Queryable VS Awesome-CLIP

Compare Queryable vs Awesome-CLIP and see what are their differences.

Queryable

Run OpenAI's CLIP model on iOS to search photos. (by mazzzystar)

Awesome-CLIP

Awesome list for research on CLIP (Contrastive Language-Image Pre-Training). (by yzhuoning)
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Queryable Awesome-CLIP
5 2
2,424 1,019
- -
7.9 0.0
15 days ago 9 months ago
Swift
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Queryable

Posts with mentions or reviews of Queryable. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-13.

Awesome-CLIP

Posts with mentions or reviews of Awesome-CLIP. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-03.

What are some alternatives?

When comparing Queryable and Awesome-CLIP you can also consider the following projects:

clip-retrieval - Easily compute clip embeddings and build a clip retrieval system with them

awesome-pretrained-stylegan2 - A collection of pre-trained StyleGAN 2 models to download

natural-language-image-search - Search photos on Unsplash using natural language

Awesome-Text-to-Image - (ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.

aphantasia - CLIP + FFT/DWT/RGB = text to image/video

dl-colab-notebooks - Try out deep learning models online on Google Colab

natural-language-youtube-search - Search inside YouTube videos using natural language

TediGAN - [CVPR 2021] Pytorch implementation for TediGAN: Text-Guided Diverse Face Image Generation and Manipulation. [Moved to: https://github.com/IIGROUP/TediGAN]

MoTIS - [NAACL 2022]Mobile Text-to-Image search powered by multimodal semantic representation models(e.g., OpenAI's CLIP)

Puddles - A native SwiftUI app architecture

Chinese-CLIP - Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.

bark.cpp - Port of Suno AI's Bark in C/C++ for fast inference