https://github.com/lartpang/hdfnet
(ECCV 2020) Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection
https://github.com/lartpang/hdfnet
eccv eccv2020 image-processing imageprocessing imagesegmentation pytorch saliency saliency-detection salient-object-detection segmentation
Last synced: 8 months ago
JSON representation
(ECCV 2020) Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection
- Host: GitHub
- URL: https://github.com/lartpang/hdfnet
- Owner: lartpang
- License: mit
- Created: 2020-07-03T15:21:42.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-09-14T05:18:42.000Z (over 2 years ago)
- Last Synced: 2025-07-04T12:07:46.965Z (9 months ago)
- Topics: eccv, eccv2020, image-processing, imageprocessing, imagesegmentation, pytorch, saliency, saliency-detection, salient-object-detection, segmentation
- Language: Python
- Homepage:
- Size: 1.63 MB
- Stars: 89
- Watchers: 3
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# HDFNet
[](https://arxiv.org/abs/2007.06227)
(ECCV 2020) Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection
Gitee Mirror:
> Author: Lart Pang(`lartpang@163.com`)
>
> This is a complete, modular and easily modified code base based on PyTorch, which is suitable for the training and testing of significant target detection task model.
```text
@inproceedings{HDFNet-ECCV2020,
author = {Youwei Pang and Lihe Zhang and Xiaoqi Zhao and Huchuan Lu},
title = {Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection},
booktitle = ECCV,
year = {2020}
}
```
**News**:
* The proposed model (HDFNet) is an important baseline of the winning solution in NTIRE 2021 (Depth Guided Image Relighting Challenge) hosted in CVPR 2021 workshop (winner: AICSNTU-MBNet team (Asustek Computer Inc & National Taiwan University)). [[PAPER](https://arxiv.org/pdf/2105.00690.pdf)] [[COOD](https://github.com/weitingchen83/NTIRE2021-Depth-Guided-Image-Relighting-MBNet)]
**NOTE**:
* In the link below, we provide the results of the two versions (with/without `_STEREO`).
* Specifically, in the file with `_STEREO`, two versions of the STEREO dataset are provided.
* One of them contains 797 pictures, and the other contains 1000 pictures.
* In our paper, the results evaluated on the latter are used, which is exactly what is provided in the file without `_STEREO`.
[[Results & PretrainedParams (j9qu)](https://pan.baidu.com/s/1hExlf0uZ0kuar99xzpL0Sw)]
* RGBD-DATASET
* https://github.com/jiwei0921/RGBD-SOD-datasets
* http://dpfan.net/d3netbenchmark/
## Repository Details
* `backbone`: Store some code for backbone networks.
* `loss`: The code of the loss function.
* `module`: The code of important modules.
* `network`: The code of the network.
* `output`: It saves all results.
* `utils`: Some instrumental code.
* `data/*py`: Some files about creating the dataloader.
* `transforms/*py`: Some operations on data augmentation.
* `metric.py`: max/mean/weighted F-measure, S-measure, E-measure and MAE. (**NOTE: If you find a problem in this part of the code, please notify me in time, thank you.**)
* `misc.py`: Some useful utility functions.
* `tensor_ops.py`: Some operations about tensors.
* `config.py`: Configuration file for model training and testing.
* `train.py`: I think you can understand.
* `test.py` and `test.sh`: These files can evaluate the performance of the model on the specified dataset. And the file `test.sh` is a simple example about how to configure and run `test.py`.
## Usage
### Environment
I provided conda environment configuration file (hdfnet.yaml), you can refer to the package version information.
And you can try `conda env create -f hdfnet.yaml` to create an environment to run our code.
### Train your own model
* Add your own module into the `module`.
* Add your own network into the `network` and import your model in the `network/__init__.py`.
* Modify `config.py`:
* change the dataset path: `datasets_root`
* change items in `arg_config`
* `model` corresponds to the name of the model in `network`
* `suffix`: finally, the form of `_` is used to form the alias of the model of this experiment and all files related to this experiment will be saved to the folder `_` in `output` folder
* `resume`: set it to `False` to train normally
* `data_mode`: set it to `RGBD` or `RGB` for using RGBD SOD datasets or RGB SOD datasets to train mdoel.
* other items, like `lr`, `batch_size` and so on...
* Run the script: `python train.py`
If the training process is interrupted, you can use the following strategy to resume the training process.
* Set `resume` to `True`.
* Run the script `train.py` again.
### Evaluate model performance
There are two ways:
1. For models that have been trained, you can set `resume` to `True` and run the script `train.py` again.
2. Use the scripts `test.sh` and `test.py`. The specific method of use can be obtained by executing this command: `python test.py --help`.
### Only evaluate generated predictions
You can use the toolkit released by us: .
## Related Works
* (ECCV 2020 Oral) Suppress and Balance: A Simple Gated Network for Salient Object Detection: https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency
* (ECCV 2020) A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection: https://github.com/Xiaoqi-Zhao-DLUT/DANet-RGBD-Saliency
* (CVPR 2020) Multi-scale Interactive Network for Salient Object Detection: https://github.com/lartpang/MINet