image-similarity-measures
generative-evaluation-prdc
| image-similarity-measures | generative-evaluation-prdc | |
|---|---|---|
| 3 | 2 | |
| 518 | 234 | |
| 2.1% | 5.1% | |
| 4.4 | 0.0 | |
| 20 days ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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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.
generative-evaluation-prdc
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[D] Comparing the efficiency of different GAN models
Also you can easily add in some extra precision/recall mterics into that repo's evaluation script from this repo: https://github.com/clovaai/generative-evaluation-prdc
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[R] What are some up-to-date references to the evaluation of model fit across various types of generative models (GANs, flows, VAEs, diffusion, etc.)?
Code for https://arxiv.org/abs/2002.09797 found: https://github.com/clovaai/generative-evaluation-prdc
What are some alternatives?
ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
clean-fid - PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]
piqa - PyTorch Image Quality Assessement package
precision-recall-distributions - Assessing Generative Models via Precision and Recall (official repository)
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
FedScale - FedScale is a scalable and extensible open-source federated learning (FL) platform.
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
ALAE - [CVPR2020] Adversarial Latent Autoencoders
COMET - A Neural Framework for MT Evaluation
rexmex - A general purpose recommender metrics library for fair evaluation.
SDV - Synthetic data generation for tabular data