pytorch-metric-learning
awesome-metric-learning
pytorch-metric-learning | awesome-metric-learning | |
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3 | 3 | |
5,770 | 433 | |
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7.9 | 1.8 | |
about 1 month ago | about 1 year ago | |
Python | ||
MIT License | Creative Commons Zero v1.0 Universal |
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pytorch-metric-learning
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Similarity Learning lacks a framework. So we built one
Not a full featured framework, but pytorch-metric-learning has data loaders, lossess, etc. to facilitate similarity learning: https://github.com/KevinMusgrave/pytorch-metric-learning
Disclaimer: I've made some contributions to it.
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[R][D] VAE Embedding Space - Can we force it to learn a metric?
You can use the triplet loss together with the Gaussian prior. It will be zero centered though and the clusters are not as separated when you use the triplet loss only.There are many alternative to the triplet loss, in case it needs to be a metric: https://github.com/KevinMusgrave/pytorch-metric-learning
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[D] Similar Image Retrieval
This repo provides the tools and examples needed to build such a model: https://github.com/KevinMusgrave/pytorch-metric-learning
awesome-metric-learning
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Create Your Own Custom Plugins for ChatGPT 🎉 Browse the Web, Execute Code, Use APIs 🛠️
And, for resources on similarity learning at large, you may want to check out this annotated list: https://github.com/qdrant/awesome-metric-learning
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Similarity Learning lacks a framework. So we built one
Some loss functions such as ArcFace loss and CosFace loss enforce the encoder model to organize their latent space in such a way that categories are placed with an angular margin from one another. Thus the model implicitly learns a continuous distance function.
Fun fact, one of the examples in Quaterion is for similar cars search.
If you find this topic and want to discover more, we collected a bunch of resources that might be helpful. https://github.com/qdrant/awesome-metric-learning
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Awesome Metric Learning!
The Metric Learning approach to data science problems is heavily underutilized. There are a lot of academic papers around it but much fewer practical guides and tutorials. So we decided that we could help people adopt metric learning by collecting related materials in one place. We are publishing a curated list of awesome practical metric learning tools, libraries, and materials - https://github.com/qdrant/awesome-metric-learning This collection aims to put together references to all required materials for building your application using Metric Learning. It is open-source, PR's are more than welcome!
What are some alternatives?
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
awesome-TS-anomaly-detection - List of tools & datasets for anomaly detection on time-series data.
lightly - A python library for self-supervised learning on images.
build-your-own-x - Master programming by recreating your favorite technologies from scratch.
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
finetuner - :dart: Task-oriented embedding tuning for BERT, CLIP, etc.
byol-pytorch - Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
autogluon - Fast and Accurate ML in 3 Lines of Code
contract-discovery - Data and additional information regarding the paper: Contract Discovery. Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines (to appear in Findings of EMNLP).
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
quaterion - Blazing fast framework for fine-tuning similarity learning models