Fine-Grained Visual Classification using Self Assessment Classifier

Prerequisites

PYTHON 3.7 version

CUDA 11.0 version

pip install -r requirements.txt

Datasets

• Download CUB-200-2011 dataset (tfrecords) at link and extract them into Bird/Data folder.
• Download FGVC AIRCRAFT dataset (tfrecords) at link and extract them into Aircraft/Data folder.
• Download STANFORD DOGS dataset at link, then convert them into tfrecords format and put into Dog/Data folder.

Dictionary

• Download data dictionary at link and extract them into data folder.

Training

Please download pretrained backbone of WS_DAN at link and extract them into pre_trained folder.

• To train our method on CUB-200-2011 dataset, please run:

  bash train_sample_bird.sh
  

• To train our method on FGVC AIRCRAFT dataset, please run:

  bash train_sample_aircraft.sh
  

• To train our method on STANFORD DOGS dataset, please run:

  bash train_sample_dog.sh
  

Testing

Evaluate

• To evaluate our method on CUB-200-2011 dataset, please run:

  bash eval_sample_bird.sh
  

• To evaluate our method on FGVC AIRCRAFT dataset, please run:

  bash eval_sample_aircraft.sh
  

• To evaluate our method on STANFORD DOGS dataset, please run:

  bash eval_sample_dog.sh
  

Pretrained model

We provide the pretrained model of SAC integrated in WS_DAN on CUB-200-2011 dataset.

• Download our pretrained weights at link and extract them into Bird/SAC/TRAIN/Bird folder.

Citation

If you use this code as part of any published research, we'd really appreciate it if you could cite the following paper:

Updating

License

MIT License