Weakly Supervised Object Detection with Symmetry Context

This is a Pytorch implementation of our WSODSC.

Introduction

WSODSC is a framework for weakly supervised object detection with deep ConvNets.

Our code is written based on PyTorch and wetectron.

Sincerely thanks for your recources.

Installation

Requirements:

• Python 3
• Pytorch 1.5+
• torchvision

We follow the same installation steps with wetectron.

• Check INSTALL.md for installation instructions.

Datasets

For PASCAL VOC 2007 dataset, download and then

mkdir -p datasets/voc
ln -s /path_to_VOCdevkit/VOC2007 datasets/voc/VOC2007

Proposals

Download the proposals from Google-drive first,

Description

• selective_search_data: precomputed proposals of VOC 2007
• context_proposals: precomputed context proposals
• pretrained_models

mkdir proposal
ln -s  /path/to/downloaded/files/*.pkl proposal/

Training

export NGPUS=1
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_net.py \
    --config-file "configs/ctx/mist-reg-ctx.yaml" --use-tensorboard \
    OUTPUT_DIR /path/to/output/dir

Evaluation

The pretrained models can be found at Google-drive. Note that all these model were trained using 1 Nvidia V100 GPU(32GB) and Pytorch 1.7.

voc models

Train data	Eval data	Config	Backbone	mAP
voc 2007	voc 2007 test	ctx/mist-reg-ctx.yaml	VGG-16	54.2
voc 2012	voc 2012 test	voc/mist-reg-ctx-voc12.yaml	VGG-16	48.3

Here is an example to evaluate the released model:

export NGPUS=1
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_net.py \
    --config-file "configs/ctx/mist-reg-ctx.yaml" TEST.IMS_PER_BATCH 8 \
    OUTPUT_DIR /path/to/output/dir \
    MODEL.WEIGHT /path/to/model

License

This code is released under the Nvidia Source Code License.

This project is built upon wetectron, which is released under Nvidia Source Code License.