Junsuk Choe1,3*, Seong Joon Oh2*, Seungho Lee1,
Sanghyuk Chun3, Zeynep Akata4, Hyunjung Shim1
* Equal contribution
1 School of Integrated Technology, Yonsei University
2 Clova AI Research, LINE Plus Corp.
3 Clova AI Research, NAVER Corp.
4 University of Tübingen
Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal WSOL work of class activation mapping (CAM), the field has focused on how to expand the attention regions to cover objects more broadly and localize them better. However, these strategies rely on full localization supervision to validate hyperparameters and for model selection, which is in principle prohibited under the WSOL setup. In this paper, we argue that WSOL task is ill-posed with only image-level labels, and propose a new evaluation protocol where full supervision is limited to only a small held-out set not overlapping with the test set. We observe that, under our protocol, the five most recent WSOL methods have not made a major improvement over the CAM baseline. Moreover, we report that existing WSOL methods have not reached the few-shot learning baseline, where the full-supervision at validation time is used for model training instead. Based on our findings, we discuss some future directions for WSOL.
Overview of WSOL performances 2016-2019. Above image shows that recent improvements in WSOL are illusory due to (1) different amount of implicit full supervision through validation and (2) a fixed score-map threshold to generate object boxes. Under our evaluation protocol with the same validation set sizes and oracle threshold for each method, CAM is still the best. In fact, our few-shot learning baseline, i.e., using the validation supervision (10 samples/class) at training time, outperforms existing WSOL methods.
- 9 Jul, 2020: Journal submission available.
- 27 Mar, 2020: New WSOL evaluations with
MaxBoxAccV2are added. - 28 Feb, 2020: New box evaluation (
MaxBoxAccV2) is available. - 22 Jan, 2020: Initial upload.
- 1. Our dataset contribution
- 2. Dataset downloading and license
- 3. Code dependencies
- 4. WSOL evaluation
- 5. Library of WSOL methods
- 6. WSOL training and evaluation
- 7. Code license
- 8. How to cite
WSOL is an ill-posed problem when only image-level labels are available (see paper for an argument).
To be able to solve the WSOL task, certain amount of full supervision is inevitable, and prior WSOL approaches have utilized
different amount of implicit and explicit full supervision (usually through validation).
We propose to use a fixed amount of full supervision per method by carefully designing validation splits (called train-fullsup in the paper), such that different methods use the same amount of localization-labelled validation split.
In this section, we explain how each dataset is split, and introduce our data contributions (image collections and new annotations) on the way.
| split | ImageNet | CUB | OpenImages |
|---|---|---|---|
train-weaksup |
ImageNet "train" | CUB-200-2011 "train" | OpenImages30k "train"  |
train-fullsup |
ImageNetV2  |
CUBV2  |
OpenImages30k "val" |
test |
ImageNet "val" | CUB-200-2011 "test" | OpenImages30k "test" |
We propose three disjoint splits for every dataset: train-weaksup,
train-fullsup, and test. The train-weaksup contains images with weak
supervision (the image-level labels). The train-fullsup contains images with
full supervision (either bounding box or binary mask). It is left as freedom for
the user to utilize it for hyperparameter search, model selection, ablative
studies, or even model fitting. The test split contains images with full
supervision; it must be used only for the final performance report. For
example, checking the test results multiple times with different model
configurations violates the protocol as the learner implicitly uses more full
supervision than allowed. The splits and their roles are more extensively explained
in the paper.
- ImageNet
- "train" and "val" splits of original ImageNet
are treated as our
train-weaksupandtest. - ImageNetV2 is treated as our
train-fullsup. Note that we have annotated bounding boxes on ImageNetV2.
- "train" and "val" splits of original ImageNet
are treated as our
- CUB
- "train" and "test" splits of original
CUB-200-2011 are
treated as our
train-weaksupandtest. - We contribute images and annotations that are similar as the original CUB, namely CUBV2.
- "train" and "test" splits of original
CUB-200-2011 are
treated as our
- OpenImages
- We curate the existing OpenImagesV5 for the task of WSOL.
- We have randomly selected images from the original "train", "val", and "test" splits of the instance segmentation subset.
For original ImageNet and CUB datasets, please follow the common procedure to download the datasets. In this section, we only explain how to obtain the less used (or never used before) datasets. We also provide the license status for each dataset. This section is for those who are interested in the full data for each dataset. If the aim is to utilize the data for WSOL evaluation and/or training, please follow the links below:
We utilize 10,000 images in the Threshold0.7 split of
ImageNetV2 for our train-fullsup
split. We have annotated bounding boxes on those images.
Box labels exist in here and are licensed by NAVERCorp. under
Attribution 2.0 Generic (CC-BY-2.0).
We have collected and annotated CUBV2 on our own as the train-fullsup split.
