SSM

Towards Human-Machine Cooperation: Evolving Active Learning with Self-supervised Process for Object Detection

License

SSM is released under the MIT License (refer to the LICENSE file for details).

Citing SSM

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

@article{wang18ssm,
    Author = {Keze Wang, Xiaopeng Yan, Dongyu Zhang, Lei Zhang, Liang Lin},
    Title = {{SSM}: Towards Human-Machine Cooperation: Evolving Active Learning with Self-supervised Process for Object Detection},
    Journal = {Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    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 ResNet-101 model as our pre-trained model.

2. Please download ImageNet-pre-trained ResNet-101 model manually, and put them into $SSM_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.