lightly
similarity
lightly | similarity | |
---|---|---|
16 | 7 | |
2,750 | 996 | |
1.1% | 0.0% | |
8.8 | 6.5 | |
7 days ago | 29 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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lightly
- Show HN: Lightly – A Python library for self-supervised learning on images
- GitHub - lightly-ai/lightly: A python library for self-supervised learning on images.
- A Python library for self-supervised learning on images
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[P] Release of lightly 1.2.39 - A python library for self-supervised learning
Another year of has passed, and we’ve seen exciting progress in research around self-supervised learning in computer vision. We’re very excited that some of the recent models such as Masked Autoencoders (MAE) or Masked Siamese Networks (MSN) have been added to our OSS framework.
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Self-Supervised Models are More Robust and Fair
If you’re interested in self-supervised learning and want to try it out yourself you can check out our open-source repository for self-supervised learning.
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[D] Can a Siamese Neural Network work for invoice classification?
I assume that you have an image of the invoice. Then using a framework like https://github.com/lightly-ai/lightly with many implemented algorithms is the way to go. And after that step, with model-producing embeddings, you need to compare the embedding of a query with your known database and check if the distance is below some threshold. Of course, pipeline with checking the closest neighbor can be more complicated but I would start with sth really simple.
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[P] TensorFlow Similarity now self-supervised training
https://github.com/lightly-ai/lightly implements a lot of self supervised models, and had been available for a while.
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Launch HN: Lightly (YC S21): Label only the data which improves your ML model
modAL indeed has a similar goal of choosing the best subset of data to be labeled. However it has some notable differences:
modAL is built on scikit-learn which is also evident from the suggested workflow. Lightly on the other hand was specifically built for deep learning applications supporting active learning for classification but also object detection and semantic segmentation.
modAL provides uncertainty-based active learning. However, it has been shown that uncertainty-based AL fails at batch-wise AL for vision datasets and CNNs, see https://arxiv.org/abs/1708.00489. Furthermore it only works with an initially trained model and thus labeled dataset. Lightly offers self-supervised learning to learn high dimensional embeddings through its open-source package https://github.com/lightly-ai/lightly. They can be used through our API to choose a diverse subset. Optionally, this sampling can be combined with uncertainty-based AL.
- Lightly – A Python library for self-supervised learning on images
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Active Learning using Detectron2
You can easily train, embed, and upload a dataset using the lightly Python package. First, we need to install the package. We recommend using pip for this. Make sure you're in a Python3.6+ environment. If you're on Windows you should create a conda environment.
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?
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
pgANN - Fast Approximate Nearest Neighbor (ANN) searches with a PostgreSQL database.
byol - Implementation of the BYOL paper.
quaterion - Blazing fast framework for fine-tuning similarity learning models
comma10k - 10k crowdsourced images for training segnets
ContraD - Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
byol-pytorch - Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch
sparse_dot_topn - Python package to accelerate the sparse matrix multiplication and top-n similarity selection