Kornia
laserembeddings
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Kornia | laserembeddings | |
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11 | 2 | |
9,364 | 223 | |
2.5% | - | |
9.4 | 0.0 | |
7 days ago | 9 months ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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.
Kornia
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[News] Kornia 0.6.6: ParametrizedLine API, load_image support for Apple Windows Developer, integration demos with Hugging Face and many more.
👉 https://github.com/kornia/kornia/releases/tag/v0.6.6
- [P] Kornia: Differential Computer Vision
- Kornia: Differential Computer Vision
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Hacker News top posts: May 10, 2022
Kornia: Differential Computer Vision\ (3 comments)
- Preprocessing for NN on GPU
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Top 5 Python libraries for Computer vision
Kornia - Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.
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[D] CPU choice for machine learning server (Epyc vs. Threadripper)
Between "not being sure yet" about GPU operations in pre-processing and choosing high-end CPUs, I think you are overthinking the wrong alternative. Besides DALI, check whether you are using codecs besides nvidia/torchvision-supported jpeg and png, and if other GPU CV libraries meet your needs: torchvision kornia
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[P] Using PyTorch + NumPy? A bug that plagues thousands of open-source ML projects.
Use kornia.augmentation where this problem is solved doing the augmentations in batch outside the dataloader. https://github.com/kornia/kornia
laserembeddings
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Firefox Translations doesn't use the cloud
You're pretty much right on the money. For ParaCrawl[1] (which I worked on) we used fast machine translation systems that were "good enough" to translate one side of each pair to the language of the other, see whether they'd match sufficiently, and then deal with all the false positives through various filtering methods. Other datasets I know of use multilingual sentence embeddings, like LASER[2], to compute the distance between two sentences.
Both of these methods have a bootstrapping problem, but at this point in the MT for many languages we have enough data to get started. Previous iterations of ParaCrawl used things like document structure and overlap of named entities among sentences to identify matching pairs. But this is much less robust. I don't know how they solve this problem today for low-resource languages.
[1] https://paracrawl.eu
[2] https://github.com/yannvgn/laserembeddings
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SpaCy v3.0 Released (Python Natural Language Processing)
I've been using LASER from Facebook Research via https://github.com/yannvgn/laserembeddings to accept multi-lingual input in front of the the domain-specific models for recommendations and stuff (that are trained on English annotated examples).
What are some alternatives?
OpenCV - Open Source Computer Vision Library
syntaxdot - Neural syntax annotator, supporting sequence labeling, lemmatization, and dependency parsing.
Face Recognition - The world's simplest facial recognition api for Python and the command line
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
BLINK - Entity Linker solution
SimpleCV - The Open Source Framework for Machine Vision
wiktextract - Wiktionary dump file parser and multilingual data extractor
multi-object-tracker - Multi-object trackers in Python
projects - 🪐 End-to-end NLP workflows from prototype to production
gaps - A Genetic Algorithm-Based Solver for Jigsaw Puzzles :cyclone:
duckling - Language, engine, and tooling for expressing, testing, and evaluating composable language rules on input strings.