Comments (7)
Hi, thanks for your work.
In 'split_coco_dataset_voc_nonvoc.py', you split 3 annotation files, but I can't find 'instances_valminusminival2014.json' and 'instances_minival2014.json' in the official website of COCO. Could you tell me where I can get these files?
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You can download from this link.
from context-transformer.
Thanks for your reply!
I have another question about the prior boxes. In the original SSD, the output of its model includes prior box layers, which contain the coordinate information of all default boxes. In your code, however, these coordinates are computed out of the model. Instead, 'obj' is computed in the model, which is the output of a convolutional layer. What does this 'obj' part stand for?
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FYI, the prior box is the same as default boxes in SSD, as stated in Sec 4.1 in the our paper. Such boxes are predefined and don't require any update during training. Therefore, it's more efficient to compute it once out of the model, which is also applied in RFBNet.
As for the 'obj', it's a foreground/background classifier, somewhat like the objectness score (RPN output) in two-stage detector Faster RCNN. For details, please refer to Sec 3 of our paper. Thanks.
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If I didn't get it wrong, this 'obj' part is the background classifier(BG) in your paper.
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Exactly.
from context-transformer.
I see. Thanks for your help!
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Related Issues (16)
- Issue about VOC2007.sh HOT 6
- demo.py HOT 2
- size 512 error. HOT 1
- [Errno 2] No such file or directory: './data/COCO/annotations/instances_valminusminival2014.json' HOT 5
- Pretraining RFBNet on source domain dataset COCO60, it shows loss nan HOT 7
- src_cls_dim HOT 1
- Can't get the simmilar mAP of VOC split1 at phase1 under incremental setting.
- cannot import name '_mask' from 'utils.pycocotools' HOT 9
- how to split voc data HOT 3
- How can I reproduce experiment in limit cuda memory HOT 3
- Issue about "trainval_1shot.txt" HOT 2
- How to train model with 1 GPU? HOT 10
- How to train on a customized dataset? HOT 1
- About Figure.5 in your paper HOT 4
- Comparing with SOTA methods HOT 1
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