From 3081ad6f414f252c231bfbdd38e6d4ccdaf5e37a Mon Sep 17 00:00:00 2001 From: Jason Cooke Date: Thu, 15 Aug 2019 14:22:59 +1200 Subject: [PATCH] docs: fix typo --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f2fdbf8..bbf0b8d 100644 --- a/README.md +++ b/README.md @@ -30,7 +30,7 @@ This is an official implementation for [Deformable Convolutional Networks](https * The original implementation is based on our internal Caffe version on Windows. There are slight differences in the final accuracy and running time due to the plenty details in platform switch. * The code is tested on official [MXNet@(commit 62ecb60)](https://github.com/dmlc/mxnet/tree/62ecb60) with the extra operators for Deformable ConvNets. - * After [MXNet@(commit ce2bca6)](https://github.com/dmlc/mxnet/tree/ce2bca6) the offical MXNet support all operators for Deformable ConvNets. + * After [MXNet@(commit ce2bca6)](https://github.com/dmlc/mxnet/tree/ce2bca6) the official MXNet support all operators for Deformable ConvNets. * We trained our model based on the ImageNet pre-trained [ResNet-v1-101](https://github.com/KaimingHe/deep-residual-networks) using a [model converter](https://github.com/dmlc/mxnet/tree/430ea7bfbbda67d993996d81c7fd44d3a20ef846/tools/caffe_converter). The converted model produces slightly lower accuracy (Top-1 Error on ImageNet val: 24.0% v.s. 23.6%). * This repository used code from [MXNet rcnn example](https://github.com/dmlc/mxnet/tree/master/example/rcnn) and [mx-rfcn](https://github.com/giorking/mx-rfcn). @@ -91,7 +91,7 @@ If you find Deformable ConvNets useful in your research, please consider citing: ## Requirements: Software -1. MXNet from [the offical repository](https://github.com/dmlc/mxnet). We tested our code on [MXNet@(commit 62ecb60)](https://github.com/dmlc/mxnet/tree/62ecb60). Due to the rapid development of MXNet, it is recommended to checkout this version if you encounter any issues. We may maintain this repository periodically if MXNet adds important feature in future release. +1. MXNet from [the official repository](https://github.com/dmlc/mxnet). We tested our code on [MXNet@(commit 62ecb60)](https://github.com/dmlc/mxnet/tree/62ecb60). Due to the rapid development of MXNet, it is recommended to checkout this version if you encounter any issues. We may maintain this repository periodically if MXNet adds important feature in future release. 2. Python 2.7. We recommend using Anaconda2 as it already includes many common packages. We do not support Python 3 yet, if you want to use Python 3 you need to modify the code to make it work.