imedslab/solt
Streaming over lightweight data transformations
This helps deep learning researchers and practitioners enhance their image datasets by applying various transformations like rotation, cropping, and noise. It takes in original images, along with optional segmentation masks, labels, or keypoints, and outputs augmented versions of these data points. This is used by anyone training deep learning models who needs to expand their dataset to improve model robustness and performance.
266 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need a fast and flexible way to augment diverse image datasets for deep learning, including medical images and those with keypoints or segmentation masks.
Not ideal if your task does not involve image-based deep learning or if you need to perform data transformations on non-visual data types.
Stars
266
Forks
20
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jun 01, 2025
Commits (30d)
0
Dependencies
6
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/imedslab/solt"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
TorchIO-project/torchio
Medical imaging processing for AI applications.
aleju/imgaug
Image augmentation for machine learning experiments.
makcedward/nlpaug
Data augmentation for NLP
mdbloice/Augmentor
Image augmentation library in Python for machine learning.
BloodAxe/pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming