Codes for "Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier"

Related tags

Deep Learning Deep-RTC
Overview

Deep-RTC [project page]

This repository contains the source code accompanying our ECCV 2020 paper.

Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier
Tz-Ying Wu, Pedro Morgado, Pei Wang, Chih-Hui Ho, Nuno Vasconcelos

@inproceedings{Wu20DeepRTC,
	title={Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier},
	author={Tz-Ying Wu and Pedro Morgado and Pei Wang and Chih-Hui Ho and Nuno Vasconcelos},
	booktitle={European Conference on Computer Vision (ECCV)},
	year={2020}
}

Dependencies

  • Python (3.5.6)
  • PyTorch (1.2.0)
  • torchvision (0.4.0)
  • NumPy (1.15.2)
  • Pillow (5.2.0)
  • PyYaml (5.1.2)
  • tensorboardX (1.8)

Data preparation

These datasets can be downloaded from the above links. Please organize the images in the hierarchical folders that represent the dataset hierarchy, and put the root folder under prepro/raw. For example,

prepro/raw/imagenet
--abstraction
----bubble
------ILSVRC2012_val_00014026.JPEG
------ILSVRC2012_val_00000697.JPEG
...
--physical_entity
----object
...

While CIFAR100 and iNaturalist have released taxonomies, we built the tree-type taxonomy of AWA2 and ImageNet with WordNet. All the taxonomies are provided in prepro/data/{dataset}/tree.npy, and the data splits are provided in prepro/splits/{dataset}/{split}.json. Please refer to prepro/README.md for more details. After the raw images are managed hierarchically, run

$ ./prepare_data.sh {dataset}

where {dataset}=awa2/cifar100/imagenet/inaturalist. This will automatically generate the data lists for all splits, and build the codeword matrices needed for training Deep-RTC. Note that our codes can be applied to other datasets once they are organized hierarchically.

Training and evaluation

To train and evaluate Deep-RTC, run

$ export PYTHONPATH=${PWD}/prepro:${PYTHONPATH}
$ ./run.sh {dataset}

where {dataset}=awa2/cifar100/imagenet/inaturalist. Our pretrained models can be downloaded here.

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Comments
  • Question regarding CIFAR 100 preparation

    Question regarding CIFAR 100 preparation

    Hi, I have a question regarding CIFAR 100 preparation. Usually, we utilize CIFAR 100 as a python-version on their webpage, which is divided into test and train files. However, according to the data preparation section on README.md, it should be saved as jpg with class hierarchy. According to train/valid/test splits in https://github.com/gina9726/Deep-RTC/tree/master/prepro/splits/cifar100, the file format is "Image{:5d}.jpg" and some indices are missing.

    Could you tell me how to form CIFAR 100 dataset?

    The Long-tailed version link just denotes the various long-tailed versions of tfrecord files.

    Thank you.

    opened by jd730 1
  • Fix hier_dataset to use the new 'tree.npy' and 'leaf_nodes.npy'

    Fix hier_dataset to use the new 'tree.npy' and 'leaf_nodes.npy'

    Hi, Thanks for your great work. I tried to execute the code and figured out there was an issue with the naming of the tree.npy and 'leaf_nodes.npy'.

    Thanks Harsh

    opened by rangwani-harsh 0
Owner
Gina Wu
https://gina9726.github.io/
Gina Wu
GitHub
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