这时候思路就很明显了,要想“软化”这个 loss,就得“软化”θ(x),而软化它就再容易不过,它就是 sigmoid 函数。我们有: 所以很显然,我们将θ(x)替换为σ(Kx)即可: 这就是我昨晚思考得到的 loss 了,显然实现上也是很容易的。 现在跟 Focal Loss Focal Loss Kaiming 大神的 Focal Loss 如果落实到ŷ =...
03/18 06:15:39 - mmengine - INFO - Epoch(train) [1][50/70] base_lr: 2.0000e-04 lr: 9.8000e-06 eta: 1:58:38 time: 1.2826 data_time: 0.0524 memory: 11573 grad_norm: nan loss: 331.5620 loss_cls: 331.5620 loss_bbox: 0.0000 loss_dfl: 0.0000 具体配置文件为 Mar 18, 2024 Author...
Search before asking I have searched the YOLOv8 issues and discussions and found no similar questions. Question Hi, I am training both models in the nano and small variants with a custom dataset. I noticed that YoloV5 had losses below 0...
P-TileHostVM 0(Key Client)VM 1(Key Server)MCDMAChannel Decoder (CH0-PF 0, CH1-PF 1)Host InterfacePIOAVSTAXI-STAXI-AVSTAVSTUncontrolledPortsMACSec0CSRCSRCSRE-TileTXRXMACQSFPLoopback*EAPOLPacketGenerator& CheckerCSRPacketGenerator& Checker[vf_active, clog2 (PF_NUM),clog2 (VF_NUM), PIO ...