# Machine Learning SOTA and Model Zoo

## List of SOTA

* [State of the Art AI](https://www.stateoftheart.ai/)
* [Gradio Hub](https://gradiohub.com/)
  * Sketch Recognition
  * Painting Generator (StyleGAN)
  * Pose Estimation
  * Colorize Photos (DeOldify)
  * Identifying Skin Cancer
  * Emotion Classification
  * MobileNet vs. InceptionNet
* [Papers with Code SOTA](https://paperswithcode.com/sota)
* [AI Hub by Google](https://aihub.cloud.google.com/s?category=notebook)

## Model Zoo

* [Model Zoo](https://modelzoo.dev/) Machine Learning Playground
* [Cloud Blobcity](https://cloud.blobcity.com/#/ps/explore) : GitHub Data Science projects repository, executable in 1 click
* [CKnowledge](https://cknowledge.io/) Find portable workflows, reusable artifacts and automation actions for deep tech (AI, ML, quantum, systems...), ML architecture comparison
* [Gradio](https://github.com/gradio-app/gradio)
* [Gradio Spaces](https://huggingface.co/spaces)
* [black0017/MedicalZooPytorch: A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation](https://github.com/black0017/MedicalZooPytorch)
* [CatalyzeX](https://www.catalyzex.com/) : Discover AI models & code to catalyze your projects

## Report about AI Progress

* [State of AI](https://www.stateof.ai/)
* [AI Index](https://hai.stanford.edu/research/ai-index-2019)
* [EFF AI Metrics](https://www.eff.org/ai/metrics)

## Specific SOTA

* [SOTA for Music Separation](https://paperswithcode.com/sota/music-source-separation-on-musdb18)
* [State-of-the-Art Image Generative Models–Aran Komatsuzaki](https://arankomatsuzaki.wordpress.com/2021/03/04/state-of-the-art-image-generative-models/)

## Machine Learning Review

* [Best deep CNN architectures and their principles: from AlexNet to EfficientNet - AI Summer](https://theaisummer.com/cnn-architectures/)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://irosyadi.gitbook.io/irosyadi/machine-learning/machine-learning-sota.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.