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126 changes: 126 additions & 0 deletions EXAMPLES.md
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## Learn from Examples
Learn the basics of fastdup through interactive examples. View the notebooks on GitHub or nbviewer. Even better, run them on Google Colab or Kaggle, for free.

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<a href="https://visual-layer.readme.io/docs/cleaning-image-dataset">
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<b>🧹 Clean Image Folder:</b> Learn how to analyze and clean a folder of images from potential issues and export a list of problematic files for further action. If you have an unorganized folder of images, this is a good place to start.
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<b>📌 Dataset:</b> <a href="https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/">Food-101</a>.
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<a href="https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb">
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<a href="https://github.com/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb">
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<a href="https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb">
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<a href="https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb">
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<a href="https://visual-layer.readme.io/docs/analyzing-labeled-images">
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<b>🖼 Analyze Image Classification Dataset:</b> Learn how to load a labeled image classification dataset and analyze for potential issues. If you have labeled ImageNet-style folder structure, have a go!
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<b>📌 Dataset:</b> <a href="https://github.com/fastai/imagenette">Imagenette</a>.
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<a href="https://github.com/visual-layer/fastdup/blob/main/examples/analyzing-image-classification-dataset.ipynb">
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<a href="https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/analyzing-image-classification-dataset.ipynb">
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<a href="https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/analysing-image-classification-dataset.ipynb">
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<a href="https://visual-layer.readme.io/docs/objects-and-bounding-boxes">
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<b>🎁 Analyze Object Detection Dataset:</b> Learn how to load bounding box annotations for object detection and analyze for potential issues. If you have a COCO-style labeled object detection dataset, give this example a try.
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<b>📌 Dataset:</b> <a href="https://cocodataset.org/#home">COCO</a>.
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<a href="https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb">
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<a href="https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb">
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<a href="https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb">
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## Load Data From Sources
The notebooks in this section show how to load data from various sources and analyze them with fastdup.

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<b>🦖 ONNX Embeddings:</b> Bring your own ONNX model. In this example we extract feature vectors of your images using <a href="https://github.com/facebookresearch/dinov2">DINOv2</a> model. Runs on CPU.
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<a href="https://github.com/visual-layer/fastdup/blob/main/examples/dinov2_notebook.ipynb">
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<a href="https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/dinov2_notebook.ipynb">
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<a href="https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/dinov2_notebook.ipynb">
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