initial commit

.gitignore

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+worktrees/
+.mypy_cache
+Makefile
+wip/
+
+# Created by https://www.gitignore.io/api/osx,vim,linux,python
+
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+# End of https://www.gitignore.io/api/osx,vim,linux,python

LICENSE

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+T License
+
+Copyright (c) 2017 Jonas Rauber & Wieland Brendel
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.rst

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+=======
+BagNets
+=======
+
+In this repository you find the model specification and pretrained weights for the bag-of-local-feature models published in
+
+`Approximating CNNs with Bag-of-local-Features models8works surprisingly well on ImageNet <http://arxiv.org/abs/XXXX.XXXX>`__.
+Wieland Brendel and Matthias Bethge, arXiv:XXXX.XXXX
+
+Installation
+------------
+
+.. code-block:: bash
+
+   pip install git+https://github.com/wielandbrendel/bag-of-local-features-models.git
+
+
+Usage
+-----
+
+The code provides simple means to initialize the models in either Pytorch or Keras. After installation please use the following
+code snippets to load the models:
+
+.. code-block:: python
+
+   import bagnets
+   pytorch_model = bagnets.pytorch.bagnet16(pretrained=True)
+
+.. code-block:: python
+
+   import bagnets
+   keras_model = bagnets.keras.bagnet16(pretrained=True)
+
+and replace bagnet16 with whatever size you want (available are bagnet8, bagnet16 and bagnet32). The last number refers to the
+maximum local patch size that the network can integrate over.
+
+Citation
+--------
+
+If you find BagNets useful for your scientific work, please consider citing it
+in resulting publications:
+
+.. code-block::
+
+  @article{brendel2018bagnets,
+    title={Approximating CNNs with Bag-of-local-Features models8works surprisingly well on ImageNet},
+    author={Brendel, Wieland and Bethge, Matthias},
+    journal={arXiv preprint arXiv:XXXX.XXXX},
+    year={2018},
+    url={http://arxiv.org/abs/XXXX.XXXX},
+    archivePrefix={arXiv},
+    eprint={XXXX.XXXX},
+  }
+
+You can find the paper on arXiv: https://arxiv.org/abs/XXXX.XXXX
+
+Authors
+-------
+
+* `Wieland Brendel <https://github.com/wielandbrendel>`_