Skip to content

Latest commit

 

History

History
128 lines (83 loc) · 5.69 KB

README.md

File metadata and controls

128 lines (83 loc) · 5.69 KB

PDiscoNet: Semantically consistent part discovery for fine-grained recognition

This is the GitHub page for the paper Semantically consistent part discovery for fine-grained recognition.

Datasets and environment

First, download the datasets here:

  1. CelebA: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html. Use the Google Drive link, download img/img_celeba.7z, and unzip.
  2. CUB: https://data.caltech.edu/records/65de6-vp158. Get CUB_200_2011.tgz.
  3. PartImageNet: https://github.com/TACJu/PartImageNet. Download the PartImageNet_OOD version.

For the default folder structure, clone the git repo and extract the datasets into their respective folders in /datasets/ (extract the CelebA dataset in a new folder /celeba/unaligned/). So, the default folder structure is:

├── celeba
├── cub
├── datasets
│   ├── celeba
│   │	└── unaligned
│   ├── cub
│   │   ├── CUB_200_2011
│   └── partimagenet
│       ├── test
│       ├── train
│       └── val
└── partimagenet

Next, build the conda environment using the environment.yml file.

Finally, to prepare the PartImageNet dataset, there is one more step. When we first downloaded the datasets, the PartImageNet_OOD version of the data was the only existing version. However, in this version, the sets of classes in 'test', 'train', and 'val' were disjoint. Thus, we created two subsets of the 'train' dataset: a training subset and a testing subset. Simply run prep.py in datasets/partimagenet to prepare the dataset.

Using the model

The argument parser in main.py has the following parameters:

--model_name determines the name under which the file containing the parameters will be saved, and the folder in which the results will be saved. The parameters file will be saved as ./[dataset]/[model_name].pt, and the results will be saved in ../results_[model_name].

--data_root is the folder containing the the dataset directories, i.e. the folder containing /celeba, /cub, and /partimagenet.

--dataset determines which dataset to use. Choose celeba, cub, or partimagenet.

--num_parts is the number of parts the model should use. In the paper we trained the model with 4, 8, and 16 parts for CelebA and CUB, and 8, 25, and 50 parts for PartImageNet.

--lr is the learning rate. We used 1e-4 for all datasets.

--batch_size is the batch size, we used 15 for CUB, and 20 for CelebA and PartImageNet.

--image_size is the resolution to which the input images will be cropped. First, the short edge of the raw image is resized to image_size, and then the resized image is cropped to (image_size x image_size).

--epochs is the number of epochs to run. The default for CelebA is 15, the default for CUB is 28, and the default for PartImageNet is 20.

--pretrained_model_name is only used when evaluating the model, or when continuing the training process with a previously saved model. It should be equal to the --model_name parameter used when the model was trained. Used in conjunction with --warm_start True.

--save_maps determines whether attention maps are saved in the validation function in main.py.

--only_test is used when you do not want to train the model, and instead you only wish to evaluate using a set of parameters. When you use this option, you should also use --warm_start and --pretrained_model_name.

See below for some examples for each dataset.

CelebA

Training
python main.py --model_name celeb_8parts --data_root ./datasets --dataset celeba --num_parts 8 --batch_size 20 --image_size 256 --epochs 15
Testing
python main.py --model_name celeb_8parts --data_path ../datasets/celeba --num_parts 8 --pretrained_model_name celeb_8parts --image_size 256 --warm_start True --only_test True
Evaluating parts
python evaluate_celeba.py --model_path ./celeba/celeb_8parts.pt --data_root ./datasets --num_parts 8 --image_size 256

CUB

Training
python main.py --model_name cub_8parts --data_root ./datasets --dataset cub --num_parts 8 --batch_size 16 --image_size 448 --epochs 28
Testing
python main.py --model_name cub_8parts --data_root ./datasets --dataset cub --num_parts 8 --batch_size 16 --image_size 448 --pretrained_model_path ./cub/cub_8parts.pt --save_figures --only_test
Evaluating parts
ppython evaluate_cub.py --model_path ./cub/cub_8parts.pt --data_root ./datasets --num_parts 8 --image_size 448

PartImageNet

Training
python main.py --model_name partimagenet_25parts --data_root ./datasets --dataset partimagenet --num_parts 25 --batch_size 20 --image_size 224 --epochs 20
Testing
python main.py --model_name partimagenet_25parts --data_root ./datasets --dataset partimagenet --num_parts 25 --batch_size 20 --image_size 224 --pretrained_model_path ./partimagenet/partimagenet_25parts.pt --save_figures --only_test
Evaluating parts
python evaluate_partimagenet.py --model_path ./partimagenet/partimagenet_25parts.pt --data_root ./datasets --num_parts 25 --image_size 224

Acknowledgements

Parts of the codebase were inspired by, or taken from:

https://github.com/subhc/unsup-parts

https://github.com/zxhuang1698/interpretability-by-parts

https://github.com/ShirAmir/dino-vit-features/.

If you find any bugs, please either send me an e-mail or open an issue on GitHub.