image-similarity-measures
ignite
| image-similarity-measures | ignite | |
|---|---|---|
| 3 | 3 | |
| 518 | 4,458 | |
| 2.1% | 0.4% | |
| 4.4 | 8.7 | |
| 20 days ago | 5 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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.
ignite
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Introducing PyTorch-Ignite's Code Generator v0.2.0
Along with the PyTorch-Ignite 0.4.5 release, we are excited to announce the new release of the web application for generating PyTorch-Ignite's training pipelines. This blog post is an overview of the key features and updates of the Code Generator v0.2.0 project release.
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Distributed Training Made Easy with PyTorch-Ignite
PyTorch-Ignite's ignite.distributed (idist) submodule introduced in version v0.4.0 (July 2020) quickly turns single-process code into its data distributed version.
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Introduction to PyTorch-Ignite
More details about distributed helpers provided by PyTorch-Ignite can be found in the documentation. A complete example of training on CIFAR10 can be found here.
What are some alternatives?
piqa - PyTorch Image Quality Assessement package
torch-metrics - Metrics for model evaluation in pytorch
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
prometheus_flask_exporter - Prometheus exporter for Flask applications
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
pymetrix - A simple Plug and Play Library for getting analytics. See website for docs.
generative-evaluation-prdc - Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.
xla - Enabling PyTorch on XLA Devices (e.g. Google TPU)
COMET - A Neural Framework for MT Evaluation
code-generator - Web Application to generate your training scripts with PyTorch Ignite
gloo - Collective communications library with various primitives for multi-machine training.
idist-snippets