This repository contains:
Please follow the instruction below to install it and run the experiment demo.
Install S3N via Nest's CLI tool:
# note that data will be saved under your current path
$ git clone https://github.com/Yao-DD/S3N.git ./S3N
$ nest module install ./S3N/ s3n
# verify the installation
$ nest module list --filter s3n
Download the CUB-200-2011 dataset:
$ mkdir ./S3N/datasets
$ cd ./S3N/datasets
# download and extract data
$ wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz
$ tar xvf CUB_200_2011.tgz
Prepare annotation files:
Move the file ./datasets/train.txt and ./datasets/test.txt into ./datasets/CUB_200_2011. The list of image file names and label is contained in the file ./datasets/CUB_200_2011/train.txt and ./datasets/CUB_200_2011/test.txt, with each line corresponding to one image:
<image_name> <class_id>
run the code as:
$ cd ./S3N
# run baseline
$ PYTHONWARNINGS='ignore' CUDA_VISIBLE_DEVICES=0,1 nest task run ./demo/cub_baseline.yml
# run S3N
$ PYTHONWARNINGS='ignore' CUDA_VISIBLE_DEVICES=0,1 nest task run ./demo/cub_s3n.yml
S3N model for CUB_200_2011 dataset is availavble on Baidu Disk.
The link:https://pan.baidu.com/s/19x9zI_ZNi32sRGRgNwN_Fw
code: r252
The current code was prepared under the above-mentioned prerequisites. The use of other version can cause problems.