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Merge pull request #200 from dnth/dnth/update-readme
Readme Updates
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‎README.md

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🔥 We've released
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<a href="./RELEASE_NOTES.md">fastdup V1.0</a> and <a href="https://techcrunch.com/2023/05/16/visual-layer-helps-enterprise-manage-the-massive-visual-data-sets-they-need-to-build-ai-models-raises-7m/">featured</a> in Techrunch, raising $7M!
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🔥 We released
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<a href="https://medium.com/@visual-layer/fastdup-one-year-strong-and-still-going-adb382a536d3">fastdup V1.0</a> and were <a href="https://techcrunch.com/2023/05/16/visual-layer-helps-enterprise-manage-the-massive-visual-data-sets-they-need-to-build-ai-models-raises-7m/">featured</a> in Techrunch, raising $7M!
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<a href="https://visual-layer.readme.io/docs/getting-started">
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<b>Quick Dataset Analysis:</b> In this example, learn how to quickly analyze a dataset for potential issues. Identify duplicates, outliers, dark/bright/blurry images, and cluster similar images with only a few lines of code. If you're new, start here.
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<a href="https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/quick-dataset-analysis.ipynb">
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<img src="./gallery/kaggle_logo.png" height="32">
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<a href="https://visual-layer.readme.io/docs/getting-started">
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<b>DINOv2 Embeddings:</b> In this example, learn how to use DINOv2 models to visualize image embeddings of your dataset. Runs on CPU!
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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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<img src="./gallery/kaggle_logo.png" height="32">
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<a href="https://visual-layer.readme.io/docs/cleaning-image-dataset">
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<b>Cleaning Image Dataset:</b> In this tutorial, learn how to clean a dataset from broken images, duplicates, outliers, and identify dark/bright/blurry images.
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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>Analyzing Labeled Image Classification Dataset:</b> In this tutorial, learn how to analyze a labeled image classification dataset for potential issues. We use the Imagenette dataset, a 10-class, 13k image subset of ImageNet as a working example.
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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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<b>Analyzing Labeled Object Detection Dataset:</b> In this tutorial learn how to load and analyze an object detection dataset with labeled bounding boxes and classes. We use the mini-coco dataset as a working example. Learn how to discover duplicates, outliers, and possible mislabeled bounding boxes.
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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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<b>Face Detection Video Analysis:</b> In this tutorial, learn how to use fastdup with a face detection model to detect and crop from videos. Following that we analyze the cropped faces for issues such as duplicates, near-duplicates, outliers, bright/dark/blurry faces.
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<a href="https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/video-face-detection.ipynb">
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<b>YOLOv5 Object Detection Video Analysis:</b> In this tutorial, learn how to use fastdup with a pre-trained yolov5 object detection model to detect and crop from videos. Following that we analyze the cropped objects for issues such as duplicates, near-duplicates, outliers, bright/dark/blurry objects.
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<b>Satellite Image Analysis:</b> In this tutorial, learn how to use fastdup to load 16-bit grayscale satellite image, work with rotated bounding boxes, understand your dataset, find issues with the data and check the quality of annotations.
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<b>Surveillance Camera Analysis:</b> In this tutorial, learn how to use fastdup to analyze surveillance camera videos, caption the activity inside the videos and detect indoor/ outdoor.
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<b>Image Search:</b> In this tutorial, learn how to use fastdup to search through large image datasets for duplicates/similar images using a query image. Runs on CPU!
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<b>Feature vectors:</b> In this tutorial, learn how to read fastdup generated feature vectors in Python and use them for downstream processing, or run fastdup on your calculated feature vectors.
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## Getting Help

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