Cross-Domain Few-Shot Learning (CD-FSL) Benchmark

Website

https://www.learning-with-limited-labels.com/

LeaderBoard

https://www.learning-with-limited-labels.com/challenge

Paper

Please cite the following paper in use of this evaluation framework: https://arxiv.org/pdf/1912.07200.pdf

@article{guo2019new,
  title={A New Benchmark for Evaluation of Cross-Domain Few-Shot Learning},
  author={Guo, Yunhui and Codella, Noel CF and Karlinsky, Leonid and Smith, John R and Rosing, Tajana and Feris, Rogerio},
  journal={arXiv preprint arXiv:1912.07200},
  year={2019}
}

Introduction

The Cross-Domain Few-Shot Learning (CD-FSL) challenge benchmark includes data from the CropDiseases [1], EuroSAT [2], ISIC2018 [3-4], and ChestX [5] datasets, which covers plant disease images, satellite images, dermoscopic images of skin lesions, and X-ray images, respectively. The selected datasets reflect real-world use cases for few-shot learning since collecting enough examples from above domains is often difficult, expensive, or in some cases not possible. In addition, they demonstrate the following spectrum of readily quantifiable domain shifts from ImageNet: 1) CropDiseases images are most similar as they include perspective color images of natural elements, but are more specialized than anything available in ImageNet, 2) EuroSAT images are less similar as they have lost perspective distortion, but are still color images of natural scenes, 3) ISIC2018 images are even less similar as they have lost perspective distortion and no longer represent natural scenes, and 4) ChestX images are the most dissimilar as they have lost perspective distortion, all color, and do not represent natural scenes.

Datasets

The following datasets are used for evaluation in this challenge:

Source domain:

• miniImageNet
  
  

The datasets below are used for relicating the results of the Multi-model Selection in the paper, for the challenge, only pre-trained model on miniImageNet is allowed

• CUB
• CIFAR100
• Caltech256
• DTD (https://www.robots.ox.ac.uk/~vgg/data/dtd/)

Target domains:

• EuroSAT:

  Home: http://madm.dfki.de/downloads

  Direct: http://madm.dfki.de/files/sentinel/EuroSAT.zip

• ISIC2018:

  Home: http://challenge2018.isic-archive.com

  Direct (must login): https://challenge.isic-archive.com/data#2018

• Plant Disease:

  Home: https://www.kaggle.com/saroz014/plant-disease/

  Direct: command line kaggle datasets download -d plant-disease/data

• ChestX-Ray8:

  Home: https://www.kaggle.com/nih-chest-xrays/data

  Direct: command line kaggle datasets download -d nih-chest-xrays/data

General information

• No meta-learning in-domain

• Only ImageNet based models or meta-learning allowed.

• 5-way classification

• n-shot, for varying n per dataset

• 600 randomly selected few-shot 5-way trials up to 50-shot (scripts provided to generate the trials)

• Average accuracy across all trials reported for evaluation.

• For generating the trials for evaluation, please refer to finetune.py and the examples below

Specific Tasks:

EuroSAT

• Shots: n = {5, 20, 50}

ISIC2018

• Shots: n = {5, 20, 50}

Plant Disease

• Shots: n = {5, 20, 50}

ChestX-Ray8

• Shots: n = {5, 20, 50}

Unsupervised Track

An optional second track has been included in this challenge that allows the use of a subset of unlabeled images from each dataset for study of un/self/semi-supervised learning methods. For learning and evaluation within each dataset, the images listed in text files contained in the unsupervised-track subfolder specific to each dataset may be used for such learning methods. Please see the website for additional information.

Enviroment

Python 3.5.5

Pytorch 0.4.1

h5py 2.9.0

Steps

1. Download the datasets for evaluation (EuroSAT, ISIC2018, Plant Disease, ChestX-Ray8) using the above links.

2. Download miniImageNet using https://drive.google.com/file/d/1uxpnJ3Pmmwl-6779qiVJ5JpWwOGl48xt/view?usp=sharing

3. Download CUB if multi-model selection is used.

    Change directory to ./filelists/CUB
    run source ./download_CUB.sh

4. Change configuration file ./configs.py to reflect the correct paths to each dataset. Please see the existing example paths for information on which subfolders these paths should point to.

5. Train base models on miniImageNet

   • Standard supervised learning on miniImageNet

       python ./train.py --dataset miniImageNet --model ResNet10  --method baseline --train_aug

   • Train meta-learning method (protonet) on miniImageNet

       python ./train.py --dataset miniImageNet --model ResNet10  --method protonet --n_shot 5 --train_aug

6. Save features for evaluation (optional, if there is no need to adapt the features during testing)

   • Save features for testing

       python save_features.py --model ResNet10 --method baseline --dataset CropDisease --n_shot 5 --train_aug

7. Test with saved features (optional, if there is no need to adapt the features during testing)

       python test_with_saved_features.py --model ResNet10 --method baseline --dataset CropDisease --n_shot 5 --train_aug

8. Test

   • Finetune with frozen model backbone:

       python finetune.py --model ResNet10 --method baseline  --train_aug --n_shot 5 --freeze_backbone

   • Finetune

       python finetune.py --model ResNet10 --method baseline  --train_aug --n_shot 5 

   • Example output: 600 Test Acc = 49.91% +- 0.44%

9. Test with Multi-model selection (make sure you have trained models on all the source domains (miniImageNet, CUB, Caltech256, CIFAR100, DTD))

   • Test Multi-model selection without fine-tuning:

      python model_selection.py --model ResNet10 --method baseline  --train_aug --n_shot 5 

   • Test Multi-model selection without fine-tuning:

     python model_selection.py --model ResNet10 --method baseline  --train_aug --n_shot 5 --fine_tune_all_models

10. For testing your own methods, simply replace the function finetune() in finetune.py with your own method. Your method should at least have the following arguments,

    • novel_loader: data loader for the corresponding dataset (EuroSAT, ISIC2018, Plant Disease, ChestX-Ray8)

    • n_query: number of query images per class

    • n_way: number of shots

    • n_support: number of support images per class

References

[1] Sharada P Mohanty, David P Hughes, and Marcel Salathe. Using deep learning for image based plant disease detection. Frontiers in plant science, 7:1419, 2016

[2] Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 12(7):2217–2226, 2019.

[3] Philipp Tschandl, Cliff Rosendahl, and Harald Kittler. The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data, 5:180161, 2018.

[4] Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, et al. Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic). arXiv preprint. arXiv:1902.03368, 2019

[5] Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly supervised classification and localization of common thorax diseases. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2097–2106, 2017