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193 lines
9.1 KiB
193 lines
9.1 KiB
--- |
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comments: true |
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description: Explore the ImageNette dataset, a subset of ImageNet with 10 classes for efficient training and evaluation of image classification models. Ideal for ML and CV projects. |
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keywords: ImageNette dataset, ImageNet subset, image classification, machine learning, deep learning, YOLO, Convolutional Neural Networks, ML dataset, education, training |
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--- |
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# ImageNette Dataset |
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The [ImageNette](https://github.com/fastai/imagenette) dataset is a subset of the larger [Imagenet](https://www.image-net.org/) dataset, but it only includes 10 easily distinguishable classes. It was created to provide a quicker, easier-to-use version of Imagenet for software development and education. |
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## Key Features |
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- ImageNette contains images from 10 different classes such as tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute. |
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- The dataset comprises colored images of varying dimensions. |
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- ImageNette is widely used for training and testing in the field of machine learning, especially for image classification tasks. |
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## Dataset Structure |
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The ImageNette dataset is split into two subsets: |
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1. **Training Set**: This subset contains several thousands of images used for training machine learning models. The exact number varies per class. |
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2. **Validation Set**: This subset consists of several hundreds of images used for validating and benchmarking the trained models. Again, the exact number varies per class. |
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## Applications |
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The ImageNette dataset is widely used for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models in image classification tasks, such as [Convolutional Neural Networks](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNNs), and various other machine learning algorithms. The dataset's straightforward format and well-chosen classes make it a handy resource for both beginner and experienced practitioners in the field of machine learning and computer vision. |
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## Usage |
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To train a model on the ImageNette dataset for 100 epochs with a standard image size of 224x224, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page. |
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!!! example "Train Example" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training) |
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# Train the model |
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results = model.train(data="imagenette", epochs=100, imgsz=224) |
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``` |
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=== "CLI" |
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```bash |
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# Start training from a pretrained *.pt model |
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yolo classify train data=imagenette model=yolo11n-cls.pt epochs=100 imgsz=224 |
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``` |
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## Sample Images and Annotations |
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The ImageNette dataset contains colored images of various objects and scenes, providing a diverse dataset for [image classification](https://www.ultralytics.com/glossary/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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## ImageNette160 and ImageNette320 |
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For faster prototyping and training, the ImageNette dataset is also available in two reduced sizes: ImageNette160 and ImageNette320. These datasets maintain the same classes and structure as the full ImageNette dataset, but the images are resized to a smaller dimension. As such, these versions of the dataset are particularly useful for preliminary model testing, or when computational resources are limited. |
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To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imagenette320' in the training command. The following code snippets illustrate this: |
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!!! example "Train Example with ImageNette160" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training) |
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# Train the model with ImageNette160 |
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results = model.train(data="imagenette160", epochs=100, imgsz=160) |
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``` |
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=== "CLI" |
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```bash |
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# Start training from a pretrained *.pt model with ImageNette160 |
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yolo classify train data=imagenette160 model=yolo11n-cls.pt epochs=100 imgsz=160 |
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``` |
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!!! example "Train Example with ImageNette320" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training) |
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# Train the model with ImageNette320 |
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results = model.train(data="imagenette320", epochs=100, imgsz=320) |
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``` |
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=== "CLI" |
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```bash |
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# Start training from a pretrained *.pt model with ImageNette320 |
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yolo classify train data=imagenette320 model=yolo11n-cls.pt epochs=100 imgsz=320 |
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``` |
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These smaller versions of the dataset allow for rapid iterations during the development process while still providing valuable and realistic image classification tasks. |
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## Citations and Acknowledgments |
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If you use the ImageNette dataset in your research or development work, please acknowledge it appropriately. For more information about the ImageNette dataset, visit the [ImageNette dataset GitHub page](https://github.com/fastai/imagenette). |
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## FAQ |
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### What is the ImageNette dataset? |
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The [ImageNette dataset](https://github.com/fastai/imagenette) is a simplified subset of the larger [ImageNet dataset](https://www.image-net.org/), featuring only 10 easily distinguishable classes such as tench, English springer, and French horn. It was created to offer a more manageable dataset for efficient training and evaluation of image classification models. This dataset is particularly useful for quick software development and educational purposes in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) and computer vision. |
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### How can I use the ImageNette dataset for training a YOLO model? |
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To train a YOLO model on the ImageNette dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch), you can use the following commands. Make sure to have the Ultralytics YOLO environment set up. |
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!!! example "Train Example" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training) |
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# Train the model |
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results = model.train(data="imagenette", epochs=100, imgsz=224) |
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``` |
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=== "CLI" |
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```bash |
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# Start training from a pretrained *.pt model |
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yolo classify train data=imagenette model=yolo11n-cls.pt epochs=100 imgsz=224 |
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``` |
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For more details, see the [Training](../../modes/train.md) documentation page. |
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### Why should I use ImageNette for image classification tasks? |
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The ImageNette dataset is advantageous for several reasons: |
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- **Quick and Simple**: It contains only 10 classes, making it less complex and time-consuming compared to larger datasets. |
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- **Educational Use**: Ideal for learning and teaching the basics of image classification since it requires less computational power and time. |
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- **Versatility**: Widely used to train and benchmark various machine learning models, especially in image classification. |
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For more details on model training and dataset management, explore the [Dataset Structure](#dataset-structure) section. |
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### Can the ImageNette dataset be used with different image sizes? |
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Yes, the ImageNette dataset is also available in two resized versions: ImageNette160 and ImageNette320. These versions help in faster prototyping and are especially useful when computational resources are limited. |
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!!! example "Train Example with ImageNette160" |
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=== "Python" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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model = YOLO("yolo11n-cls.pt") |
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# Train the model with ImageNette160 |
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results = model.train(data="imagenette160", epochs=100, imgsz=160) |
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``` |
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=== "CLI" |
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```bash |
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# Start training from a pretrained *.pt model with ImageNette160 |
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yolo detect train data=imagenette160 model=yolo11n-cls.pt epochs=100 imgsz=160 |
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``` |
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For more information, refer to [Training with ImageNette160 and ImageNette320](#imagenette160-and-imagenette320). |
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### What are some practical applications of the ImageNette dataset? |
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The ImageNette dataset is extensively used in: |
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- **Educational Settings**: To educate beginners in machine learning and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv). |
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- **Software Development**: For rapid prototyping and development of image classification models. |
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- **Deep Learning Research**: To evaluate and benchmark the performance of various deep learning models, especially Convolutional [Neural Networks](https://www.ultralytics.com/glossary/neural-network-nn) (CNNs). |
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Explore the [Applications](#applications) section for detailed use cases.
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