This is a project website of the paper "R^2-Trans: Fine-Grained Visual Categorization with Redundancy Reduction" This website includes the following materials for testing and checking our results reported in our paper
- The trained model
- The test scripts
- More visualization of our experiments [TODO]
We trained and tested our code in the following environments with one A6000 GPU:
ubuntu18.04
python3.7
CUDA 11.1
cuDNN 8.0.5
Pytorch 1.8.1
...
Then install the required packages:
pip install -r requirements.txt
We provide trained models (Dropbox) on three different datasets: cub, dogs and nabirds. You can download model files as well as corresponding datasets, and organize them as follows:
.
├── checkpoint
│ ├── cub_R2-Trans_checkpoint.pth
│ ├── dogs_R2-Trans_checkpoint.pth
│ └── nabirds_R2-Trans_checkpoint.pth
├── data
│ ├── cub/
│ ├── dogs/
│ └── nabirds/
└── ···
example for cub:
python3 test.py --dataset cub
--data_root ./data/CUB_dataset/CUB_200_2011/
--checkpoint ./checkpoint/cub_R2-Trans_checkpoint.pth
--eval_batch_size 5
--gpu_ids 0