Generating Annotations for VOC with MaskCut about cutler HOT 12 CLOSED

After debugging the maskcut.py, it is found that the value of counts are garbled. Why does it happen? How to solve it ?

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Hi, the mask annotation is in COCO's Run Length Encoding (RLE) format. You can refer to the discussion here to learn how to convert RLE to the human-readable polygon format.

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By the way, it is recommended to stick with the RLE format for mask annotation unless you have a specific reason to use the polygon format, as the former is more efficient in terms of storage and computation.

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Closing it now. Please feel free to reopen it if you have further questions!

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Hi, could you please resend your questions? There are many &nbsp symbols, it is hard to read your message. Thanks!

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Sorry，i rephrase my questions.
i have deleted segmentation, height, and width code in annotation_info which are garbled, in order to generate annotations for voc, because the jsonfile of voc download from github also does not include this part of code.

However , after generating the jsonfile and running the command
（ python train_net.py --num-gpus 3
--config-file model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml
--test-dataset cls_agnostic_voc --no-segm
--eval-only MODEL.WEIGHTS "/home/yli/CutLER-main/cutler_cascade_final.pth"）, AP50 is 64.58 which is much higher than the value in modelzoo which you offered. Therefore, i compared those two jsonfiles, I found that bbox information in my jsonfile is different from yours. i do not know why there are these differences, do you have any idea?

The yellow boxes are my jsonfile visualized results, the red boxes are yours. why are our jsonfiles inconsistent?

By the way , my steps are followings:

1. downlowad VOC2007 JPEGImages

2. run the command :python maskcut.py --vit-arch base --patch-size 8

3. specify the jsonfile in cutler/data/datasets/builtin.py

4. run the command:

   python train_net.py --num-gpus 3
   --config-file model_zoo/configs/CutLER-ImageNet/cascade_mask_rcnn_R_50_FPN.yaml
   --test-dataset cls_agnostic_voc --no-segm
   --eval-only MODEL.WEIGHTS "/home/yli/CutLER-main/cutler_cascade_final.pth")

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i also evaluated the model's performance on VOC using the jsonfile download from your github, the result is the same as what you submitted in modelzoo. i feel that my steps are correct, but why my jsonfile generated by maskcut is different from yours？

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voc_annotations.zip
This is my jsonfile annotations generated according to the steps described above, do you have any idea why it is different from yours? And how can my result be consistent with yours?

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Answered in #29.

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