We have ensured that the data distribution
follows the original CUB dataset and there is no duplicate image.
We have collected 5 images per class
(1,000 images total) from Flickr.
Box labels and license files of all images exist in here.
Both class and box labels are licensed by NAVERCorp under
Attribution 2.0 Generic (CC-BY-2.0).
Download images
Download segmentation masks
The WSOL community has relied on ImageNet and CUB datasets at least for the last three years. It is perhaps time for us to move on. We provide a WSOL benchmark based on the OpenImages30k dataset to provide a new perspective on the generalizability of WSOL methods in the past and future. To make it suitable for the WSOL task, we use 100 classes to ensure the minimum number of single-class samples for each class. We have randomly selected 29,819, 2,500, and 5,000 images from the original "train", "val", and "test" splits of OpenImagesV5. Corresponding metadata can be found in here. The annotations are licensed by Google LLC under Attribution 4.0 International (CC-BY-4.0). The images are listed as having a Attribution 2.0 Generic (CC-BY-2.0).
Below tables summarizes dataset statistics of each split.
| # images/classes | ImageNet 1,000 classes | CUB 200 classes | OpenImages 100 classes |
|---|---|---|---|
train-weaksup |
~1,200 | ~30 | ~300 |
train-fullsup |
10 | ~5 | 25 |
test |
10 | ~29 | 50 |
The licenses corresponding to our dataset contribution are summarized as follows
| Dataset | Images | Class Annotations | Localization Annotations |
|---|---|---|---|
| ImageNetV2 | See the original Github | See the original Github | CC-BY-2.0 NaverCorp. |
| CUBV2 | Follows original image licenses. See here. | CC-BY-2.0 NaverCorp. | CC-BY-2.0 NaverCorp. |
| OpenImages | CC-BY-2.0 (Follows original image licenses. See here) | CC-BY-4.0 Google LLC | CC-BY-4.0 Google LLC |
Detailed license files are summarized in the release directory.
Note: At the time of collection, images were marked as being licensed under the following licenses:
Attribution-NonCommercial License
Attribution License
Public Domain Dedication (CC0)
Public Domain Mark
However, we make no representations or warranties regarding the license status of each image. You should verify the license for each image yourself.
Both the evaluation-only and eval+train scripts require only the following libraries:
pip freeze returns the version information as below:
munch==2.5.0
numpy==1.18.1
opencv-python==4.1.2.30
Pillow==7.0.0
six==1.14.0
torch==1.4.0
torchvision==0.5.0
We support evaluation of weakly-supervised object localization (WSOL) methods
on CUB, ImageNet, and OpenImages. The main script for evaluation is
evaluation.py. We will show how to download the train-fullsup
(validation) and test set images and localization annotations.
An example evaluation script will be provided.
WSOL evaluation data consist of images and corresponding localization ground truths. On CUB and ImageNet, they are given as boxes, and on OpenImages, they are given as binary masks.
To prepare evaluation data, first, download ImageNet "val" split from
here and put the downloaded file on
dataset/ILSVRC2012_img_val.tar.
Then, run the following command
./dataset/prepare_evaluation_data.shThe script will download the train-fullsup (validation) and test images at
dataset. Metadata and box annotations already exist in this repository
under metadata. OpenImages mask annotations are also downloaded by
the above script, and will be saved under dataset with the images.
The structure of image files looks like
dataset
└── ILSVRC
└── val2
└── 0
├── 0.jpeg
├── 1.jpeg
└── ...
└── 1
└── ...
└── val
├── ILSVRC2012_val_00000001.JPEG
├── ILSVRC2012_val_00000002.JPEG
└── ...
└── CUB
└── 001.Black_footed_Albatross
├── Black_Footed_Albatross_0046_18.jpg
├── Black_Footed_Albatross_0002_55.jpg
└── ...
└── 002.Laysan_Albatross
└── ...
└── OpenImages
└── val
└── 0bt_c3
├── 1cd9ac0169ec7df0.jpg
├── 1cd9ac0169ec7df0_ignore.png
├── 1cd9ac0169ec7df0_m0bt_c3_6932e993.png
└── ...
└── 0bt9lr
└── ...
└── test
└── 0bt_c3
├── 0a51958fcd523ae4.jpg
├── 0a51958fcd523ae4_ignore.png
├── 0a51958fcd523ae4_m0bt_c3_41344f12.png
├── 0a51958fcd523ae4_m0bt_c3_48f37c0f.png
└── ...
└── 0bt9lr
└── ...
Our WSOL evaluation evaluates heatmaps of the same width and height as the input images. The evaluation script requires the heatmaps to meet the following criteria:
- Heatmap file structure.
