https://github.com/acecoooool/nldf-pytorch
An unofficial implementation of non-local deep features for salient object detection
https://github.com/acecoooool/nldf-pytorch
python pytorch salient-object-detection
Last synced: about 1 year ago
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An unofficial implementation of non-local deep features for salient object detection
- Host: GitHub
- URL: https://github.com/acecoooool/nldf-pytorch
- Owner: AceCoooool
- Created: 2017-12-31T14:10:54.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-01-19T07:25:45.000Z (over 7 years ago)
- Last Synced: 2025-04-01T12:04:10.781Z (about 1 year ago)
- Topics: python, pytorch, salient-object-detection
- Language: Python
- Size: 343 KB
- Stars: 39
- Watchers: 2
- Forks: 10
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# NLDF
[中文说明](./README.zh.md)
An unofficial implementation of [Non-Local Deep Features for Salient Object Detection](https://sites.google.com/view/zhimingluo/nldf).
The official Tensorflow version: [NLDF](https://github.com/zhimingluo/NLDF)
Some thing difference:
1. ~~dataset~~
2. score with one channel, rather than two channels
3. Dice IOU: boundary version and area version
## Prerequisites
- [Python 3](https://www.continuum.io/downloads)
- [Pytorch 1.0](http://pytorch.org/)
- [torchvision](http://pytorch.org/)
- [visdom](https://github.com/facebookresearch/visdom) (optional for visualization)
## Results
The information of Loss:
Performance:
| Dataset | max F(paper) | MAE(paper) | max F(here) | MAE(here) |
| :-----: | :----------: | :--------: | :---------: | :-------: |
| MSRA-B | 0.911 | 0.048 | 0.9006 | 0.0592 |
Note:
1. only training 200 epoch, larger epoch may nearly the original paper
2. This reproduction use area IOU, and original paper use boundary IOU
3. ~~it's unfairness to this compare. (Different training data, I can not find the dataset use in original paper )~~
## Usage
### 1. Clone the repository
```shell
git clone git@github.com:AceCoooool/NLDF-pytorch.git
cd NLDF-pytorch/
```
### 2. Download the dataset
Note: the original paper use other datasets.
Download the [ECSSD](http://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html) dataset.
```shell
bash download.sh
```
### 3. Get pre-trained vgg
```bash
cd tools/
python extract_vgg.py
cd ..
```
### 4. Demo
```shell
python demo.py --demo_img='your_picture' --trained_model='pre_trained pth' --cuda=True
```
Note:
1. default choose: download and copy the [pretrained model](https://drive.google.com/file/d/10cnWpqABT6MRdTO0p17hcHornMs6ggQL/view?usp=sharing) to `weights` directory.
2. a demo picture is in `png/demo.jpg`
### 5. Train
```shell
python main.py --mode='train' --train_path='you_data' --label_path='you_label' --batch_size=8 --visdom=True --area=True
```
Note:
1. `--area=True, --boundary=True` area and boundary Dice IOU (default: `--area=True --boundary=False`)
2. `--val=True` add the validation (but your need to add the `--val_path` and `--val_label`)
3. `you_data, you_label` means your training data root. (connect to the step 2)
### 6. Test
```shell
python main.py --mode='test', --test_path='you_data' --test_label='your_label' --batch_size=1 --model='your_trained_model'
```
Note:
1. use the same evaluation (this is a reproduction from original achievement)
## Bug
1. The boundary Dice IOU may cause `inf`，it is better to use area Dice IOU.
Maybe, it is better to add Batch Normalization.