Adversarial Complementary Learning for Weakly Supervised Object Localization

This is PyTorch implementation of Adversarial Complementary Learning for Weakly Supervised Object Localization

Prerequisites

• Python 3.6+
• Pytorch ( >= 1.1)
• Python bindings for OpenCV
• tqdm
• tensorboardX

Data Preparation

• ImageNet download link
• CUB-200-2011 download link

Execution

Train

git clone https://github.com/halbielee/ACoL_pytorch.git
wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz
tar -xvf CUB_200_2011.tgz
cd ACoL_pytorch
bash script/train.sh

Evaluate

# First download trained model from the link
bash script/evaluate.sh

Performance

We provide the performance with trained model.

Dataset	Method	Acc1	Acc5	Top1_LOC(0.15/0.2)	GT-known	condition
CUB-200	acol	76.46	92.44	49.78 / 46.06	59.46	batch 32, lr 0.001, wd 1e-4, 40/150, thr 0.7

Qualitative Image