Model Card for INTR: A Simple Interpretable Transformer for Fine-grained Image Classification and Analysis

INTR checkpoint on CUB dataset with backbone DETR-R50

Model Details

Model Description

Developed by: Dipanjyoti Paul, Arpita Chowdhury, Xinqi Xiong, Feng-Ju Chang, David Carlyn, Samuel Stevens, Kaiya Provost, Anuj Karpatne, Bryan Carstens, Daniel Rubenstein, Charles Stewart, Tanya Berger-Wolf, Yu Su, and Wei-Lun Chao
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Model type: [More Information Needed]
License: Apache 2.0
Fine-tuned from model: DETR-R50

Model Sources

Repository: Imageomics/INTR
Paper: A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
Demo: Inference time single-image prediction and visualization notebook. Note that this is focused on the CUB dataset.

Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Model	Dataset
CUB checkpoint download	CUB [More Information Needed]
Bird checkpoint download	Birds 525 [More Information Needed]
Butterfly checkpoint download	Cambridge Butterfly, images in the `train` folder

Training Procedure

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Follow the below format for data.

datasets
├── dataset_name
│   ├── train
│   │   ├── class1
│   │   │   ├── img1.jpeg
│   │   │   ├── img2.jpeg
│   │   │   └── ...
│   │   ├── class2
│   │   │   ├── img3.jpeg
│   │   │   └── ...
│   │   └── ...
│   └── val
│       ├── class1
│       │   ├── img4.jpeg
│       │   ├── img5.jpeg
│       │   └── ...
│       ├── class2
│       │   ├── img6.jpeg
│       │   └── ...
│       └── ...

Preprocessing [optional]

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Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

To evaluate the performance of INTR on the CUB dataset, on a multi-GPU (e.g., 4 GPUs) settings, execute the below command. INTR checkpoints are available at Fine-tune model and results.

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --master_port 12345 --use_env main.py --eval --resume <path/to/intr_checkpoint_cub_detr_r50.pth> --dataset_path <path/to/datasets> --dataset_name <dataset_name>

Similarly, replace cub in the name of the checkpoint with bird or butterfly to evaluate with the Birds 525 or Cambridge Butterfly checkpoint, respectively.

Testing Data, Factors & Metrics

Testing Data

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Model	Dataset
CUB checkpoint download	CUB [More Information Needed]
Birds checkpoint download	Birds 525 [More Information Needed]
Butterfly checkpoint download	Cambridge Butterfly, images in the `val` folder

Factors

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Metrics

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Results

Dataset	acc@1	acc@5
CUB	71.8	89.3
Birds 525	97.4	99.2
Butterfly	95.0	98.3

Summary

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Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation

BibTeX:

If you find our work helpful for your research, please consider citing our paper as well.

@article{paul2023simple,
  title={A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis},
  author={Paul, Dipanjyoti and Chowdhury, Arpita and Xiong, Xinqi and Chang, Feng-Ju and Carlyn, David and Stevens, Samuel and Provost, Kaiya and Karpatne, Anuj and Carstens, Bryan and Rubenstein, Daniel and Stewart, Charles and Berger-Wolf, Tanya and Su, Yu and Chao, Wei-Lun},
  journal={arXiv preprint arXiv:2311.04157},
  year={2023}
}

Model Citation:

@software{Paul_A_Simple_Interpretable_2023,
  author = {Paul, Dipanjyoti and Chowdhury, Arpita and Xiong, Xinqi and Chang, Feng-Ju and Carlyn, David and Stevens, Samuel and Provost, Kaiya and Karpatne, Anuj and Carstens, Bryan and Rubenstein, Daniel and Stewart, Charles and Berger-Wolf, Tanya and Su, Yu and Chao, Wei-Lun},
  license = {Apache-2.0},
  title = {{A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis}},
  doi = {<doi once generated>},
  url = {https://huggingface.co/imageomics/INTR},
  version = {1.0.0},
  month = sep,
  year = {2023}
}

APA:

Paper:

Paul, D., Chowdhury, A., Xiong, X., Chang, F., Carlyn, D., Stevens, S., Provost, K., Karpatne, A., Carstens, B., Rubenstein, D., Stewart, C., Berger-Wolf, T., Su, Y., & Chao, W. (2023). A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis. arXiv. https://doi.org/10.48550/arXiv.2311.04157.

Model Citation:

Acknowledgements

Our model is inspired by the DEtection TRansformer (DETR) method.

We thank the authors of DETR for doing such great work.

The Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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