Yao-DD/S3N

Selective Sparse Sampling for Fine-grained Image Recognition

PyTorch Implementation

This repository contains:

• the pytorch implementation of Selective Sparse Sampling.
• the CUB-200-2011 demo (training, test).

Please follow the instruction below to install it and run the experiment demo.

Prerequisites

• System (tested on Ubuntu 14.04LTS and Win10)
• 2 Tesla P100 + CUDA CuDNN (CPU mode is also supported but significantly slower)
• Python=3.6.8
• PyTorch=0.4.1
• Jupyter Notebook
• Nest

Installation

1. 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

Prepare Data

1. 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

2. 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 demo

1. 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

Pretrained models

1. S3N model for CUB_200_2011 dataset is availavble on Baidu Disk.

   The link：https://pan.baidu.com/s/19x9zI_ZNi32sRGRgNwN_Fw
   code: r252

CAUTION

The current code was prepared under the above-mentioned prerequisites. The use of other version can cause problems.