M2Det_obj365

M2Det for objects365 dataset. The code based on original M2Det

Contents

• Data Preparation and Installation

• Demo(preparing...)

• Evaluation(preparing...)

• Training

Preparation

the supported version is pytorch-0.4.1

• Prepare python environment using Anaconda3.
• Install deeplearning framework, i.e., pytorch, torchvision and other libs.

conda install pytorch torchvision -c pytorch
pip install opencv-python,tqdm

• Clone this repository.

git clone https://github.com/ryota717/M2Det_obj365.git

• Compile the nms and coco tools:

sh make.sh

• Prepare objects365 dataset and put them on /home/data, as shown in below.

/home/
      ┣ data/
            ┣ objects365/
                ┣ annotations/
                    ┣ instances_train.json
                    ┣ instances_val.json
                ┣ images/
                    ┣ train/
                        ┣ 〇〇〇〇.jpg
                        ┣ △△△△.jpg
                    ┣ val/
                        ┣ 〇〇〇〇.jpg
                        ┣ △△△△.jpg

Demo(preparing...)

Evaluation(preparing...)

Training

As simple as demo and evaluation, Just use the train script:

  CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py -c=configs/m2det800_resnext.py --ngpu 4 -t True

When you use VGG as backbone, download pre-trained model from here and put it "M2Det_obj365/weights/". All training configs and model configs are written well in configs/*.py.

Citation:

Please cite the following paper if you feel M2Det useful to your research

@inproceedings{M2Det2019aaai,
  author    = {Qijie Zhao and
               Tao Sheng and
               Yongtao Wang and
               Zhi Tang and
               Ying Chen and
               Ling Cai and
               Haibing Lin},
  title     = {M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network},
  booktitle   = {The Thirty-Third AAAI Conference on Artificial Intelligence,AAAI},
  year      = {2019},
}