mbodenham / CPD-python3

Python 3 Code of Cascaded Partial Decoder for Fast and Accurate Salient Object Detection (CVPR2019)

Cascaded Partial Decoder for Fast and Accurate Salient Object Detection (CVPR2019)

https://drive.google.com/file/d/1gBFWGrjt0hBIW1I9IOoUJYTF-C1CwPfr/view?usp=sharing

Documentation

CPD ranks first in the challenging SOC benchmark [2019.11.6].

Requirements:

Python 3.8.2

PyTorch 1.5.0

TorchVision 0.6.0

Usage

Datsets

Datasets to be set in the following directory format:

.
├── datasets
    ├── test
    │   ├── DUTS-TE
    │   │   ├── gts
    │   │   │   ├── ILSVRC2012_test_00000003.jpg
    │   │   │   ...
    │   │   │   └── sun_ekmqudbbrseiyiht.jpg
    │   │   └── imgs
    │   │       ├── ILSVRC2012_test_00000003.png
    │   │       ...
    │   │       └── sun_ekmqudbbrseiyiht.png
    │   ├── PASCAL-S
    │       ├── gts
    │       │   ├── 1.jpg
    │       │   ...
    │       │   └── 850.jpg
    │       └── imgs
    │           ├── 1.png
    │           ...
    │           └── 850.png
    └── train
        └── DUTS-TR
            ├── gts
            │   ├── ILSVRC2012_test_00000004.jpg
            │   ...
            │   └── sun_dzkggnowaqnfrorl.jpg
            └── img
                ├── ILSVRC2012_test_00000004.jpg
                │   ...
                └── sun_dzkggnowaqnfrorl.jpg

Training

Run train.py to train a new model. All models a saved to ./models/<model_name>

usage: train.py [-h] [--datasets_path DATASETS_PATH] [--device {cuda,cpu}]
                [--attention] [--imgres IMGRES] [--epoch EPOCH] [--lr LR]
                [--batch_size BATCH_SIZE] [--clip CLIP]
                [--decay_rate DECAY_RATE] [--decay_epoch DECAY_EPOCH]

optional arguments:
  -h, --help            show this help message and exit
  --datasets_path DATASETS_PATH
                        path to datasets, default = ./datasets/train
  --device {cuda,cpu}   use cuda or cpu, default = cuda
  --attention           use attention branch model
  --imgres IMGRES       image input and output resolution, default = 352
  --epoch EPOCH         number of epochs, default = 100
  --lr LR               learning rate, default = 0.0001
  --batch_size BATCH_SIZE
                        training batch size, default = 10
  --clip CLIP           gradient clipping margin, default = 0.5
  --decay_rate DECAY_RATE
                        decay rate of learning rate, default = 0.1
  --decay_epoch DECAY_EPOCH
                        every n epochs decay learning rate, default = 50

Testing

Pre-trained model should be placed in the root directory. Run test.py to produce ground truths.

usage: test.py [-h] [--datasets_path DATASETS_PATH] [--save_path SAVE_PATH]
               [--pth PTH] [--attention] [--imgres IMGRES]

optional arguments:
  -h, --help            show this help message and exit
  --datasets_path DATASETS_PATH
                        path to datasets, default = ./datasets/test
  --save_path SAVE_PATH
                        path to save results, default = ./results
  --pth PTH             model filename, default = CPD.pth
  --attention           use attention branch model
  --imgres IMGRES       image input and output resolution, default = 352

Models

As cited in the paper the following model architectures are as follows.

Model	Backbone	Attention Branch Only
CPA	VGG16
CPA-A	VGG16	X
CPD-R	ResNet50
CPD-RA	ResNet50	X

Pre-trained model

VGG16 backbone: google drive, BaiduYun (code: gb5u)

ResNet50 backbone: google drive, BaiduYun (code: klfd)

Pre-computed saliency maps

VGG16 backbone: google drive

ResNet50 backbone: google drive

Performance

Maximum F-measure

Model	FPS	ECSSD	HKU-IS	DUT-OMRON	DUTS-TEST	PASCAL-S
PiCANet	7	0.931	0.921	0.794	0.851	0.862
CPD	66	0.936	0.924	0.794	0.864	0.866
CPD-A	105	0.928	0.918	0.781	0.854	0.859
PiCANet-R	5	0.935	0.919	0.803	0.860	0.863
CPD-R	62	0.939	0.925	0.797	0.865	0.864
CPD-RA	104	0.934	0.918	0.783	0.852	0.855

MAE

Model	ECSSD	HKU-IS	DUT-OMRON	DUTS-TEST	PASCAL-S
PiCANet	0.046	0.042	0.068	0.054	0.076
CPD	0.040	0.033	0.057	0.043	0.074
CPD-A	0.045	0.037	0.061	0.047	0.077
PiCANet-R	0.046	0.043	0.065	0.051	0.075
CPD-R	0.037	0.034	0.056	0.043	0.072
CPD-RA	0.043	0.038	0.059	0.048	0.077

Shadow Detection

pre-computed maps: google drive

Performance

BER

Model	SBU	ISTD	UCF
DSC	5.59	8.24	8.10
CPD	4.19	6.76	7.21

Citation

@InProceedings{Wu_2019_CVPR,
author = {Wu, Zhe and Su, Li and Huang, Qingming},
title = {Cascaded Partial Decoder for Fast and Accurate Salient Object Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

Changelog

1.2 - 2019-05-31

Added

• model/CPD_model.py - VGG16 model with only the attention branch

Changed

• train.py - Now able to train attention only model
• train.py - Organised parse argument names to be more clear

Fixed

• gts_folder.py - Error with os.walk

1.1 - 2019-05-30

Added

• gts_folder.py - New torchvision dataset. Loads images from multiple datasets
• train.py - Dataset root parser argument
• train.py - CUDA parser argument

Changed

• train.py - train_loader now uses new trochvison dataset

1.0 - 2019-05-26

Added

• vgg.py - PyTorch Hub to fetch pre-trained VGG-16 model
• test_CPD.py - Use CUDA only if available
• test_CPD.py - Progress print out

Changed

• test_CPD.py - Corrected dataset paths

Fixed

• test_CPD.py - Replaced deprecated torch.nn.functional.upsample with torch.nn.functional.interpolate
• test_CPD.py - Replaced deprecated scipy.misc.imsave with torchvision.utils.save_image
• CPD_models.py - Relative import in Python 3
• CPD_ResNet_models.py - Relative import in Python 3