Comments (4)
@azuryl emm, i think the accuracy is meaningless. So i haven't paid attention on this function. You can question this in the origin hrnet repo.
from udp-pose.
Dear JunJie thank your reply
in
lif config.MODEL.TARGET_TYPE == 'offset':
_, avg_acc, cnt, pred = accuracy(output.cpu().numpy()[:, ::3, :, :],
target.cpu().numpy()[:, ::3,:, :])
you used this function. I do not know why
Thank you
from udp-pose.
@azuryl i just imitate the source hrnet repo to add this for preventing the error.
from udp-pose.
ok I see thank :)
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Related Issues (20)
- ERROR: Could not find a version that satisfies the requirement opencv-python==3.4.1.15 HOT 2
- missing keypoint-aware occlusion augmentation HOT 1
- Question about the pre-process HOT 2
- what is difference pred and preds
- why preds need X scale HOT 1
- why your json's bboxes have so many no human's joints HOT 1
- Confuse about merge origin hrnet into mmpose HOT 9
- About the multi-scale testing HOT 1
- About KPD HOT 5
- Good job,but a little flaw? HOT 1
- AID
- Results on COCO val2017
- Download the pre-trained model
- test accuracy too low use offset
- pose_hrnet_w48* means using additional data from AI challenger for training?有 AI challenger 和coco数据集合并完成的数据集提供吗? HOT 1
- Can you give some explanation on apply affine transform, i.e, the use of get_warpmatrix and warpAffine?
- About evaluation index
- can you release the udp code for simple-R50 and pretrained weight?
- 可以在UDP的heatmap的基础上运用simcc的方法吗?
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