fastdup VS jina

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

Jina mainly because of their use of neural networks and AI.

Jina - It’s an open-source project where you can build search engines. Well maybe not no code but it claims that you only need a few lines of code for creating projects. The project supports semantic, text, image, audio, and video search. What I’m also interested in is with their neural search and generative AI. I’m also interested in the amount of github repo that they have. I have this on my radar since this is also something I was interested in.

Do you guys have any ideas how we can match images on our database? We’re working on a project that about matching images on our database. We were trying to use SIFT and some other similar methods, but for some reason, nothing doesn’t seem to be working that well. Does anyone have any suggestions for the most effective way to do this? Maybe some open-source solutions like HuggingFace or Jina AI? We just want to make sure our image matching is correct and that part’s been a bit of a struggle on our part.

The tech lets you find 3D models without sifting through tons of text - An information retrieval framework does the heavy lifting and compares models to each other, no descriptions or keywords needed.

Jina AI -They offer a neural search solution that can help build smarter, more efficient search engines. They also have a list of cool github repos that you can check out. Similar to Vertex AI, they have image classification tools, NLPs, fine tuners etc.

Well, it's not exactly a new feature, but we've been working on early support for DocArray v2 in Jina.

Jina let’s you serve all of your models through the same Gateway while deploying them as individual microservices. You can also tie your models together in a pipeline if needed. Also some nice ML focussed features such as dynamic batching.