dhash
fastdup
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| dhash | fastdup | |
|---|---|---|
| 2 | 18 | |
| 329 | 1,403 | |
| - | 3.6% | |
| 1.8 | 9.4 | |
| over 1 year ago | 25 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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dhash
fastdup
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Visualize your dataset using DINOv2 embedding
Visualizing your dataset (especially large ones) in a low-dimensional embedding space can tell you a lot about the patterns and clusters in your dataset.
We recently release a notebook showing how you can visualize your dataset using DINOv2 models by running it on your CPU.
Yes! No GPUs needed.
We used it to find clusters of similar images, duplicates, and outliers in a subset of the LAION dataset
Try it on your own dataset:
Colab notebook - https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/dinov2_notebook.ipynb
GitHub repo - https://github.com/visual-layer/fastdup
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[R][P] How to extract feature vectors of large datasets using DINOv2 on CPU
Run 1M images from the LAION dataset through the DINOv2 model and cluster the images using a free tool - fastdup.
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Computer Vision pre-trained model for finding how similar two photos of a room are
Another option could be fastdup (https://github.com/visual-layer/fastdup) which is probably most helpful for analysis type objectives.
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Find image duplicates and outliers – A free, scalable, efficient tool
I recently stumbled upon fastdup a tool that lets you gain insights from a large image/video collection.
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How can we match images in our database?
There is this fastdup framework which supposedly allows you to find duplicates and similar images. i haven't used it though
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Measure Images Similarity
I came across fastdup recently https://github.com/visual-layer/fastdup
- Dedup-ing LAION (60M duplicates) and ImageNet (1.2M duplicates) with fastdup
- [R] Dedup-ing LAION (60M duplicates) and ImageNet (1.2M duplicates) with fastdup
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