
# ✨ PanCollection

🤗 To get started with PanCollection benchmark (training, inference, etc.), we recommend reading [Google Colab](https://colab.research.google.com/drive/1KpWWj1lVUGllZCws01zQfd6CeURuGL2O#scrollTo=k53dsFhAdp6n)!


## Recommendations

We recommend users to use the code-toolbox [DLPan-Toolbox](https://github.com/liangjiandeng/DLPan-Toolbox/tree/main/02-Test-toolbox-for-traditional-and-DL(Matlab)) + the dataset [PanCollection](https://drive.google.com/drive/folders/15VXUjqPybtqUN_spKfJbw40W05K4nDdY?usp=sharing) for fair training and testing!

### Deploy

PanCollection has provided complete packages.
```
pip install pancollection --upgrade
```

## How to Get Started with the Model


```python
import pancollection as pan
cfg = pan.TaskDispatcher.new(task='pansharpening', mode='entrypoint', arch='FusionNet', 
                             dataset_name="gf2", use_resume=False,
                             dataset={'train': 'gf2', 'test': 'test_gf2_multiExm1.h5'})
print(pan.TaskDispatcher._task)
pan.trainer.main(cfg, pan.build_model, pan.getDataSession)
```

## Training Details

See [Google Colab](https://colab.research.google.com/drive/1KpWWj1lVUGllZCws01zQfd6CeURuGL2O) for quick start.

See [Github Project](https://github.com/XiaoXiao-Woo/PanCollection) for coding details.

## Evaluation

See the [Leaderboard](https://paperswithcode.com/dataset/worldview-3-pancollection) for model results.

See the [PanCollection Paper](https://liangjiandeng.github.io/papers/2022/deng-jig2022.pdf) for early results.



| **Satellite** | **Value** | **Comment**                            |
|--------------------|-----------|----------------------------------------|
| WorldView-3        | 2047      |                                        |
| QuickBird          | 2047      |                                        |
| GaoFen-2           | 1023      |                                        |
| WorldView-2        | 2047      |                                        |


## Citation

To learn more about the PanCollection dataset, see the [Github Pages](https://github.com/liangjiandeng/PanCollection).

```
@ARTICLE{dengjig2022,
	author={邓良剑，冉燃，吴潇，张添敬},
	journal={中国图象图形学报},
	title={遥感图像全色锐化的卷积神经网络方法研究进展},
 	year={2022},
  	volume={},
  	number={9},
  	pages={},
  	doi={10.11834/jig.220540}
   }
```

```
@ARTICLE{deng2022vivone,
	author={L. -J. Deng, G. Vivone, M. E. Paoletti, G. Scarpa, J. He, Y. Zhang, J. Chanussot, and A. Plaza},
	journal={IEEE Geoscience and Remote Sensing Magazine}, 
	title={Machine Learning in Pansharpening: A Benchmark, from Shallow to Deep Networks}, 
	year={2022},
	volume={10},
	number={3},
	pages={279-315},
	doi={10.1109/MGRS.2022.3187652}
   }
```


## License

PanCollection is made available under the GPLv2.0 license.

## Contact
wxwsx1997@gmail.com

liangjiandeng@uestc.edu.cn
size_categories:1K<n<10K language:en license:gpl-2.0 Pytorch region:us
{"language": "en", "license": "gpl-2.0", "size_categories": ["1K<n<10K"], "datasets": ["PanCollection"], "tags": ["Pytorch"]}
TAGS
#size_categories-1K<n<10K #language-English #license-gpl-2.0 #Pytorch #region-us 

PanCollection
=============


To get started with PanCollection benchmark (training, inference, etc.), we recommend reading Google Colab!


Recommendations
---------------


We recommend users to use the code-toolbox DLPan-Toolbox) + the dataset PanCollection for fair training and testing!


### Deploy


PanCollection has provided complete packages.


How to Get Started with the Model
---------------------------------


Training Details
----------------


See Google Colab for quick start.


See Github Project for coding details.


Evaluation
----------


See the Leaderboard for model results.


See the PanCollection Paper for early results.


Satellite: WorldView-3, Value: 2047, Comment: 
Satellite: QuickBird, Value: 2047, Comment: 
Satellite: GaoFen-2, Value: 1023, Comment: 
Satellite: WorldView-2, Value: 2047, Comment: 


To learn more about the PanCollection dataset, see the Github Pages.


License
-------


PanCollection is made available under the GPLv2.0 license.


Contact
-------


wxwsx1997@URL


liangjiandeng@URL



### Deploy


PanCollection has provided complete packages.


How to Get Started with the Model
---------------------------------


Training Details
----------------


See Google Colab for quick start.


See Github Project for coding details.


Evaluation
----------


See the Leaderboard for model results.


See the PanCollection Paper for early results.


Satellite: WorldView-3, Value: 2047, Comment: 
Satellite: QuickBird, Value: 2047, Comment: 
Satellite: GaoFen-2, Value: 1023, Comment: 
Satellite: WorldView-2, Value: 2047, Comment: 


To learn more about the PanCollection dataset, see the Github Pages.


License
-------


PanCollection is made available under the GPLv2.0 license.


Contact
-------


wxwsx1997@URL


liangjiandeng@URL
TAGS
#size_categories-1K<n<10K #language-English #license-gpl-2.0 #Pytorch #region-us 
 ### Deploy


PanCollection has provided complete packages.


How to Get Started with the Model
---------------------------------


Training Details
----------------


See Google Colab for quick start.


See Github Project for coding details.


Evaluation
----------


See the Leaderboard for model results.


See the PanCollection Paper for early results.


Satellite: WorldView-3, Value: 2047, Comment: 
Satellite: QuickBird, Value: 2047, Comment: 
Satellite: GaoFen-2, Value: 1023, Comment: 
Satellite: WorldView-2, Value: 2047, Comment: 


To learn more about the PanCollection dataset, see the Github Pages.


License
-------


PanCollection is made available under the GPLv2.0 license.


Contact
-------


wxwsx1997@URL


liangjiandeng@URL
passage: TAGS
#size_categories-1K<n<10K #language-English #license-gpl-2.0 #Pytorch #region-us 
### Deploy


PanCollection has provided complete packages.


How to Get Started with the Model
---------------------------------


Training Details
----------------


See Google Colab for quick start.


See Github Project for coding details.


Evaluation
----------


See the Leaderboard for model results.


See the PanCollection Paper for early results.


Satellite: WorldView-3, Value: 2047, Comment: 
Satellite: QuickBird, Value: 2047, Comment: 
Satellite: GaoFen-2, Value: 1023, Comment: 
Satellite: WorldView-2, Value: 2047, Comment: 


To learn more about the PanCollection dataset, see the Github Pages.


License
-------


PanCollection is made available under the GPLv2.0 license.


Contact
-------


wxwsx1997@URL


liangjiandeng@URL