autoalbument
caer
Our great sponsors
| autoalbument | caer | |
|---|---|---|
| 1 | 8 | |
| 195 | 749 | |
| 2.6% | - | |
| 0.0 | 0.0 | |
| over 2 years ago | 7 months ago | |
| Python | Python | |
| MIT License | MIT 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.
autoalbument
-
[P] Albumentations 1.0 is released (a Python library for image augmentation)
We started to implement differentiable augmentations as a part of AutoAlbument (a tool that automatically searches for the best augmentation policies for your data) - https://github.com/albumentations-team/autoalbument, it is more a research project.
caer
- Show HN: Caer – A lightweight GPU-accelerated Vision library in Python
- I wrote a lightweight GPU-accelerated Vision library in Python
-
Jetson nano python3 illegal instruction problem
I think it may have. If you look at line 10 of https://github.com/jasmcaus/caer/blob/master/configs.ini, you’ll see that caer has numpy and opencv-contrib-python dependencies that get referenced in its setup.py. If I recall correctly, pip on the nano doesn’t pick up the default numpy and opencv-python system installs, so when you go to install something like caer that has them as dependencies, it will install new copies except the wheel files that it grabs are incompatible. The solution I have found to work is to run something similar to the command above: “pip3 install —no-binary caer —no-binary numpy—no-binary opencv-contrib-python —no-binary typing-extensions —no-binary mypy —force-reinstall caer”. Some of those —no-binary options may not be necessary but they’ll at least ensure pip grabs the source for each of the dependencies and rebuilds it locally rather than using an imcompatible version. This command will take awhile! But you only should have to do it once.
- jasmcaus/caer Modern Computer Vision on the Fly
- Caer: High-performance Vision Library in Python (faster than Torchvision)
- Caer – A GPU-accelerated Computer Vision library (faster than Torchvision)
- jasmcaus/caer lightweight, scalable Computer Vision library for high-performance AI research
- Caer – A GPU-Accelerated Computer Vision Library in Python
What are some alternatives?
albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
fiftyone - The open-source tool for building high-quality datasets and computer vision models
YOLO-Mosaic - Perform mosaic image augmentation on data for training a YOLO model
img2table - img2table is a table identification and extraction Python Library for PDF and images, based on OpenCV image processing
imgaug - Image augmentation for machine learning experiments.
opencv - Haskell binding to OpenCV-3.x
ttach - Image Test Time Augmentation with PyTorch!
Single-Image-Dehazing-Python - python implementation of the paper: "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization"
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
autovideo - AutoVideo: An Automated Video Action Recognition System
moviepy - Video editing with Python