- Heatmaps shall be located at the user-defined
<heatmap_root>. <heatmap_root>folder contains the heatmap files with the file names dictated by themetadata/<dataset>/<split>/image_ids.txtfiles.- If an
image_idhas slashes (/), e.g.val2/995/0.jpeg, then the corresponding heatmaps shall be located at the corresponding sub-directories, e.g.<heatmap_root>/val2/995/0.npy.
- Heatmap data type.
- Each heatmap file should be a
.npyfile that can be loaded as a numpy array withnumpy.load(). - The array shall be two-dimensional array of shape
(height, width), same as the input image sizes. - The array shall be of type
np.float. - The array values must be between 0 and 1.
We support three datasets, CUB, ImageNet, and OpenImages.
On CUB and ImageNet, we evaluate the MaxBoxAcc, the maximal box accuracy at
the optimal heatmap threshold, where the box accuracy is measured by the ratio
of images where the box generated from the heatmap overlaps with the ground
truth box with IoU at least 0.5. Please see the code and paper for the full
details.
On OpenImages, we evaluate the PxAP, pixel average precision. We generate
the pixel-wise precision-recall curve, and compute the area under the curve.
Please see the code and paper for the full details.
We present an example call to the evaluation API below:
python evaluation.py --scoremap_root=train_log/scoremaps/ \
--metadata_root=metadata/ \
--mask_root=dataset/ \
--dataset_name=CUB \
--split=val \
--cam_curve_interval=0.01When CUB evaluation data are downloaded at dataset using our download script
above, and the corresponding heatmaps are saved under train_log/scoremaps/,
then the MaxBoxAcc will be evaluated as a result of this call.
The test code for the evaluation modules is given at evaluation_test.py. The unit tests ensure the correctness of the evaluation logic, and potentially prevents unnoticed changes in the functionalities of underlying libraries (e.g. OpenCV, Numpy). To run the unit test, run
nosetestspip3 install nose may be required to install nose.
We support the training and evaluation of the following weakly-supervised object localization (WSOL) methods. Our implementation of the methods can be found in the wsol folder. Please add your own WSOL method in the list by making a pull request.
We provide the full training and evaluation scripts on the provided WSOL methods. Details will be explained in the next section.
| Method | Paper | Original code |
|---|---|---|
| Class-Activation Mapping (CAM) | CVPR'16 | Code |
| Hide-and-Seek (HaS) | ICCV'17 | Code |
| Adversarial Complementary Learning (ACoL) | CVPR'18 | Code |
| Self-Produced Guidance (SPG) | ECCV'18 | Code |
| Attention-based Dropout Layer (ADL) | CVPR'19 | Code |
| CutMix | ICCV'19 | Code |
Evaluation of WSOL methods. How much have WSOL methods improved upon the vanilla CAM
model? MaxBoxAccV2 and PxAP performances over the test split are shown, relative to the vanilla CAM performance.
We recommend the MaxBoxAccV2 over the original box metric MaxBoxAcc used in the CVPR version.
For details, see the latest arXiv version.
Hyperparameters have been optimized over the identical train-fullsup split for
all WSOL methods and the FSL baseline: (10,5,5) full supervision/class for
(ImageNet,CUB,OpenImages). Note that we evaluate the last checkpoint of each
training session. More detailed results and corresponding hyperparameter sets
are available at
here.
We describe the data preparation and training scripts for the above six prior WSOL methods.
Our repository enables evaluation and training of WSOL methods on two commonly-used benchmarks, CUB and ImageNet, and our newly-introduced benchmark OpenImages. We describe below how to prepare those datasets.
Both the original ImageNet and
ImageNetV2 are required for WSOL
training. Note that "val" split of the original ImageNet is considered as test
split, and ImageNetV2 is used for split (train-fullsup) in our framework.
To prepare ImageNet data, download ImageNet "train" and "val" splits from
here and put the downloaded file on
dataset/ILSVRC2012_img_train.tar and dataset/ILSVRC2012_img_val.tar.
Then, run the following command on root directory to extract the images.
./dataset/prepare_imagenet.sh
apt-get install parallel may be required to install parallel.
The structure of image files looks like
dataset
└── ILSVRC
└── train
└── n01440764
├── n01440764_10026.JPEG
├── n01440764_10027.JPEG
└── ...
└── n01443537
└── ...
└── val2
└── 0
├── 0.jpeg
├── 1.jpeg
└── ...
└── 1
└── ...
└── val
├── ILSVRC2012_val_00000001.JPEG
├── ILSVRC2012_val_00000002.JPEG
└── ...
Corresponding annotation files can be found in here.
Both the original
CUB-200-2011
and our CUBV2 datasets are required for WSOL training. Note that CUBV2 is
considered as a validation split (train-fullsup). Then, run the following
command to download original CUB dataset and extract the image files on root
directory.
