quaterion
awesome-metric-learning
quaterion | awesome-metric-learning | |
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4 | 3 | |
626 | 433 | |
2.6% | 0.5% | |
2.3 | 1.8 | |
about 1 month ago | about 1 year ago | |
Python | ||
Apache License 2.0 | Creative Commons Zero v1.0 Universal |
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quaterion
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Similarity Learning lacks a framework. So we built one
PML is a great collection of implementations, but not the best framework. Also you can use PML with Quaterion: https://github.com/qdrant/quaterion/blob/master/examples/tra...
- Show HN: Quaterion – x100 faster fine-tuning of similarity learning models
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[N] Quaterion, a blazingly fast framework for similarity learning.
Just released. Quaterion — an open source framework for training and fine-tuning similarity learning models. It enables you to train models significantly (100x) faster, and iterate over experiments in minutes instead of hours even with a laptop GPU. It takes advantage of the PyTorch Lightning backend to make a flexible and scalable learning pipeline. GitHub https://github.com/qdrant/quaterion
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Introducing the Quaterion: a framework for fine-tuning similarity learning models
Quaterion on GitHub: https://github.com/qdrant/quaterion
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?
lightning-flash - Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains
awesome-TS-anomaly-detection - List of tools & datasets for anomaly detection on time-series data.
similarity - TensorFlow Similarity is a python package focused on making similarity learning quick and easy.
build-your-own-x - Master programming by recreating your favorite technologies from scratch.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
finetuner - :dart: Task-oriented embedding tuning for BERT, CLIP, etc.
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
kervolution - Kervolution Library in PyTorch (CVPR 2019 Oral)
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).
CEBRA - Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
Milvus - A cloud-native vector database, storage for next generation AI applications