
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If you use the VOC dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -79,7 +79,7 @@ The example showcases the variety and complexity of the data in the xView datase
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If you use the xView dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -109,7 +109,7 @@ The dataset's richness offers invaluable insights into object detection challeng
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For those leveraging DOTA v2 in their endeavors, it's pertinent to cite the relevant research papers:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -32,7 +32,7 @@ An example of a `*.txt` label file for the above image, which contains an object
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To train a model using these OBB formats:
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!!! Example ""
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!!! Example
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=== "Python"
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@ -69,7 +69,7 @@ For those looking to introduce their own datasets with oriented bounding boxes,
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Transitioning labels from the DOTA dataset format to the YOLO OBB format can be achieved with this script:
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!!! Example ""
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!!! Example
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=== "Python"
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@ -77,7 +77,7 @@ The example showcases the variety and complexity of the images in the COCO-Pose
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If you use the COCO-Pose dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -62,7 +62,7 @@ The example showcases the variety and complexity of the images in the COCO8-Pose
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If you use the COCO dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -64,7 +64,7 @@ The `train` and `val` fields specify the paths to the directories containing the
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## Usage
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!!! Example ""
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!!! Example
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=== "Python"
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@ -125,7 +125,7 @@ If you have your own dataset and would like to use it for training pose estimati
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Ultralytics provides a convenient conversion tool to convert labels from the popular COCO dataset format to YOLO format:
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!!! Example ""
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!!! Example
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=== "Python"
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@ -76,7 +76,7 @@ The example showcases the variety and complexity of the images in the COCO-Seg d
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If you use the COCO-Seg dataset in your research or development work, please cite the original COCO paper and acknowledge the extension to COCO-Seg:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -62,7 +62,7 @@ The example showcases the variety and complexity of the images in the COCO8-Seg
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If you use the COCO dataset in your research or development work, please cite the following paper:
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!!! Note ""
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!!! Quote ""
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=== "BibTeX"
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@ -66,7 +66,7 @@ The `train` and `val` fields specify the paths to the directories containing the
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## Usage
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!!! Example ""
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!!! Example
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=== "Python"
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@ -101,7 +101,7 @@ If you have your own dataset and would like to use it for training segmentation
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You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:
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!!! Example ""
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!!! Example
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=== "Python"
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@ -123,7 +123,7 @@ Auto-annotation is an essential feature that allows you to generate a segmentati
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To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
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!!! Example ""
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!!! Example
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=== "Python"
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@ -12,7 +12,7 @@ Multi-Object Detector doesn't need standalone training and directly supports pre
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## Usage
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!!! Example ""
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!!! Example
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=== "Python"
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