./dataset/prepare_cub.sh
Note: you can also download the CUBV2 dataset from
here. Put
the downloaded file on dataset/CUBV2.tar directory and then run the above script.
The structure of image files looks like
dataset
└── CUB
└── 001.Black_footed_Albatross
├── Black_Footed_Albatross_0001_796111.jpg
├── Black_Footed_Albatross_0002_55.jpg
└── ...
└── 002.Laysan_Albatross
└── ...
Corresponding annotation files can be found in here.
We provide a new WSOL benchmark, OpenImages30k, based on OpenImagesV5.
To download and extract files, run the following command on root directory
./dataset/prepare_openimages.sh
Note: you can also download the OpenImages30k dataset from here
(images
, masks).
Put the downloaded OpenImages_images.zip and OpenImages_annotations.zip
files in dataset directory and run the above script.
The structure of image files looks like:
dataset
└── OpenImages
└── train
└── 0bt_c3
├── 0a9b7df4d832baf7.jpg
├── 0abee225b2418fe7.jpg
└── ...
└── 0bt9lr
└── ...
└── val
└── 0bt_c3
├── 1cd9ac0169ec7df0.jpg
├── 1cd9ac0169ec7df0_ignore.png
├── 1cd9ac0169ec7df0_m0bt_c3_6932e993.png
└── ...
└── 0bt9lr
└── ...
└── test
└── 0bt_c3
├── 0a51958fcd523ae4.jpg
├── 0a51958fcd523ae4_ignore.png
├── 0a51958fcd523ae4_m0bt_c3_41344f12.png
├── 0a51958fcd523ae4_m0bt_c3_48f37c0f.png
└── ...
└── 0bt9lr
└── ...
Corresponding annotation files can be found in here.
We support the following architecture and method combinations:
-
Architectures.
vgg16inception_v3resnet50
-
Methods (see Library of WSOL methods and paper for descriptions).
camhasacolspgadlcutmix
Below is an example command line for the train+eval script.
python main.py --dataset_name OpenImages \
--architecture vgg16 \
--wsol_method cam \
--experiment_name OpenImages_vgg16_CAM \
--pretrained TRUE \
--num_val_sample_per_class 5 \
--large_feature_map FALSE \
--batch_size 32 \
--epochs 10 \
--lr 0.00227913316 \
--lr_decay_frequency 3 \
--weight_decay 5.00E-04 \
--override_cache FALSE \
--workers 4 \
--box_v2_metric True \
--iou_threshold_list 30 50 70 \
--eval_checkpoint_type lastSee config.py for the full descriptions of the arguments, especially the method-specific hyperparameters.
During training, we evaluate the model on train-fullsup split at every epoch and
save a checkpoint (best_checkpoint.pth.tar) if the localization performance
surpasses every previous score. We also save last checkpoint
(last_checkpoint.pth.tar) when the training is finished. You can select
checkpoint type for evaluation on test split by setting eval_checkpoint_type
argument accordingly. We suggest to use the last checkpoint for evaluation.
We introduce an improved box evaluation metric, MaxBoxAccV2, over the original metric used in the CVPR version: MaxBoxAcc.
Key improvements are as follows:
-
Box evaluation using multiple IoU thresholds (default: 30%, 50%, 70%). If you set
multi_iou_evaltoTrue(default), thelocalizationmetric in the log shows a mean ofMaxBoxAccacross all IoU thresholds. Otherwise, it only showsMaxBoxAccat 50% IoU threshold. The IoU threshold list can be easily set by changingiou_threshold_listargument. -
A new advanced bounding box mining scheme. Bounding boxes are extracted from all contours in the thresholded score map. You can use this feature by setting
multi_contour_evaltoTrue(default). Otherwise, bounding boxes are extracted from the largest connected component of the score map.
We recommend for future researchers to use the MaxBoxAccV2 metric for box-based evaluation.
Users can evaluate the WSOL methods with this metric by setting box_v2_metric to True.
This project is distributed under MIT license.
Copyright (c) 2020-present NAVER Corp.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
@inproceedings{choe2020cvpr,
title={Evaluating Weakly Supervised Object Localization Methods Right},
author={Choe, Junsuk and Oh, Seong Joon and Lee, Seungho and Chun, Sanghyuk and Akata, Zeynep and Shim, Hyunjung},
year = {2020},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
note = {to appear},
pubstate = {published},
tppubtype = {inproceedings}
}
@ARTICLE{choe2022tpami,
author={Choe, Junsuk and Oh, Seong Joon and Chun, Sanghyuk and Lee, Seungho and Akata, Zeynep and Shim, Hyunjung},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets},
year={2022},
volume={},
number={},
pages={1-1},
doi={10.1109/TPAMI.2022.3169881}}