TransFG: A Transformer Architecture for Fine-grained Recognition

Official PyTorch code for the paper: TransFG: A Transformer Architecture for Fine-grained Recognition

Implementation based on DeiT pretrained on ImageNet-1K with distillation fine-tuning will be released soon.

Framework

Dependencies:

• Python 3.7.3
• PyTorch 1.5.1
• torchvision 0.6.1
• ml_collections

Usage

1. Download Google pre-trained ViT models

• Get models in this link: ViT-B_16, ViT-B_32...

wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz

2. Prepare data

In the paper, we use data from 5 publicly available datasets:

• CUB-200-2011
• Stanford Cars
• Stanford Dogs
• NABirds
• iNaturalist 2017

Please download them from the official websites and put them in the corresponding folders.

3. Install required packages

Install dependencies with the following command:

pip3 install -r requirements.txt

4. Train

To train TransFG on CUB-200-2011 dataset with 4 gpus in FP-16 mode for 10000 steps run:

CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m torch.distributed.launch --nproc_per_node=4 train.py --dataset CUB_200_2011 --split overlap --num_steps 10000 --fp16 --name sample_run

Citation

If you find our work helpful in your research, please cite it as:

@article{he2021transfg,
  title={TransFG: A Transformer Architecture for Fine-grained Recognition},
  author={He, Ju and Chen, Jieneng and Liu, Shuai and Kortylewski, Adam and Yang, Cheng and Bai, Yutong and Wang, Changhu and Yuille, Alan},
  journal={arXiv preprint arXiv:2103.07976},
  year={2021}
}

Acknowledgement

Many thanks to ViT-pytorch for the PyTorch reimplementation of An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale