L1-SVM based parts extraction

Code for the paper "Classification-Specific Parts for Improving Fine-Grained Visual Categorization"

Installation

Prerequisite

tested on Ubuntu 18.04

1. Install Docker CE:

sudo apt-get update
sudo apt-get install apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo apt-key fingerprint 0EBFCD88
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt-get update
sudo apt-get install docker-ce

2. Install Docker-compose:

sudo curl -L "https://github.com/docker/compose/releases/download/1.24.1/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
sudo ln -s /usr/local/bin/docker-compose /usr/bin/docker-compose

3. Install Nvidia-Docker:

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install nvidia-docker2

4. Restart Docker services or reboot your PC.

Build the experiment image

Create the required docker image with the script:

./01_install.sh

Running the experiments

Download the datasets and models

1. Download the needed datasets. Set up the according paths in the .env file.
2. Download the fine-tuned models or copy your own models to the models folder

Start an experiment

You could either start the whole pipeline for all set datasets (DATASET variable in the .env file):

./02_main.sh

or set according datasets (and GPUs) manually:

GPU=0 DATASETS=NAB ./02_main.sh
GPU=1 DATASETS="CUB200 FLOWERS" ./02_main.sh
GPU=2 BATCH_SIZE=16 DATASETS=CARS ./02_main.sh

All trained SVMs, logs with accuracy results, and extracted features will be stored in output and datasets folders.

Citation

You are welcome to use our code in your research! If you do so please cite it as:

@inproceedings{Korsch19_CSPARTS,
  title = {Classification-Specific Parts for Improving Fine-Grained Visual Categorization},
  booktitle = {German Conference on Pattern Recognition (GCPR)},
  author = {Dimitri Korsch and Paul Bodesheim and Joachim Denzler},
  pages = {62--75},
  year = {2019},
}