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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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docs/en/datasets/classify/caltech101.md
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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
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:
![Dataset sample image](https://user-images.githubusercontent.com/26833433/239366386-44171121-b745-4206-9b59-a3be41e16089.png)
![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/caltech101-sample-image.avif)
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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docs/en/datasets/classify/caltech256.md
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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
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)):
![Dataset sample image](https://user-images.githubusercontent.com/26833433/239365061-1e5f7857-b1e8-44ca-b3d7-d0befbcd33f9.jpg)
![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/caltech256-sample-image.avif)
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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docs/en/datasets/classify/cifar10.md
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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
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:
![Dataset sample image](https://miro.medium.com/max/1100/1*SZnidBt7CQ4Xqcag6rd8Ew.png)
![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/cifar10-sample-image.avif)
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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docs/en/datasets/classify/cifar100.md
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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
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:
![Dataset sample image](https://user-images.githubusercontent.com/26833433/239363319-62ebf02f-7469-4178-b066-ccac3cd334db.jpg)
![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/cifar100-sample-image.avif)
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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docs/en/datasets/classify/fashion-mnist.md
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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
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:
![Dataset sample image](https://user-images.githubusercontent.com/26833433/239359139-ce0a434e-9056-43e0-a306-3214f193dcce.png)
![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/fashion-mnist-sample.avif)
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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docs/en/datasets/classify/imagenet.md
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@ -66,7 +66,7 @@ To train a deep learning model on the ImageNet dataset for 100 epochs with an im
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:
![Dataset sample images](https://user-images.githubusercontent.com/26833433/239280348-3d8f30c7-6f05-4dda-9cfe-d62ad9faecc9.png)
![Dataset sample images](https://github.com/ultralytics/docs/releases/download/0/imagenet-sample-images.avif)
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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docs/en/datasets/classify/imagenet10.md
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@ -52,7 +52,7 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
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.
![Dataset sample images](https://user-images.githubusercontent.com/26833433/239689723-16f9b4a7-becc-4deb-b875-d3e5c28eb03b.png) 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.
![Dataset sample images](https://github.com/ultralytics/docs/releases/download/0/imagenet10-sample-images.avif) 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.
## Citations and Acknowledgments

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docs/en/datasets/classify/imagenette.md
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@ -54,7 +54,7 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
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:
![Dataset sample image](https://docs.fast.ai/22_tutorial.imagenette_files/figure-html/cell-21-output-1.png)
![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/imagenette-sample-image.avif)
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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docs/en/datasets/classify/imagewoof.md
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@ -89,7 +89,7 @@ It's important to note that using smaller images will likely yield lower perform
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:
![Dataset sample image](https://user-images.githubusercontent.com/26833433/239357533-ec833254-4351-491b-8cb3-59578ea5d0b2.png)
![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/imagewoof-dataset-sample.avif)
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.