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VA-Count

[ECCV 2024] Zero-shot Object Counting with Good Exemplars [paper]

Zero-shot Object Counting with Good Exemplars

News🚀

2024.09.27: Our code is released.
2024.09.26: Our inference code has been updated, and the code for selecting exemplars and the training code will be coming soon.
2024.07.02: VA-Count is accepted by ECCV2024.

Overview

Overview of the proposed method. The proposed method focuses on two main elements: the Exemplar Enhancement Module (EEM) for improving exemplar quality through a patch selection integrated with Grounding DINO, and the Noise Suppression Module (NSM) that distinguishes between positive and negative class samples using density maps. It employs a Contrastive Loss function to refine the precision in identifying target class objects from others in an image.

Environment

pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install timm==0.3.2
pip install numpy
pip install matplotlib tqdm 
pip install tensorboard
pip install scipy
pip install imgaug
pip install opencv-python
pip3 install hub

For more information on Grounding DINO, please refer to the following link:

GroundingDINO We are very grateful for the Grounding DINO approach, which has been instrumental in our work！

Datasets

FSC147
CARPK

Preparing the datasets as follows:

./data/
|--FSC147
|  |--images_384_VarV2
|  |  |--2.jpg
|  |  |--3.jpg
|  |--gt_density_map_adaptive_384_VarV2
|  |  |--2.npy
|  |  |--3.npy
|  |--annotation_FSC147_384.json
|  |--Train_Test_Val_FSC_147.json
|  |--ImageClasses_FSC147.txt
|  |--train.txt
|  |--test.txt
|  |--val.txt
|--CARPK/
|  |--Annotations/
|  |--Images/
|  |--ImageSets/

Inference

For inference, you can download the model from Baidu-Disk, passward:paeh

python FSC_test.py --output_dir ./data/out/results_base --resume ./data/checkpoint_FSC.pth

Single and Multiple Object Classifier Training

python datasetmake.py
python biclassify.py

You can also directly download the model from Baidu-Disk, passward:psum Save it in ./data/out/classify/

Generate exemplars

python grounding_pos.py --root_path ./data/FSC147/
python grounding_neg.py --root_path ./data/FSC147/

Train

CUDA_VISIBLE_DEVICES=0 python FSC_pretrain.py \
    --epochs 500 \
    --warmup_epochs 10 \
    --blr 1.5e-4 --weight_decay 0.05

You can also directly download the pre-train model from Baidu-Disk, passward:xynw Save it in ./data/

CUDA_VISIBLE_DEVICES=0 python FSC_train.py --epochs 1000 --batch_size 8 --lr 1e-5 --output_dir ./data/out/

Citation

@inproceedings{zhu2024zero,
  title={Zero-shot Object Counting with Good Exemplars},
  author={Zhu, Huilin and Yuan, Jingling and Yang, Zhengwei and Guo, Yu and Wang, Zheng and Zhong, Xian and He, Shengfeng},
  booktitle={Proceedings of the European Conference on Computer Vision},
  year={2024}
}

Acknowledgement

This project is based on the implementation from CounTR, we are very grateful for this work and GroundingDINO.