research-papers VS fastdup

See links 1. https://youtu.be/w--c0qG9MCc 2. https://github.com/TessFerrandez/research-papers/tree/prod/facenet

I think a good architecture would be a triplet network like the one described in the Facenet paper. Tess Ferrandez has an implementation on GitHub. I believe the model works well with small dataset and works even better if you have pre-trained weights (I believe the GitHub link has a small network pre-trained weights). You can also look up Siamese networks and Quadruplet networks

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

Run 1M images from the LAION dataset through the DINOv2 model and cluster the images using a free tool - fastdup.

Another option could be fastdup (https://github.com/visual-layer/fastdup) which is probably most helpful for analysis type objectives.

I recently stumbled upon fastdup a tool that lets you gain insights from a large image/video collection.

There is this fastdup framework which supposedly allows you to find duplicates and similar images. i haven't used it though

I came across fastdup recently https://github.com/visual-layer/fastdup