pyRDF2Vec
lightly
pyRDF2Vec | lightly | |
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1 | 16 | |
240 | 2,750 | |
0.8% | 1.1% | |
2.9 | 8.8 | |
11 days ago | 7 days ago | |
Python | Python | |
MIT License | MIT License |
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pyRDF2Vec
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[P] pyRDF2Vec 0.2.0 is out!
This release is packed with many new features and optimizations under the hood. An entire overview of what's new can be found in our CHANGELOG (https://github.com/IBCNServices/pyRDF2Vec/releases/tag/0.2.0). An overview of some major updates:
lightly
- Show HN: Lightly โ 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 weโve seen exciting progress in research around self-supervised learning in computer vision. Weโre 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 youโre 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 โ 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.
What are some alternatives?
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gensim - Topic Modelling for Humans
simsiam-cifar10 - Code to train the SimSiam model on cifar10 using PyTorch
scattertext - Beautiful visualizations of how language differs among document types.
byol - Implementation of the BYOL paper.
jRDF2Vec - A high-performance Java Implementation of RDF2Vec
comma10k - 10k crowdsourced images for training segnets
hub - A library for transfer learning by reusing parts of TensorFlow models.
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
YassQueenDB - Graph database library that allows you to store, analyze, and search through your data in a graph format. By using the Universal Sentence Encoder, it provides an efficient and semantic approach to handle text data. ๐๐ง ๐
byol-pytorch - Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch