DeepDanbooru VS jina

Okay, it turns out that someone has trained an image classifier on danbooru as a dataset.

First of all - DeepDanbooru is the exclusive project of KichangKim, who aimed to train models based on (variants of) the ResNet architecture to output Danbooru tags.

> pip install -r requirements.txt Like what does this mean, where do i put the files i got from https://github.com/KichangKim/DeepDanbooru ? The whole process is confusing me

For: https://github.com/KichangKim/DeepDanbooru I see the "Put your deepbooru release project folder here.txt" file so I "git clone" it into this folder, moved it up and so on but the button did not show and in the console are no errors. Also I restarted the UI after each moving of the project.

if not is_installed("deepdanbooru") and deepdanbooru: run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")

i think you could make an argument that training an art-generator model falls afoul of the "market for the original work" prong of fair use in a pretty severe way. but something like DeepDanbooru where the output is not the image but a descriptor of it would be a much easier sell as fair use.

DeepDanbooru is suprisingly good for tagging photos and drawings.

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.