This repository is the official implementation of our CVPR 2022 Paper "Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting". Link
In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Min Shi, Hao Lu, Chen Feng, Chengxin Liu, Zhiguo Cao*
Key Laboratory of Image Processing and Intelligent Control, Ministry of Education School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, China * corresponding author.
- We are currently organizing a more detailed readme file, with more instructions and discussions on how to build a strong baseline for class-agnostic counting. You can first explore our codes. Feel free to post your questions!
- 23 Apr 2022: Training and inference code is released.
Our code has been tested on Python 3.8.5 and PyTorch 1.8.1+cu111. Please follow the official instructions to setup your environment. See other required packages in requirements.txt.
We train and evaluate our methods on FSC-147 dataset. Please follow the FSC-147 official repository to download and unzip the dataset. Then, please place the data lists data_list/train.txt, data_list/val.txt and data_list/test.txt in the dataset directory. Note that, you should also download data annotation file annotation_FSC147_384.json and ImageClasses_FSC147.txt file from Link and place them in the dataset folder. Final the path structure used in our code will be like :
$PATH_TO_DATASET/
├──── gt_density_map_adaptive_384_VarV2
│ ├──── 6146 density maps (.npy files)
│
├──── images_384_VarV2
│ ├──── 6146 images (.jpg)
│
├────annotation_FSC147_384.json (annotation file)
├────ImageClasses_FSC147.txt (category for each image)
├────Train_Test_Val_FSC_147.json (official data splitation file, which is not used in our code)
├────train.txt (We generate the list from official json file)
├────val.txt
├────test.txt
Please first specify the checkpoint directory and checkpoint files in the configuration yaml file. We have included our pretrained model here, and the configuration has been set in ./config/test_bmnet+.yaml. Please place the downloaded model_best.pth in /checkpoint/bmnet+_pretrained.
Run the following command to conduct inference on the FSC-147 dataset. By default the model will be evaluated on the test set. If you want to test the model on the validation set. Please modify the list_test under DATASET to DIR_OF_FSC147_DATASET/val.txt
cd CODE_DIRECTORY
python train.py --cfg 'config/test_bmnet+.yaml'
Note that, before running inference, you need to modify DIR_OF_FSC147_DATASET to the path of your own FSC-147 dataset.
- The pretrained checkpoint should produce the same results as reported in the paper, i.e., MAE=15.74, MSE=58.53 on the validation set, MAE=14.62, MSE=91.83 on the test set.
Please first modify DIR_OF_FSC147_DATASET and DIR_FOR_YOUR_CHECKPOINTS in config/bmnet+_fsc147.yaml to train BMNet+, same modifications for training BMNet.
Run the following command to train BMNet+:
cd CODE_DIRECTORY
python train.py --cfg 'config/bmnet+_fsc147.yaml'
- Training BMNet+ requires less than 12GB memory on a single RTX 3090. The training takes about 1 day.
If you find this work or code useful for your research, please cite:
@inproceedings{min2022bmnet,
title={Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting},
author={Shi, Min and Hao, Lu and Feng, Chen and Liu, Chengxin and Cao, Zhiguo},
booktitle={Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
This code is only for non-commercial purposes. Trained models included in this repository can only be used/distributed for non-commercial purposes. Anyone who violates this rule will be at his/her own risk.