导致其他类的loss趋近于0。解决方案是通过oversampling等方式平衡各类目标的数量。
Hello, I trained YOLOv5_5.0and YOLOv5_6.1 version, there is only one class, why is its class loss 0? Additional No response fyy378 added the question label Nov 23, 2023 Contributor github-actions bot commented Nov 23, 2023 👋 Hello @fyy378, thank you for your interest in YOLOv5 ...
其中target是经过one-hot编码的0 or 1,pred是经过sigmoid计算而得的0~1。 最后总结一下,sigmoid版的CrossEntropyLoss执行步骤: 对label target进行one-hot编码 根据ignore_index,若某正样本的label target为ignore_index,那么把它的label weight权重设置为0 调用F.binary_cross_entropy_with_logits计算,并且对各个类...
有一个值得注意的点是,在做 Umax 的时候,Brake 选择 Partner With KOL,而在做 Cal AI 的时候,他们完全是 Paid influencer Marketing,Brake 认为,这是领域不同导致的策略不同。Cal AI 所在的领域其实是 Weight Loss,这是一个非常大的领域,很多头部产品立在里面,look maxing 则是另外一回事情,很小的细分赛道,...
(see above for traceback): assertion failed: [] [Condition x == y did not hold element-wise:] [x (losses/fast_rcnn_cls_loss/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [101 1] [y (losses/fast_rcnn_cls_loss/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [101]...