n2d
similarity
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| n2d | similarity | |
|---|---|---|
| 1 | 7 | |
| 122 | 994 | |
| - | 0.2% | |
| 0.0 | 6.5 | |
| 6 months ago | 26 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | 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.
n2d
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Time-Series image clustering. Advice needed!
So far I found some approaches that looks promising, for example n2d or k-means with DTW distance, and there are some more (e.g. T-DPSOM), but I want to start from these.
similarity
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New free tool that uses fine-tuned BERT model to surface answers from research papers
Tensorflow Ranking and Tensorflow similarity (maybe relevant/irrelevant contrastive learning?) look like they could be useful.
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Non-Machine Learning Image Matching with a Vector DB
There is the metric learning problem to learn a hash for similarity https://github.com/tensorflow/similarity
That said, I don't see many good models available for download on tfhub or huggingface optimized for it, but you can always programmatically modify your images (if you truly mean identical to humans) - change white balance, crop, rotate, select adjacent frames from videos, etc. and optimize a network that is small enough for you to be satisfied and see if that works, as a possible alternative.
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Face Detection for 520 People
Metric learning has great implementations inside Tensorflow Similarity library: https://github.com/tensorflow/similarity Although the documentation is quite bad, but the jupyter notebooks are great.
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[P] TensorFlow Similarity 0.16 is out
Just a quick note that TensorFlow Similarity 0.16 is out -- this release beside adding the XMB loss is mostly focus on refactoring and optimizing the core components to ensure everything works smoothly and accurately. Details are in the changelog as usual and a simple pip install -U tensorflow_similarity should just work.
- Self-supervised learning added to TensorFlow Similarity
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[P] TensorFlow Similarity now self-supervised training
Very happy to announce that as part of the 0.15 release, TensorFlow Similarity now support self-supervised learning using STOA algorithms. To help you get started we included in the release a detailed getting started notebook that you can run in Colab. This notebook shows you how to use SimSiam self-supervised pre-training to almost double the accuracy compared to a model trained from scratch on CIFAR 10.
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TensorFlow Introduces ‘TensorFlow Similarity’, An Easy And Fast Python Package To Train Similarity Models Using TensorFlow
Github: https://github.com/tensorflow/similarity
What are some alternatives?
minisom - :red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
pgANN - Fast Approximate Nearest Neighbor (ANN) searches with a PostgreSQL database.
quaterion - Blazing fast framework for fine-tuning similarity learning models
ContraD - Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
sparse_dot_topn - Python package to accelerate the sparse matrix multiplication and top-n similarity selection
Keras - Deep Learning for humans
finetuner - :dart: Task-oriented embedding tuning for BERT, CLIP, etc.
ColBERT - ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22, ACL'23, EMNLP'23)
awesome-semantic-search - A curated list of awesome resources related to Semantic Search🔎 and Semantic Similarity tasks.
elasticsearch-learning-to-rank - Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch