This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

Related tags

Deep Learning fine-grained-recognition
Overview

TransFG: A Transformer Architecture for Fine-grained Recognition

PWC PWC PWC PWC

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

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:

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

Comments
  • visualization code

    visualization code

    Thanks for your wonderful work! I meet some problems when I try to visualize the part attention patch as your paper showed. So could you provide the visualization code. Thanks so much! image

    opened by lao-ling-jie 3
  • Pip won't find requirements

    Pip won't find requirements

    I'm trying to setup the environment but pip won't find the requirements. In a virtual environment with python 3.7: $ pip install -r requirements.txt
    Defaulting to user installation because normal site-packages is not writeable ERROR: Could not find a version that satisfies the requirement torch==1.5.1 (from versions: 1.7.1, 1.8.0, 1.8.1, 1.9.0, 1.9.1, 1.10.0) ERROR: No matching distribution found for torch==1.5.1

    opened by DRM-Free 1
  • patch embeddings always 0?

    patch embeddings always 0?

    I was reading the paper and checking the code and I can't see when you add value to the patch embbedings, I was debugging the code and in this part I only see you create a zero tensor and after on forward you only add this tensor. In which moment you give a value to the patch embeddings?

    line 157 https://github.com/TACJu/TransFG/blob/master/models/modeling.py#L157 self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches+1, config.hidden_size))

    Line 173 embeddings = x + self.position_embeddings

    opened by dcastf01 1
  • CVE-2007-4559 Patch

    CVE-2007-4559 Patch

    Patching CVE-2007-4559

    Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

    If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

    opened by TrellixVulnTeam 0
  • About the training details

    About the training details

    First of all, thank you for your work, which has benefited me a lot.

    After several attempts, only 91% accuracy can be obtained on the cub. Can you provide model parameters and training details with 91.7% accuracy.Thank you very much if you reply.

    opened by shiyan-cui 0
  • Would you like to open source the implementation based on [DeiT] pretrained on ImageNet-1K with distillation fine-tuning.

    Would you like to open source the implementation based on [DeiT] pretrained on ImageNet-1K with distillation fine-tuning.

    There was a sentence on the project page that went, "Implementation based on DeiT pretrained on ImageNet-1K with distillation fine-tuning will be released soon". It will be great if you still have the plan to open source the implementation based on [DeiT] pretrained on ImageNet-1K. Thank you! I am looking forward to your reply.

    opened by Anyway2022 0
  • About Stanford dogs accuracy

    About Stanford dogs accuracy

    Hi, could you release your training settings for the Stanford dogs dataset? I set the lr to 3e-3 and did not change other settings, however the model is underfitting. I only get 1.7% accuracy after 200k steps.

    opened by EdwinKuo1337 2
Owner
Ju He
I'm a first-year PhD student at Johns Hopkins University, where my advisor is Bloomberg Distinguished Professor Alan L. Yuille.
Ju He
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