Comments (7)
In my understanding, you are referring to Table 1 rather than Table 2, right?
gt_cls means replacing the classification score with the value of 1.0 at the ground-truth label position. For example, [0.1, 0.3, 0.6] -> [0.1, 0.3, 1.0], assuming the gt label is 3 (0.6 -> 1.0). This indicates the perfect classification for the generated bounding box.
Please be aware that this experiment aims to find out the performance barrier of current dense detectors. It is not part of the evaluation of our proposed algorithm.
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但是这样在评估的时候,score不都是1.0了吗。AP的计算不是需要对score进行排序吗,这样性能不会下降吗
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我知道这只是想验证检测的上界,并不是方法部分。我只是想知道这部分验证实验的一些细节。还望赐教
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Not all generated bounding boxes will have a score of 1.0, but only those boxes which are generated from a few of points inside the gt boxes, i.e foreground points defined by ATSS, will be given a new score of 1.0. Others will be unchanged.
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Ideally, it is necessary to make the score of the estimated more accurate box as high as possible, right? If they are all set to 1.0, the boxes of these positive samples are considered to be very accurate, Can this indicate the upper boundary?
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This upper boundary is achieved by the perfect classification of bounding boxes (here perfect classification means assigning a correct label to a bounding box without considering its spatial accuracy), and as you see it is limited, although it is higher than the original performance (43.1 vs 38.5) . On the other hand, as shown in Table 1, if you can predict perfect IACSs (gt_cls_iou) as the detection score, the upper boundary is quite high.
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OK, thanks for your patient reply! I have no problem
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Related Issues (20)
- Some question about the inference HOT 4
- VFNet-X's config file 404 error HOT 2
- VFocalLoss in yolov5 HOT 3
- Do you think the `VarifocalLoss` could be used for labels with value of 0 & 1 ? HOT 2
- How to visualize detection results?
- where is Star-Shaped Box Feature Representation and Bounding Box Refinement in the code HOT 2
- GPU error HOT 3
- KeyError: 'ATSSVGFLHead is not in the head registry' HOT 9
- How to reimplement IACS? HOT 2
- Varifocal Loss for YOLOv5 HOT 2
- Using MMDet version of VFNet with the lastest backbone (e,g. Poolformer S36, ConvNeXt Small) with Inf Issues on Varifocal loss HOT 1
- VarifocalLoss HOT 1
- Can varifocal loss be applied to softmax classifier?
- AttributeError:'ConfigDict' object has no attribute 'test_cfg'
- Question about `detach` HOT 7
- About applying Varfifocal to yolox objectness loss HOT 2
- cls loss is increasing HOT 4
- Train custom dataset HOT 2
- Welcome update to OpenMMLab 2.0
- Architecture dimension
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