readme

README.md

@@ -2,14 +2,14 @@
 
 This repo constains the pytorch implementation for the ECCV 2018 paper [(arxiv)](https://arxiv.org/pdf/.pdf).
 We use deep networks to learn feature representations optimized for nearest neighbor classifiers, which could generalize better for new object categories.
-We re-investigate the [Neighborhood Component Analysis (NCA)](http://www.cs.toronto.edu/~fritz/absps/nca.pdf), and we propose a technique to make it 
-scalable to deep networks and large-scale datasets.
+This project is a re-investigation of [Neighborhood Component Analysis (NCA)](http://www.cs.toronto.edu/~fritz/absps/nca.pdf)
+with recent technologies to make it scalable to deep networks and large-scale datasets.
 
-Much of code is extened from the previous [unsupervised learning project](https://arxiv.org/pdf/1805.01978.pdf).
+Much of code is extended from the previous [unsupervised learning project](https://arxiv.org/pdf/1805.01978.pdf).
 Please refer to [this repo](https://github.com/zhirongw/lemniscate.pytorch) for more details.
 
 
-## Pretrained Model
+## Pretrained Models
 
 Currently, we provide three pretrained ResNet models.
 Each release contains the feature representation of all ImageNet training images (600 mb) and model weights (100-200mb).
@@ -18,6 +18,8 @@ Each release contains the feature representation of all ImageNet training images
 - [ResNet 34](http://zhirongw.westus2.cloudapp.azure.com/models/snca_resnet34.pth.tar) (top 1 accuracy 74.41%)
 - [ResNet 50](http://zhirongw.westus2.cloudapp.azure.com/models/snca_resnet50.pth.tar) (top 1 accuracy 76.57%)
 
+Code to reproduce the rest of the experiments are comming soon.
+
 ## Nearest Neighbors
 
 Please follow [this link](http://zhirongw.westus2.cloudapp.azure.com/nn.html) for a list of nearest neighbors on ImageNet.
@@ -49,8 +51,8 @@ Please refer to the official repo for details of data preparation and hardware c
   Currently, the implementation of nca module is not paralleled across multiple GPUs.
   Hence, the first GPU will consume much more memory than the others.
   For example, when training a ResNet18 network, GPU 0 will consume 11GB memory, while the others each takes 2.5GB.
-  You will need to set the Caffe style "iter_size" for training deep networks.
-  Our released models are trained with V100 machines 
+  You will need to set the Caffe style "-b 128 --iter-size 2" for training deep networks.
+  Our released models are trained with V100 machines.
   
 - Training on CIFAR10:
 
@@ -74,7 +76,7 @@ For any questions, please feel free to reach
 Zhirong Wu: xavibrowu@gmail.com
 ```
 
-# Contributing
+## Contributing
 
 This project welcomes contributions and suggestions.  Most contributions require you to agree to a
 Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us

ThirdPartyNotices.txt

@@ -0,0 +1,81 @@
+************************************************************************
+
+THIRD-PARTY SOFTWARE NOTICES AND INFORMATION
+
+This project incorporates components from the projects listed below.
+The original copyright notices and the licenses under which Microsoft received such components are set forth below.
+Microsoft reserves all rights not expressly granted herein, whether by implication, estoppel or otherwise.
+
+1.  Pytorch (https://github.com/pytorch/pytorch)
+2.  lemniscate (https://github.com/zhirongw/lemniscate.pytorch)
+
+From PyTorch:
+
+Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
+Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
+Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+Copyright (c) 2011-2013 NYU                      (Clement Farabet)
+Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
+Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+
+From Caffe2:
+
+Copyright (c) 2016-present, Facebook Inc. All rights reserved.
+
+All contributions by Facebook:
+Copyright (c) 2016 Facebook Inc.
+ 
+All contributions by Google:
+Copyright (c) 2015 Google Inc.
+All rights reserved.
+ 
+All contributions by Yangqing Jia:
+Copyright (c) 2015 Yangqing Jia
+All rights reserved.
+ 
+All contributions from Caffe:
+Copyright(c) 2013, 2014, 2015, the respective contributors
+All rights reserved.
+ 
+All other contributions:
+Copyright(c) 2015, 2016 the respective contributors
+All rights reserved.
+ 
+Caffe2 uses a copyright model similar to Caffe: each contributor holds
+copyright over their contributions to Caffe2. The project versioning records
+all such contribution and copyright details. If a contributor wants to further
+mark their specific copyright on a particular contribution, they should
+indicate their copyright solely in the commit message of the change when it is
+committed.
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
+   and IDIAP Research Institute nor the names of its contributors may be
+   used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.