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

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

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

It should have this basic structure

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

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

1170300714/w2n_wsod