image-crop-analysis VS google-research

Compare image-crop-analysis vs google-research and see what are their differences.


Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency (by twitter-research)
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image-crop-analysis google-research
2 66
241 27,040
3.3% 1.7%
0.0 9.8
over 1 year ago 6 days ago
Jupyter Notebook Jupyter Notebook
Apache License 2.0 Apache License 2.0
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.


Posts with mentions or reviews of image-crop-analysis. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning image-crop-analysis yet.
Tracking mentions began in Dec 2020.


Posts with mentions or reviews of google-research. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-01-12.

What are some alternatives?

When comparing image-crop-analysis and google-research you can also consider the following projects:

milvus - Vector database for scalable similarity search and AI applications.

qdrant - Qdrant - Vector Search Engine and Database for the next generation of AI applications. Also available in the cloud

fast-soft-sort - Fast Differentiable Sorting and Ranking

struct2depth - Models and examples built with TensorFlow

ML-KWS-for-MCU - Keyword spotting on Arm Cortex-M Microcontrollers

rmi - A learned index structure

faiss - A library for efficient similarity search and clustering of dense vectors.

torchsort - Fast, differentiable sorting and ranking in PyTorch

ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

CLIP - Contrastive Language-Image Pretraining


haystack - :mag: Haystack is an open source NLP framework that leverages pre-trained Transformer models. It enables developers to quickly implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications.