C2AM (Unsupervised)

November 5, 2024 · View on GitHub

Update (2022-12-12)

Method	Loc Back.	Cls Back.	CUB (top1/top5 loc)	CUB (GT-Known)	ImageNet (top1/top5 loc)	ImageNet (GT-Known)
ORNet	VGG16	VGG16	67.74 / 80.77	86.20	52.05 / 63.94	68.27
PSOL	ResNet50	ResNet50	70.68 / 86.64	90.00	53.98 / 63.08	65.44
C2AM (supervised initialization)	ResNet50	ResNet50	76.36 / 89.15	93.40	54.41 / 64.77	67.80
C2AM (unsupervised initialization)	ResNet50	ResNet50	74.76 / 87.37	91.54	54.65 / 65.05	68.07

We provide the extracted class-agnostic bounding boxes (on CUB-200-2011 and ILSVRC2012) and background cues (on PASCAL VOC12) from here.

Dependencies

Python 3
Paddlepaddle 2.1.0
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 from 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 from 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

PASCAL VOC2012

You will need to download the images (JPEG format) in PASCAL VOC2012 dataset from here. Make sure your data/VOC2012 folder is structured as follows:

├── VOC2012/
|   ├── Annotations
|   ├── ImageSets
|   ├── SegmentationClass
|   ├── SegmentationClassAug
|   └── SegmentationObject

For WSOL task

please refer to the directory of './WSOL'

cd WSOL

For WSSS task

please refer to the directory of './WSSS'

cd WSSS

As CCAM is an unsupervised method, it can be applied to various scenarios, like ReID, Saliency detection, or skin lesion detection. We provide an example to apply CCAM on your custom dataset like 'Market-1501'.

cd CUSTOM