youngfly11
commented
2 years ago Thanks for your interest.
- we extract at most 100 proposals for each image. In fact, we set at least 10 and at most 100 by only setting the nms score and set scores.
- Most of results are performed by using the BUTD detectors. Our rn50 faster rcnn on coco experiments is only for fair comparison with seqground.
Yongfei Liu
ShnaghaiTech University
在 2021年12月7日，11:34，Zhangjiacheng144 @.***> 写道：
Hi, Thank you for your great work, your code is very excellent! But may I ask you for some questions about the algorithm? 1，How did you generate the fixed num of the pre-computed box? In your paper, you said we first need to generate M =100 pre-computed box as the region proposals，but when I use Faster RCNN to generate the pre-computed box, it seems can't guarantee to get the fixed num of pre-computed box because of the NMS process. In the test mode Faster RCNN, there are two NMS processes, one for proposals generated by RPN, another for the boxes generated by ROI Head. In the first situation, after NMS, there are still much proposal, but in the second situation, after NMS for each class label, only some number of boxes survive. However, in bottom-up-attention, we use the bbox generated by ROI Head. Actually, when I use Faster RCNN weight file you provided and roi_head_nms=0.3 as you said, I can only get much less pre-computed boxes, like 56, 18..., sometimes even less than topN=10, which would cause the error when run your code. For this situation, I guess it because the well-trained Faster RCNN can already accurate the location of the object, which result in most of boxes are generated at the same position and removed by the ROI_Head_NMS. So, currently, I set the nms=0.7, and I just feel a little bit confused about this. 2，Which dataset you pre-train the Faster RCNN for generating the pre-computed box? In your code, it seems that you used the Faster RCNN pre-trained on COCO dataset to generate the pre-computed box, but it only covers 81 categories. In the Flickr30K Entities, there are lots of categories doesn't exist in COCO, so does this situation matter? Because I find that most of works use the Faster RCNN pre-trained on Visual Genome dataset which covers 1600 classes and some attribute labels.
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JCZ404
Hi, Thank you for your great work, your code is very excellent! But may I ask you for some questions about the algorithm? 1，How did you generate the fixed num of the pre-computed box? In your paper, you said we first need to generate M =100 pre-computed box as the region proposals，but when I use Faster RCNN to generate the pre-computed box, it seems can't guarantee to get the fixed num of pre-computed box because of the NMS process. In the test mode Faster RCNN, there are two NMS processes, one for proposals generated by RPN, another for the boxes generated by ROI Head. In the first situation, after NMS, there are still much proposal, but in the second situation, after NMS for each class label, only some number of boxes survive. However, in bottom-up-attention, we use the bbox generated by ROI Head. Actually, when I use Faster RCNN weight file you provided and roi_head_nms=0.3 as you said, I can only get much less pre-computed boxes, like 56, 18..., sometimes even less than topN=10, which would cause the error when run your code. For this situation, I guess it because the well-trained Faster RCNN can already accurate the location of the object, which result in most of boxes are generated at the same position and removed by the ROI_Head_NMS. So, currently, I set the nms=0.7, and I just feel a little bit confused about this. 2，Which dataset you pre-train the Faster RCNN for generating the pre-computed box? In your code, it seems that you used the Faster RCNN pre-trained on COCO dataset to generate the pre-computed box, but it only covers 81 categories. In the Flickr30K Entities, there are lots of categories doesn't exist in COCO, so does this situation matter? Because I find that most of works use the Faster RCNN pre-trained on Visual Genome dataset which covers 1600 classes and some attribute labels.