pretty-print-confusion-matrix
modAL
Our great sponsors
| pretty-print-confusion-matrix | modAL | |
|---|---|---|
| 1 | 4 | |
| 507 | 2,140 | |
| - | 1.4% | |
| 0.0 | 1.9 | |
| over 1 year ago | 2 months ago | |
| Python | Python | |
| Apache License 2.0 | 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.
pretty-print-confusion-matrix
-
Trying to understand an Index Error Message
I am attempting to use the pretty-print confusion-matrix library to create a confusion matrix.
modAL
-
modAL VS encord-active - a user suggested alternative
2 projects | 12 Apr 2023
- What are frameworks/tools used for Human-In-The-Loop (active) learning ?
-
Launch HN: Lightly (YC S21): Label only the data which improves your ML model
How does it differentiate from modAL?
https://github.com/modAL-python/modAL
- Active Learning Using Detectron2
What are some alternatives?
pycm - Multi-class confusion matrix library in Python
active_learning - Code for Active Learning at The ImageNet Scale. This repository implements many popular active learning algorithms and allows training with torch's DDP.
magnitude - A fast, efficient universal vector embedding utility package.
GPflowOpt - Bayesian Optimization using GPflow
tslearn - The machine learning toolkit for time series analysis in Python
paramonte - ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.
lightly - A python library for self-supervised learning on images.
igel - a delightful machine learning tool that allows you to train, test, and use models without writing code
baybe - Bayesian Optimization and Design of Experiments
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
Encord Active - Open source active learning toolkit to find failure modes in your computer vision models, prioritize data to label next, and drive data curation to improve model performance.