image-crop-analysis VS computervision-recipes

Compare image-crop-analysis vs computervision-recipes 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 computervision-recipes
2 1
241 8,821
3.3% 0.5%
0.0 5.3
over 1 year ago 8 days ago
Jupyter Notebook Jupyter Notebook
Apache License 2.0 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.


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 computervision-recipes. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning computervision-recipes yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing image-crop-analysis and computervision-recipes you can also consider the following projects:

VIAME - Video and Image Analytics for Multiple Environments

fastdup - FastDup is a tool for gaining insights from a large image collection. It can find anomalies, duplicate and near duplicate images, clusters of similaritity, learn the normal behavior and temporal interactions between images. It can be used for smart subsampling of a higher quality dataset, outlier removal, novelty detection of new information to be sent for tagging. FastDup scales to millions of images running on CPU only.

vqgan-clip-generator - Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

Face-Mask-Detection - Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras

One-Piece-Image-Classifier - A quick image classifier trained with manually selected One Piece images.

MLB-Pitch-Identification-with-ML - Identify the name of a pitch being thrown using a machine learning model.

smletsexchangeconnector - SMLets PowerShell based Exchange Connector for controlling Microsoft System Center Service Manager 2016+

sod - An Embedded Computer Vision & Machine Learning Library (CPU Optimized & IoT Capable)

synthetic-dataset-object-detection - How to Create Synthetic Dataset for Computer Vision (Object Detection) (Article on Medium)

Deep-Learning-Computer-Vision - My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.

opencv - Experimenting using Machine Vision OpenCV and Python to create software suitable for driving a Golf launch monitor similar to technology like SkyTrak, GC2 and GC Quad

neural-style-transfer - :paintbrush: This repository contains, well-structured Python library and runnable fully prepared Python notebook of the "Neural Style Transfer" algorithm