w2n_wsod

Official implementation of the paper ``W2N: Switching From Weak Supervision to Noisy Supervision for Object Detection"

Overall

This code release the implementation of inference phase for W2N. The full training code and data procession scripts will be integrated and released after the code review.

We implemented the code based on detectron2 toolkit and unbiased teacher. Sincerely thanks for your resources.

Hardware

We use 8 RTX 1080Ti GPU (11GB) to train and evaluate our method, GPU with larger memory is better (e.g., TITAN RTX with 24GB memory)

Requirements

• Python 3.6 or higher
• CUDA 10.2 with cuDNN 7.6.5
• PyTorch 1.6.0
• numpy 1.19.2
• opencv 4.5.1

Additional resources

Datasets

For example, PASCAL VOC 2007 dataset

1. Download the training, validation, test data and VOCdevkit

   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
   wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar

2. Extract all of these tars into one directory named VOCdevkit

   tar xvf VOCtrainval_06-Nov-2007.tar
   tar xvf VOCtest_06-Nov-2007.tar
   tar xvf VOCdevkit_08-Jun-2007.tar

3. It should have this basic structure

   $VOCdevkit/                           # development kit
   $VOCdevkit/VOCcode/                   # VOC utility code
   $VOCdevkit/VOC2007                    # image sets, annotations, etc.
   # ... and several other directories ...
   

4. Create symlinks for the PASCAL VOC dataset

   cd $PROJECTS_ROOT
   mkdir datasets
   cd datasets
   ln -s $VOCdevkit/VOC2007 VOC2007

Pretrained Model

OICR+REG+W2N on PASCAL VOC 2007

CASD+W2N on PASCAL VOC 2007

LBBA+W2N on PASCAL VOC 2007

Inference

python train_net.py --eval-only --config-file=configs\pascal_voc_no_labeled\faster_rcnn_R_50_FPN_pascasl_unlabeled_reg.yaml --num-gpus=8 MODEL.WEIGHTS model_w2n_lbba.pth