image-similarity-measures
COMET
image-similarity-measures | COMET | |
---|---|---|
3 | 3 | |
518 | 401 | |
2.1% | 3.7% | |
4.4 | 7.7 | |
20 days ago | 5 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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
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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.
COMET
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Benchmarking of OpenAI GPT-3 VS other proprietary APIs (details in dev.to/samyme article)
It's definitely a hard task to evaluate. I think we can use models like https://github.com/Unbabel/COMET for translation to try and mimic human evaluation. I don't know if datasets exist for that. There are some research done about that : https://aclanthology.org/P19-1502/ https://arxiv.org/abs/2104.00054v1
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OpenAI GPT-3 vs Other Models [Benchmark] - Should AI companies be really worried ?
2/ Evaluation We compare Open AI to DeepL, ModernMT, NeuralSpace, Amazon and Google. A lot of metrics exist for automatic machine translation evaluation. We chose COMET by Unbabel (wmt21-comet-da) which is based on a machine learning model trained to get state-of-the-art levels of correlation with human judgements. (read more on their paper ) .
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What does the output of COMET metric really mean ?
I'm trying to understand how I can use COMET to evaluate translation models https://github.com/Unbabel/COMET ? I don't really understand how it was trained the meaning of the outputed values ? https://unbabel.github.io/COMET/html/faqs.html#which-comet-model-should-i-use
What are some alternatives?
ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
edenai-apis - Eden AI: simplify the use and deployment of AI technologies by providing a unique API that connects to the best possible AI engines
piqa - PyTorch Image Quality Assessement package
Tatoeba-Challenge
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
AutomaticKeyphraseExtraction - Data for Automatic Keyphrase Extraction Task
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
generative-evaluation-prdc - Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python