image-similarity-measures
:chart_with_upwards_trend: Implementation of eight evaluation metrics to access the similarity between two images. The eight metrics are as follows: RMSE, PSNR, SSIM, ISSM, FSIM, SRE, SAM, and UIQ. (by up42)
OCTIS
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track) (by MIND-Lab)
| image-similarity-measures | OCTIS | |
|---|---|---|
| 3 | 7 | |
| 518 | 685 | |
| 2.1% | 1.0% | |
| 4.4 | 6.0 | |
| 20 days ago | 4 months ago | |
| Python | Python | |
| MIT License | 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.
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.
image-similarity-measures
Posts with mentions or reviews of image-similarity-measures.
We have used some of these posts to build our list of alternatives
and similar projects.
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Using VAE for image compression
Speaking of math, using this library -- https://github.com/up42/image-similarity-measures -- I computed the following for these images vs the original image:
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I matched 400+ images to create illusion of motion [epilepsy]
The easiest place to start is using the classical approaches such as implemented here. For the kind of qualitative assessments you're performing, you'd probably need to use some deep learning techniques but these generally require significant technical background to implement.
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I made a website that tracks Forsen's Jump King progress and can notify you above chosen percentage.
I use https://github.com/up42/image-similarity-measures for image similarity.
OCTIS
Posts with mentions or reviews of OCTIS.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-31.
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Interpretation of topic modeling results between LDA and BERTopic
OCTIS
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(NLP) Best practices for topic modeling and generating interesting topics?
My team and I have recently released a python library called OCTIS (https://github.com/mind-Lab/octis) that allows you to automatically optimize the hyperparameters of a topic model according to a given evaluation metric (not log-likelihood). I guess, in your case, you might be interested in topic coherence. So you will get good quality topics with a low effort on the choice of the hyperparameters. Also, we included some state-of-the-art topic models, e.g. contextualized topic models (https://github.com/MilaNLProc/contextualized-topic-models).
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I am working on a topic modelling paper and I need your help
I recently released a topic modeling library that also includes different evaluation measures. If you are interested, I leave here the link: https://github.com/mind-Lab/octis
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Latest trends in topic modelling?
Silvia Terragni (a coauthor on the above) also brought a topic modelling library OCTIS which was exhibited as a demo paper and aims to be the huggingface transformers of topic modelling - it includes wrappers around the above model as well as and LDA and some baselines as well as some tools and frameworks for comparing them.
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OCTIS a python framework to compare and optimize Topic Models
Link to the code Paper
- OCTIS, our new python framework to optimize and compare topic models has been accepted at EACL2021!
- [p] OCTIS: Optimizing and Comparing Topic models Is Simple. Our new python framework to compare and optimize topic models using Bayesian Optimization
What are some alternatives?
When comparing image-similarity-measures and OCTIS you can also consider the following projects:
ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
piqa - PyTorch Image Quality Assessement package
contextualized-topic-models - A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
auto-sklearn - Automated Machine Learning with scikit-learn
generative-evaluation-prdc - Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.
SMAC3 - SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
COMET - A Neural Framework for MT Evaluation
TopMost - A Topic Modeling System Toolkit
image-similarity-measures vs ignite
OCTIS vs BERTopic
image-similarity-measures vs piqa
OCTIS vs contextualized-topic-models
image-similarity-measures vs PyTorch-NLP
OCTIS vs auto-sklearn
image-similarity-measures vs generative-evaluation-prdc
OCTIS vs SMAC3
image-similarity-measures vs COMET
OCTIS vs TopMost