demo

[trungpham2606]

Thank you @Shank2358 for sharing a great work.
Iam trying to visualize the detection with your work.
But when I normally ran the test.py file, it popped up an error about e2cnn. So can you show me the reference of e2cnn.
Thank in advance.

[Shank2358]

Thank you @Shank2358 for sharing a great work. Iam trying to visualize the detection with your work. But when I normally ran the test.py file, it popped up an error about e2cnn. So can you show me the reference of e2cnn. Thank in advance.

Thanks. You only need to comment out all the parts of E2CNN. I updated GGHL.py, you can try it with the new version.

In addition, the E2CNN library (https://github.com/csuhan/e2cnn) is used by ReDet's backbone. Because I want to try other backbones for recently, I added it.
If there are other problems, I am happy to answer them.

[trungpham2606]

Thank you @Shank2358 for your quick response.
According to your paper, you also tested the model on SKU dataset, the results were extremely great. Can you provide the pretrained weights for the SKU dataset as well ?

[Shank2358]

for the SKU dataset

Of course. The weights for the SKU datase has already been available. You can download it from Baidu Disk(password: c3jv) or Google Drive.

The weights for the SSDD+ dataset will also be available soon.

Thank you.

[trungpham2606]

@Shank2358
Thank to your weights, I was able to visualize the detection on an image from DOTA and SKU.

I really want to train my own dataset. The VOC format is not a problem but can you tell me the way you define the rotating angle ?

[Shank2358]

@Shank2358 Thank to your weights, I was able to visualize the detection on an image from DOTA and SKU.

I really want to train my own dataset. The VOC format is not a problem but can you tell me the way you define the rotating angle ?

Congratulations!

For oriented bounding boxes, maybe you can use cv2.polylines(img, [points]) to draw them. The results in our paper are drawn like this. (I see that the result of your visualization is horizontal bounding boxes, maybe you can try this)

The format of the training dataset is like this:
image_path xmin,ymin,xmax,ymax,class_id,x1,y1,x2,y2,x3,y3,x4,y4,area_ratio,angle[0,-90)

We use the opencv definition of the angle, that is, the range of the angle is [0,-90), and a more specific explanation is as follows

You can use the cv2.minAreaRect(Points) function to calculate the angle.
For more specific calculation methods and explanations, you can refer to the official documentation of opencv。
https://docs.opencv.org/3.4/de/d62/tutorial_bounding_rotated_ellipses.html

[2021-11-15-16:23] I have updated the description of the data set format in readme.md.

[trungpham2606]

Hello @Shank2358
How can I calculate the area_ratio ?

[Shank2358]

Hello @Shank2358 How can I calculate the area_ratio ?

Hi. I have added a script for generating datasets in ./datasets_tools/DOTA2Train.py, maybe you can try it.
The Line 72-72 of DOTA2Train.py is used for calculating the area_ratio, which is the ratio of OBB's area and HBB's area
Thank you.

[trungpham2606]

@Shank2358 I see, I will try it.

[trungpham2606]

@Shank2358
I see in the script, the angle is calculated directly from cv2, but you dont change its value as stated on the paper ?
I mean: if angle in [pi/2, pi] -> angle = angle - pi/2 ?

[Shank2358]

@Shank2358 I see in the script, the angle is calculated directly from cv2, but you dont change its value as stated on the paper ? I mean: if angle in [pi/2, pi] -> angle = angle - pi/2 ?

This angle transformation in paper has been done by the opencv function. so the output is (-pi/2,0] directly.

[trungpham2606]

@Shank2358
After converting my custom dataset to the format that GGHL needs, I can train but met this issue:

[Shank2358]

@Shank2358 After converting my custom dataset to the format that GGHL needs, I can train but met this issue:

Hi. I guess the following potential problems may lead to Nan.

1. You'd better to check whether the converted data is correct. It would be a good idea to visualize them. The correct results should be like this in the paper.
   

2. Model initialization parameters need to be reset. Maybe you can try our pre training weight (trained on ImageNet), which will make the convergence more stable. The link of pre-trained weight is as follows.
   Baidu_Disk(password:0blv)
   Google_Drive

3. If the pre-trained weight is not used, the parameter initialization method may need to be adjusted. Our default initialization is the initialization with the mean value of 0 and the variance of 0.01. Maybe you can try Xavier initialization or Kaiming initialization.

4. Maybe you can check whether the denominator or log is 0, which will also lead to NaN

5. Training hyper-parameters such as learning rate may also need to be readjusted if you train your own dataset.

As the only information available to me is this screenshot, I can only infer from experience that Nan may be caused by the above reasons. I guess it is a higher probability of data conversion and pre-trained weight. Try them first. If you still have problems, please leave me a message or e-mail. I will try my best to help you solve this problem.

Thank you.