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Xin Wu, Danfeng Hong, Jocelyn Chanussot. UIU-Net: U-Net in U-Net for Infrared Small Object Detection, IEEE Trans. Image. Process., 2022, 32: 364-376.

License: Apache License 2.0

Python 100.00%

ieee_tip_uiu-net's Introduction

UIU-Net: U-Net in U-Net for Infrared Small Object Detection

Xin Wu, Danfeng Hong, Jocelyn Chanussot

The code in this toolbox implements the "UIU-Net: U-Net in U-Net for Infrared Small Object Detection". More specifically, it is detailed as follow.

Citation

Please kindly cite the papers if this code is useful and helpful for your research.

X. Wu, D. Hong, J. Chanussot. UIU-Net: U-Net in U-Net for Infrared Small Object Detection, IEEE Trans. Image. Process., 2023, 32: 364-376.

 @article{wu2023uiu,
  title     = {UIU-Net: U-Net in U-Net for Infrared Small Object Detection},
  author    = {X. Wu and D. Hong and J. Chanussot},
  journal   = {IEEE Trans. Image. Process.}, 
  volume    = {32},
  pages     = {364--376},
  year      = {2023},
  publisher = {IEEE}
 }

System-specific notes

Please refer to the file of requirements.txt for the running enviroment of this code.

❗ The model in saved_models/uiunet can be downloaded from the following baiduyun:

Baiduyun: https://pan.baidu.com/s/11JSqFKxq7XTvTzgOIWfdOg (access code: eu9f)

Google drive: https://drive.google.com/file/d/1wZ_W5-Wj3wOt7kMB6nwa0xntZTD14sPZ/view?usp=share_link

Licensing

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program.

Contact Information:

Danfeng Hong: [email protected]
Danfeng Hong is with the Aerospace Information Research Institute, Chinese Academy of Sciences, 100094 Beijing, China.

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ieee_tip_uiu-net's Issues

请问论文中所述Synthetic dataset 选取6200张图像，具体是怎么实现的？

请问论文中所述Synthetic dataset 选取6200张图像，具体是怎么实现的？有开源的代码吗？文中只说了手动去除预处理后具有零值或负值的图像，那具体是保留了哪些图像呢？

关于传统算法指标计算问题

您好，我在计算红外小目标检测传统算法IPI的时候遇到了问题。
我利用混淆矩阵计算指标，发现计算出来的IoU与nIoU是一样。因为IoU=tp/(fn+fp+tp)，nIoU=tp/(t+p-tp)。公式表示是一样的，我感觉我算出来的是nIoU。
请问您可以教教我，如何计算红外小目标检测传统算法的IoU与nIoU吗？

train_data问题

你好，提供的train_data是空的，但是train.py包含如下：
tra_image_dir = os.path.join('DUTS', 'DUTS-TR', 'DUTS-TR', 'im_aug' + os.sep)
tra_label_dir = os.path.join('DUTS', 'DUTS-TR', 'DUTS-TR', 'gt_aug' + os.sep)
我应该从哪里下载'DUTS', 'DUTS-TR'，'im_aug' 'gt_aug' 这些

What is the difference between your work and the U^2 net?

U^2 Net: https://github.com/xuebinqin/U-2-Net

test.py代码的问题

你好，因为test输出的结果中平均值只有零点零几，所以我发现了几个问题：

   if IoU > best_iou:
        best_iou = IoU
    if nIoU > best_nIoU:
        best_nIoU = nIoU

    total_iou = total_iou + IoU
    total_niou = total_niou + nIoU`

这一部分代码是不是应该放到for循环里呀。
还有

IoU=total_iou/20
nIoU=total_niou/20

用新的名字结果就对了:

avg_IoU=total_iou/20
avg_nIoU=total_niou/20

不能生成预测的图片吗？

在test.py中IoU=total_IoU/20为什么除以20？

代码中iou的计算感觉有点问题。

为什么我测试出来iou比niou还小

测试结果iou比niou还小，是哪里出错了吗

YOLOv7添加该网络

作者尝试过把这个网络添加到yolov7中去么

the model is available just for chinese

i can't download the model because i should have an account in baidu and this app works for chinese

RuntimeError: torch.cat(): input types can't be cast to the desired output type Byte

您好，我在使用其它两种数据集训练时，在一个epoch下，不同批次会出现报错
l0: 0.010986, l1: 0.011472, l2: 0.014452, l3: 0.004034, l4: 0.004883, l5: 0.013490, l6: 0.004046

[epoch: 1/500, batch: 45/ 213, ite: 15] train loss: 0.884871, tar: 0.183994
Traceback (most recent call last):
File "D:\contrast-methods\IEEE_TIP_UIU-Net-main\train.py", line 139, in
for i, data in enumerate(salobj_dataloader):
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch\utils\data\dataloader.py", line 630, in next
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch\utils\data\dataloader.py", line 1345, in _next_data
return self._process_data(data)
^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch\utils\data\dataloader.py", line 1371, in _process_data
data.reraise()
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch_utils.py", line 694, in reraise
raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch\utils\data_utils\worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch\utils\data_utils\fetch.py", line 54, in fetch
return self.collate_fn(data)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch\utils\data_utils\collate.py", line 265, in default_collate
return collate(batch, collate_fn_map=default_collate_fn_map)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch\utils\data_utils\collate.py", line 127, in collate
return elem_type({key: collate([d[key] for d in batch], collate_fn_map=collate_fn_map) for key in elem})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch\utils\data_utils\collate.py", line 127, in
return elem_type({key: collate([d[key] for d in batch], collate_fn_map=collate_fn_map) for key in elem})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch\utils\data_utils\collate.py", line 119, in collate
return collate_fn_map[elem_type](batch, collate_fn_map=collate_fn_map)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\hp-\AppData\Roaming\Python\Python311\site-packages\torch\utils\data_utils\collate.py", line 162, in collate_tensor_fn
return torch.stack(batch, 0, out=out)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: torch.cat(): input types can't be cast to the desired output type Byte。
请问这种错误有解决方案吗？

Modify input shape

Hello. How can I modify the input shape of the model?

requirements and IoU values

i want to ask if there are any precise versions of the requirements (specially for torch, torchvision) because it didn't work for me without specifying it . I'm looking for a quick response please. and if you can help me with any information about the metrics because the way you coded IoU and nIoU have a really small value0.000xx even if there is an intersection (visually noticed) . I tried to print the intersection to check, and it is = 0 and the comparison of the label and predicted mask shows the opposite (a good result).