conformal_classification
glasses
| conformal_classification | glasses | |
|---|---|---|
| 2 | 2 | |
| 211 | 413 | |
| - | - | |
| 0.0 | 1.8 | |
| over 1 year ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| 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.
conformal_classification
-
[P] 🚀 AWS launches Fortuna, an open-source library for Uncertainty Quantification
What is the best end-to-end example showing it? https://github.com/awslabs/fortuna/blob/main/examples/mnist_classification.ipynb ? It would be nice to have some visual explainer, as in https://github.com/aangelopoulos/conformal_classification .
-
[R] Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification - Link to a free online lecture by the author in comments
​Uncertainty Sets for Image Classifiers using Conformal Prediction https://arxiv.org/abs/2009.14193 https://github.com/aangelopoulos/conformal_classification
glasses
-
Are Open-sourced Implementations Sometimes Over-engineered?
Yes, they are. Take with a grain of salt, but researchers (usually) do not know how to code and (or) they don't care to properly share their work. Things that are learned in the first Computer Science bachelor year, like OOP, DRY, packages, good variables/function naming, are apparently not used in ml research. This is why I created my own library (https://github.com/FrancescoSaverioZuppichini/glasses), for me, good code means less time I have to spend working and more free time.
-
[N] Facebook announced a new AI open-source called DeiT (A new technique to train computer vision models)
I have implemented most of the sota models in my library (https://github.com/FrancescoSaverioZuppichini/glasses). These are my 2 cents:
What are some alternatives?
learnopencv - Learn OpenCV : C++ and Python Examples
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
AI-For-Beginners - 12 Weeks, 24 Lessons, AI for All!
monodepth2 - [ICCV 2019] Monocular depth estimation from a single image
DeepLearning - Contains all my works, references for deep learning
One-Piece-Image-Classifier - A quick image classifier trained with manually selected One Piece images.
geomstats - Computations and statistics on manifolds with geometric structures.
gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
Deep-Learning-Experiments - Videos, notes and experiments to understand deep learning
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
edward2 - A simple probabilistic programming language.
HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision