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Logo-2K+:A Large-Scale Logo Dataset for Scalable Logo Classification

Logo-2k+ Dataset

example

Logo-2k+ Dataset Description

In this work, we construct a large scale logo dataset, Logo-2K+, which covers a diverse range of logo classes from real-world logo images. Our resulting logo dataset contains 167,140 images with 10 root categories and 2,341 categories.
The statistic comparison of 10 root categories from Logo-2K+ is shown as follows.

Root Category Logos Images
Food 769 54,507
Clothes 286 20,413
Institution 238 17,103
Accessories 210 14,569
Transportation 203 14,719
Electronic 191 13,972
Necessities 182 13,205
Cosmetic 115 7,929
Leisure 99 7,338
Medical 48 3,385
Total 2,341 167,140

Download links

Baidu Drive link: https://pan.baidu.com/s/1L_9JROsWSQEAznNiy-wTBQ password: 945w

Google Drive link: https://drive.google.com/open?id=1PTA24UTZcsnzXPN1gmV0_lRg3lMHqwp6

DRNA-Net

This is a PyTorch implementation of the AAAI2020 paper "Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification".

Requirements

  • Python >= 3
  • PyTorch >= 0.4 Install PyTorch >=0.4 with GPU (code are GPU-only), refer to official website
  • Install cupy, you can install via pip install cupy-cuda80 or(cupy-cuda90,cupy-cuda91, etc).
  • Install other dependencies: pip install -r requirements.txt

Datasets

Download the Logo-2K+(https://github.com/msn199959/Logo-2k-plus-Dataset) datasets and put it in the root directory. You can also try other classification datasets.

Training on Logo-2K+ dataset:

Download the training, testing data. Since the program loading the data in drna_master/data by default, you can set the data path as following.

  • cd drna_master
  • mkdir data
  • cd data
  • ln -s $ dataset path

Then you can set some hyper-parameters in drna_master/config.py. If you want to train the DRNA-Net, just run python train.py. During training, the log file and checkpoint file will be saved in save_dir directory.

Test the model

If you want to test the DRNA-Net, just run python test.py. You need to specify the test_model in config.py to choose the checkpoint model for testing.

Reference

If you are interested in our work and want to cite it, please acknowledge the following paper:

@inproceedings{Wang2020Logo2K,
author={Jing Wang, and Weiqing Min, and Sujuan Hou, and Shengnan Ma, and Yuanjie Zheng, and Haishuai Wang, and Shuqiang Jiang},
booktitle={AAAI Conference on Artificial Intelligence. Accepted},
title={{Logo-2K+:} A Large-Scale Logo Dataset for Scalable Logo Classification},
year={2020}
}