updated README and fixed typos in guide

README.rst

@@ -1,16 +1,92 @@
-=======
-Foolbox
-=======
+.. raw:: html
 
-We are in the process of creating Foolbox 3.0. For now, please continue with the `README of Foolbox 2.0 <https://github.com/bethgelab/foolbox/tree/v2>`_.
+   <a href="https://foolbox.jonasrauber.de"><img src="https://raw.githubusercontent.com/bethgelab/foolbox/master/guide/.vuepress/public/logo.png" align="right" /></a>
 
-Overview
---------
+.. image:: https://badge.fury.io/py/foolbox.svg
+   :target: https://badge.fury.io/py/foolbox
 
-.. image:: https://raw.githubusercontent.com/bethgelab/foolbox/master/overview.png
+.. image:: https://codecov.io/gh/bethgelab/foolbox/branch/master/graph/badge.svg
+   :target: https://codecov.io/gh/bethgelab/foolbox
 
+.. image:: https://readthedocs.org/projects/foolbox/badge/?version=latest
+    :target: https://foolbox.readthedocs.io/en/latest/
 
-Tutorial
---------
+.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
+   :target: https://github.com/ambv/black
 
-`Tutorial <https://github.com/jonasrauber/foolbox-native-tutorial/blob/master/foolbox-native-tutorial.ipynb>`_
+=========================================================================================================================
+Foolbox Native: A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
+=========================================================================================================================
+
+`Foolbox <https://foolbox.jonasrauber.de>`_ is a **Python library** that let's you easily run adversarial attacks against machine learning models like deep neural networks. It is built on top of `EagerPy <https://eagerpy.jonasrauber.de>`_ and works natively with models in `PyTorch <https://pytorch.org>`_, `TensorFlow <https://www.tensorflow.org>`_, `JAX <https://github.com/google/jax>`_, and `NumPy <https://numpy.org>`_.
+
+🔥 Design 
+----------
+
+`Foolbox 3 a.k.a. Foolbox Native has been rewritten from scratch
+using `EagerPy <https://github.com/jonasrauber/eagerpy>`_ instead of
+NumPy to achieve native performance on models
+developed in PyTorch, TensorFlow and JAX, all with one code base.
+
+- **Native Performance**: Foolbox 3 is built on top of EagerPy and runs natively in PyTorch, TensorFlow, JAX, and NumPyand comes with real batch support.
+- **State-of-the-art attacks**: Foolbox provides a large collection of state-of-the-art gradient-based and decision-based adversarial attacks.
+- **Type Checking**: Catch bugs before running your code thanks to extensive type annotations in Foolbox.
+
+📖 Documentation
+-----------------
+
+The best place to get started with Foolbox is the official `guide <https://foolbox.jonasrauber.de>`_.
+If you are looking for a tutorial, check out this `Jupyter notebook <https://github.com/jonasrauber/foolbox-native-tutorial/blob/master/foolbox-native-tutorial.ipynb>`_.
+Finally, you can find the full API documentation on `ReadTheDocs <https://foolbox.readthedocs.io/en/stable/>`_.
+
+🚀 Quickstart
+--------------
+
+.. code-block:: bash
+
+   pip install foolbox
+
+
+🎉 Example
+-----------
+
+.. code-block:: python
+
+   import foolbox as fb
+
+   model = ...
+   fmodel = fb.PyTorchModel(model)
+
+   attack = fb.attacks.LinfPGD()
+   epsilons = [0.0, 0.001, 0.01, 0.03, 0.1, 0.3, 0.5, 1.0]
+   _, advs, success = attack(fmodel, images, labels, epsilons=epsilons)
+
+
+More examples can be found in the `examples <./examples/>`_ folder, e.g.
+a full `ResNet-18 example <./examples/pytorch_resnet18.py>`_.
+
+📄 Citation
+------------
+
+If you use Foolbox for your work, please cite our `paper <https://arxiv.org/abs/1707.04131>`_ using the this BibTex entry:
+
+.. code-block::
+
+   @inproceedings{rauber2017foolbox,
+     title={Foolbox: A Python toolbox to benchmark the robustness of machine learning models},
+     author={Rauber, Jonas and Brendel, Wieland and Bethge, Matthias},
+     booktitle={Reliable Machine Learning in the Wild Workshop, 34th International Conference on Machine Learning},
+     year={2017},
+     url={http://arxiv.org/abs/1707.04131},
+   }
+
+
+🐍 Compatibility
+-----------------
+
+We currently test with the following versions:
+
+* PyTorch 1.4.0
+* TensorFlow 2.1.0
+* JAX 0.1.57
+* NumPy 1.18.1

guide/README.md

@@ -18,7 +18,7 @@ footer: Copyright © 2020 Jonas Rauber
 
 ### What is Foolbox?
 
-**Foolbox** is a **Python library** that let's easily run adversarial attacks against machine learning models like deep neural networks. It is built on top of [**EagerPy**](https://eagerpy.jonasrauber.de) and works natively with models in [**PyTorch**](https://pytorch.org), [**TensorFlow**](https://www.tensorflow.org), [**JAX**](https://github.com/google/jax), and [**NumPy**](https://numpy.org).
+**Foolbox** is a **Python library** that let's you easily run adversarial attacks against machine learning models like deep neural networks. It is built on top of [**EagerPy**](https://eagerpy.jonasrauber.de) and works natively with models in [**PyTorch**](https://pytorch.org), [**TensorFlow**](https://www.tensorflow.org), [**JAX**](https://github.com/google/jax), and [**NumPy**](https://numpy.org).
 
 ```python
 import foolbox as fb

guide/guide/README.md

@@ -2,4 +2,4 @@
 
 ## What is Foolbox?
 
-**Foolbox** is a **Python library** that let's easily run adversarial attacks against machine learning models like deep neural networks. It is built on top of [**EagerPy**](https://eagerpy.jonasrauber.de) and works natively with models in [**PyTorch**](https://pytorch.org), [**TensorFlow**](https://www.tensorflow.org), [**JAX**](https://github.com/google/jax), and [**NumPy**](https://numpy.org).
+**Foolbox** is a **Python library** that let's you easily run adversarial attacks against machine learning models like deep neural networks. It is built on top of [**EagerPy**](https://eagerpy.jonasrauber.de) and works natively with models in [**PyTorch**](https://pytorch.org), [**TensorFlow**](https://www.tensorflow.org), [**JAX**](https://github.com/google/jax), and [**NumPy**](https://numpy.org).