Closed roysubhankar closed 2 years ago
Hi @roysubhankar, 1) I forgot to remove the parameter. You can easily remove opts.new_protocol from the if. 2) Yes, it is the pretrained from InPlace-ABN 3) I don't think I got the question, I try to answer then correct me. The COCO 61-80 classes are the same VOC classes. I don't use the COCO images during training in step 1 but only the VOC ones. During the evaluation, I evaluate both on full-COCO (Considering all the classes and averaging the IoU for 1-60 and 61-80) and on full-VOC. 4) These classes are excluded in the COCO dataset since under-represented. Actually, COCO has 91 object classes, but 11 are ignored by default. You can check at this link the removed classes. 5) You're right. To avoid deleting old annotations, I named the folder annotations_my in the script, but I renamed them after to be simply annotations. You should use the generated labels (they start from 1 instead of 0 to add the background class and to remove stuffs).
Hi @fcdl94
Thanks for your responses, all my questions have been answered.
Just regarding question 1 above, if I remove the opts.new_protocol from the if statement then both the if statements will evaluate to true and the crop_size = 321 will be set from the second if statement. Are you using crop_size = 321
or crop_size=448
in your coco-to-voc experiment?
Moreover, when I ran WILSON on coco-to-voc the numbers are off by some margin in both the datasets. Here is what I obtain compared to what is reported in the paper.
I will describe the dataset preparation steps to be sure if I am doing the same, as you have done:
python data/coco/make_annotation.py
. This generates a annotations_my/
folder. The segmentation masks from this folder is used in the experiments and the original annotations/
folder is ignored.python data/make_cocovoc.py
to re-map the VOC labels to the COCO label space of 61-80 classes. This generates a folder called SegmentationClassAugAsCoco/
.Please let me know if you are doing anything differently than me. Thank you again.
Hey! I used crop size 448.
Regarding the results, I updated the results on arxiv after fixing a small bug I had in the CVPR submission. The new results are COCO: 39.8 41.0 40.6 VOC (All) 55.7, which is very close to what you get.
What parameter of alpha (label smoothing) did you use? I'll report the results I obtained in coco-to-voc changing the value of alpha.
Alpha | 1-60 | 61-80 | All (COCO) | All (VOC) |
---|---|---|---|---|
0.3 | 40.3% | 38.1% | 40.3% | 51.0% |
0.5 | 40.2% | 38.9% | 40.4% | 52.1% |
0.7 | 40.0% | 39.9% | 40.5% | 53.8% |
1.0 | 37.6% | 29.1% | 35.9% | 40.7% |
0.9 (reported) | 39.8% | 41.0% | 40.6% | 55.7% |
Hi,
I am also using alpha=0.9.
Thanks for all the clarifications.
Hi Fabio,
I would like to ask some questions regarding the COCO-to-VOC experiment:
python data/coco/make_annotation.py
creates a folder calledannotations_my/
, whereas in the line below the oldannotations/
folder is used. Which annotation folder must be used for the COCO-to-VOC experiment?https://github.com/fcdl94/WILSON/blob/41c7d5b21b627722311d559ad5f6d835917f95d1/dataset/coco.py#L32Thank you.