ORNet

Code repository for our paper "Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization" in ICCV 2021 (Poster Presentation).

The repository includes full training, evaluation, and visualization codes for CUB-200-2011 and ILSVRC2012 datasets.

Dependencies

• Python 3
• Pytorch 1.0.0+
• OpenCV-Python
• Numpy
• Scipy
• MatplotLib
• Yaml
• Easydict

Dataset

CUB-200-2011

You will need to download the images (JPEG format) in CUB-200-2011 dataset at here. Make sure your data/CUB_200_2011 folder is structured as follows:

├── CUB_200_2011/
|   ├── images
|   ├── images.txt
|   ├── bounding_boxes.txt
|   ...
|   └── train_test_split.txt

You will need to download the images (JPEG format) in ILSVRC2012 dataset at here. Make sure your data/ILSVRC2012 folder is structured as follows:

ILSVRC2012

├── ILSVRC2012/
|   ├── train
|   ├── val
|   ├── val_boxes
|   |   ├——val
|   |   |   ├—— ILSVRC2012_val_00050000.xml
|   |   |   ├—— ...
|   ├── train.txt
|   └── val.txt

Training

Training (You can specify the desired settings in config/CUB_200_2011.yaml and config/ILSVRC2012.yaml, e.g., the data root):

On CUB-200-2011 dataset

Download the pretrained checkpoints at here and put them in the directory of debug/checkpoints/

├── debug/
|   ├── images
|   ├—— checkpoints
|   |   ├—— evaluator
|   |   ├—— cub_coarse_best_model
|   |   ├—— cub_fine_best_model
|   └── logs

Train the model

OMP_NUM_THREADS=16 CUDA_VISIBLE_DEVICES=0 python train_2nd_step.py --cfg config/CUB_200_2011.yaml --experiment cub_fine_model

The code will create experiment folders for model checkpoints (./debug/checkpoint), log files (./debug/log) and visualization (./debug/images/).

├── debug/
|   ├── checkpoints
|   ├—— images
|   |   ├—— cub_fine_model
|   |   |   ├—— train
|   |   |   ├—— test
|   |   ├—— ...
|   └── logs

use the last checkpoint for evaluation of second stage

OMP_NUM_THREADS=16 CUDA_VISIBLE_DEVICES=0 python train_2nd_step.py --cfg config/CUB_200_2011.yaml --experiment cub_fine_model --evaluate True

To reproduce the evaluation results presented in the paper.

OMP_NUM_THREADS=16 CUDA_VISIBLE_DEVICES=0 python train_2nd_step.py --cfg config/CUB_200_2011.yaml --experiment cub_fine_best_model --evaluate True

On ILSVRC2012 dataset (TODO)

Reference

If you are using our code, please consider citing our paper.

@InProceedings{Xie_2021_ICCV,
    author    = {Xie, Jinheng and Luo, Cheng and Zhu, Xiangping and Jin, Ziqi and Lu, Weizeng and Shen, Linlin},
    title     = {Online Refinement of Low-Level Feature Based Activation Map for Weakly Supervised Object Localization},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {132-141}
}