Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
lib
 
 
 
 
 
 
 
 
 
 
 
 

ASM

Cost-Effective Object Detection: Active Sample Mining with Switchable Selection Criteria

Keze Wang, Liang Lin, Xiaopeng Yan, Ziliang Chen, Dongyu Zhang, Lei Zhang

Sun Yat-Sen University, Presented at TNNLS

License

For Academic Research Use Only!

Citing ASM

If you find ASM useful in your research, please consider citing:

@article{wang18asm,
    Author = {Keze Wang,Liang Lin, Xiaopeng Yan, Ziliang Chen, Dongyu Zhang, Lei Zhang},
    Title = {{ASM}: Cost-Effective Object Detection: Active Sample Mining with Switchable Selection Criteria},
    Journal = {IEEE Transactions on Neural Networks and Learning System(TNNLS)},
    Year = {2018}
}

Dependencies

The code is built on top of R-FCN. Please carefully read through py-R-FCN and make sure py-R-FCN can run within your enviornment.

Datasets/Pre-trained model

  1. In our paper, we used Pascal VOC2007/VOC2012 and COCO as our datasets, and res101.pth model as our pre-trained model.

  2. Please download ImageNet-pre-trained res101.pth model manually, and put them into $ASM_ROOT/data/imagenet_models

Usage

  1. training

    Before training, please prepare your dataset and pre-trained model and store them in the right path as R-FCN.You can go to ./tools/ and modify train_net.py to reset some parameters.Then, simply run sh ./train.sh.

  2. testing

    Before testing, you can modify test.sh to choose the trained model path, then simply run sh ./test.sh to get the evaluation result.

Misc

Tested on Ubuntu 14.04 with a Titan X GPU (12G) and Intel(R) Xeon(R) CPU E5-2623 v3 @ 3.00GHz.

Acknowledgement

Thanks for the contribution of Xiaoxi Wang.

About

Cost-Effective Object Detection: Active Sample Mining with Switchable Selection Criteria

Topics

Resources

Releases

No releases published

Packages

No packages published