Comments (5)
Q1: YES. We apply HungarianMatcher to get the corresponding queries, and then use ground-truth boxes to extract RoI features.
Q2: NO. The target frames only take part in tracking head at training phase.
Q3: NO.
from queryinst.
Q1: YES. We apply HungarianMatcher to get the corresponding queries, and then use ground-truth boxes to extract RoI features. Q2: NO. The target frames only take part in tracking head at training phase. Q3: NO.
Thanks a lot!
from queryinst.
Q1: YES. We apply HungarianMatcher to get the corresponding queries, and then use ground-truth boxes to extract RoI features.
Q2: NO. The target frames only take part in tracking head at training phase.
Q3: NO.
In the Dynamic Instance Embedding part, do the key frame share queries(
from queryinst.
Yes, we inference querytrack on reference frame (with torch.no_grad()
) and apply another HungarianMatcher to get the assignment between queries and ground-truths.
Q1: YES. We apply HungarianMatcher to get the corresponding queries, and then use ground-truth boxes to extract RoI features.
Q2: NO. The target frames only take part in tracking head at training phase.
Q3: NO.
In the Dynamic Instance Embedding part, do the key frame share queries(
$q_{t-1}^{*}$ in formula(4) ) with the reference frame? Or another HungarianMatcher is needed to get the reference frame's exclusive queries?
from queryinst.
Yes, we inference querytrack on reference frame (
with torch.no_grad()
) and apply another HungarianMatcher to get the assignment between queries and ground-truths.Q1: YES. We apply HungarianMatcher to get the corresponding queries, and then use ground-truth boxes to extract RoI features.
Q2: NO. The target frames only take part in tracking head at training phase.
Q3: NO.In the Dynamic Instance Embedding part, do the key frame share queries(
$q_{t-1}^{*}$ in formula(4) ) with the reference frame? Or another HungarianMatcher is needed to get the reference frame's exclusive queries?
Thank you! And could you please tell me the weights of ClassificationCost
, BBoxL1Cost
and IoUCost
when getting the assignment between queries and ground-truth bboxes of the reference frame? Thanks again.
from queryinst.
Related Issues (20)
- pytorch2onnx failed HOT 1
- AttributeError: 'EmbeddingRPNHead' object has no attribute 'async_simple_test_rpn' HOT 2
- mAP = 0 all the time HOT 7
- Code navigation: queries HOT 5
- which learning rate is true? HOT 2
- The parameter of the tracking loss HOT 2
- gpu HOT 2
- how to inference the pretrained model on my test data without GT HOT 1
- KeyError: "QueryInst: 'QueryRoIHead is not in the models registry'" HOT 1
- Base config file does not exist
- Instance Segmentation using CPU fails on certain images when Swin Transformer backbone was used
- cityscapes config HOT 10
- Why use 'batch-nms' in an end2end model?
- 您好,请问代码里是不是没使用预训练模型? HOT 1
- MMCV version
- Loss value when training QueryInst
- test stage mAP(box)=0, mAP(seg)=0.71 HOT 1
- Welcome update to OpenMMLab 2.0
- How to get QueryInst to train on an external dataset
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from queryinst.