hachi

I work on an image search engine[0], main idea has been to preserve all the original meta-data and directory structure while allowing semantic and meta-data search from a single interface. All meta-data is stored in a single json file, with original Path and filenames, in case ever to create backups. Instead of uploading photos to a server, you could host it on a cheap VPS with enough space, and instead Index there. (by default it a local app). It is an engine though and don't provide any Auth or specific features like sharing albums! [0] https://github.com/eagledot/hachi

Interesting project, very dense post. I like the idea of a genuine personal search engine. You’d think that Windows and MacOS would do this well, but they really don’t. Project GitHub is here https://github.com/eagledot/hachi

for images readme is at: https://github.com/eagledot/hachi/tree/main/images/readme.md with more than enough details! It is supposed to be a search engine for all modalities, for now `images` are supported ! For demo, i don't have much resources to host it, would a video showcasing features help ? Also for windows there is a portable app at https://github.com/eagledot/hachi/releases/download/v1.3/hac...

> https://github.com/eagledot/hachi promising but i want to see more before i try it - could you invest a little in your readme to list out features, maybe do a little loom demo?

Disclaimer: I work on such a project[0] I think a combination of CLIP and some face-recognition may solve your issues! It just takes a path to your directory, and can index all the images while preserving your folder hierarchy along with a high quality face-clustering. Each image indexing takes about 100ms on a cpu. Every combination can then be mixed and matched, from a single interface, It doesn't take much to try as dependencies are very minimal . There is a self contained app for windows too. I have been looking for feedback, so just plugging it here in case some one has a use case. [0] https://github.com/eagledot/hachi

I think image-encoder from CLIP (even smallest variant ViT B/32) is good enough to capture a lot of semantic information to allow natural language query once images are indexed. A lot of work actually goes into integrating with existing meta-data like local-directory, date-time to augment NL query and re-ranking the results. I work on such a tool[0] to enable end to end indexing of user's personal photos and recently added functionality to index Google Photos too! [0] https://github.com/eagledot/hachi

We have been working on https://github.com/ramanlabs-in/hachi for some time. This ia a webapp that lets you seaech your videos and images locally. We really need feedback about ui/ux, features and usefullness of the webapp.

For what it is worth, we work on a tool[0] to index all local videos and images and later allowing query just using natural language. It is based on CLIP which has been trained on image-text pairs, but seems to work great for videos after applying some naive heuristics. [0] https://github.com/ramanlabs-in/hachi

I think, it is really underrated how much faster numpy approach is for low-key workloads like personal images and videos where you would generally have a few K embeddings and it is also possible to hide some latency by sharding and stream results as soon as a shard is done. We work on a similar project[0] to make it easy to index personal images and videos. Before using numpy, we also looked at libraries like Annoy to index the embeddings, but being approximate sometimes such libraries would leave out most-similiar image/frame. For personal data, we found it is better to depend on exact-search rather than an approximate one even if it comes with a speed tradeoff. [0]: https://github.com/ramanlabs-in/hachi

Interface really looks nice! We also develop a similar project but for indexing personal videos and images[0] based on CLIP. It is a Flask based application behind a caddy proxy. [0] https://github.com/ramanlabs-in/hachi

Hi, I am Anubhav from ramanlabs. We have been working on a self hosted solution to enable natural language search for videos and images. Idea is to allow users to Index all locally available images and videos just once, and then later query them using natural language. It doesn't depend on any deep-learning framework and should make installation easier and faster. All Indices are stored locally without no dependence on outside network. Semantic search is powered by CLIP architecture. We already have some features on the roadmap and are interested in hearing your feedback/thoughts. Github: <https://github.com/ramanlabs-in/hachi> Demo: <https://demo.ramanlabs.in>