Unsupervised Salient Object Detection with Spectral Cluster Voting [CVPRW 2022]

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This repo contains the code to reproduce the experiments results in the paper "Unsupervised Salient Object Detection with Spectral Cluster Voting". [Project page]

Contents

• Demo
• Preparation
• Training
• Inference
• Pre-trained weights
• Generating pseudo-masks with own images
• Citation
• Acknowledgements

Demo

Please find our demo built with Hugging Face and Gradio.

Preparation

1. Download datasets and pseudo-masks

To train/evaluate SelfMask, you first need to download some datasets. For training, please download the DUTS-TR dataset and its pseudo-masks (located at datasets/swav_mocov2_dino_p16_k234.json in this repo). For evaluation, please download the DUT-OMRON, DUTS-TE, and ECSSD datasets. Please don't change the (sub)directory name(s) as the code assumes the original directory names. We advise you to put the downloaded dataset(s) into the following directory structure for ease of implementation:

your_dataset_directory
├──DUTS
│  ├──DUTS-TE-Image
│  ├──DUTS-TE-Mask
│  ├──DUTS-TR-Image
├──DUTS-OMRON
│  ├──DUT-OMRON-image
│  ├──pixelwiseGT-new-PNG
├──ECSSD
   ├──images
   ├──ground_truth_mask

2. Download the following python packages:

faiss-gpu==1.7.1
torch>=1.10
matplotlib==3.5.1
natsort==7.1.1
opencv==4.5.5
pycocotools==2.0.4
scikit-learn==1.0.2
scipy==1.7.3
timm==0.4.12
tqdm==4.63.0
ujson==4.2.0
wandb==0.12.11
pyyaml==6.0

Training

Before running a training script, you need to set up some directory/file paths (e.g., dataset directory). For this please open "duts-dino-k234-nq20-224-swav-mocov2-dino-p16-sr10100.yaml" file in configs directory and find "dir_ckpt", "dir_dataset", and "pseudo_masks_fp" arguments. Then, type your corresponding paths:

...
dir_ckpt: [YOUR_DESIRED_CHECKPOINT_DIR]
...
dir_dataset: [YOU_DATASET_DIR]
...
pseudo_masks_fp: [PATH_TO_DOWNLOADED_PSEUDO_MASKS_FILE]
...

To train a model with 20 queries from scratch, please move to the scripts directory and run

bash train-selfmask-nq20.sh

It is worth noting that, by default, the code will evaluate the model at the end of every epoch, stores the metric values (and the model weights if there was an improvement in terms of metrics).

Inference

To run an inference of a pre-trained model, please run

python3 evaluator.py --dataset_name $DATASET_NAME --p_state_dict $PATH_TO_WEIGHTS --config $PATH_TO_MODEL_CONFIG

Here, the config file is the configuration file used for pre-training.

Pre-trained weights

We provide the pre-trained weights used for our experiments:

	# queries	IoU (%)	model
SelfMask	10	64.5	link
SelfMask	20	65.3	link

IoUs are measured on the DUTS-TE benchmark.

Generating pseudo-masks with own images

To generate pseudo-masks for your own images, please use mask_generator.py file. All you need to do is to set a list of image paths in the script (and save the resulting file if needed).

Citation

@InProceedings{shin2022selfmask,
    author    = {Shin, Gyungin and Albanie, Samuel and Xie, Weidi},
    title     = {Unsupervised Salient Object Detection With Spectral Cluster Voting},
    booktitle = {CVPRW},
    year      = {2022}
}

Acknowledgements

We borrowed the code for ViT, DINO, and MaskFormer from https://github.com/rwightman/pytorch-image-models, https://github.com/facebookresearch/dino, and https://github.com/facebookresearch/MaskFormer, respectively.

If you have any questions, please contact us at gyungin [at] robots [dot] ox [dot] ac [dot] uk.