PyTorch训练模型中 box_loss、obj_loss、cls_loss为nan的原因及解决方法 1. 整体流程 在理解“为什么使用PyTorch训练模型的box_loss、obj_loss、cls_loss都为nan”之前,我们需要了解整个训练过程的流程。下面是PyTorch训练模型的一般流程: 准备数据:首先我们需要准备训练数据,包括输入数据和对应的标签。 定义模型:然后...
解释:obj_loss(对象损失)在目标检测任务中用于衡量模型预测的对象存在性与真实情况之间的误差。它通常与模型判断一个区域是否包含目标对象的能力相关。 意义:通过最小化obj_loss,模型能够更好地区分图像中的前景和背景,提高目标检测的准确性。 cls_loss在分类任务中的角色: 解释:cls_loss(分类损失)在分类任务中用...
Box regression loss is also updated for stability and complexity improvements. Author karl-gardner commented Oct 6, 2021 @glenn-jocher so the cls loss is the same as the original yolo papers: while the obj_loss is updated slightly to the CIOU loss which can be found in this review on ob...
When generating results.png, bug happened with disorder on Y-axis of val/box_loss, val/obj_loss and val/cls_loss #7650 Closed 1 of 2 tasks sylvanding opened this issue Apr 30, 2022· 4 comments · Fixed by #7654 Labels bug Comments Contributor sylvanding commented Apr 30, 2022...
conf_alpha * (class_loss + (obj_loss * keep).sum()) def conf_objectness_loss(self, conf_data, conf_t, batch_size, loc_p, loc_t, priors): """ Instead of using softmax, use class[0] to be p(obj) * p(IoU) as in YOLO....
labels,目标类别的标签,形状为 [num_obj] loc_t,conf_t,landm_t,输出,分别为匹配好的先验框编码后的位置,置信度,人脸 landmark 的位置。 idx,索引,也即 batch 中的第 idx 张图片(元素)。 match() 函数解析 如前文所述,究竟将先验框匹配为目标还是背景取决于先验框与目标框的 IOU,因此 match() ...
labels: (tensor) All the class labels for the image, Shape: [num_obj]. loc_t: (tensor) Tensor to be filled w/ endcoded location targets. conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds. idx: (int) current batch index Return: The matched indices correspo...
def jaccard(box_a, box_b): """ A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) box_a: (tensor) Ground truth bounding boxes, Shape: [num_obj, 4] box_b: (tensor) Anchor boxes from anchor layers, Shape: [num_obj, 4] :return: jaccard overlap: (tensor)...
load_from=r'C:\Users\DELL\Desktop\YOLO-World-master\configs\finetune_coco\Weight\yolo_world_v2_l_obj365v1_goldg_pretrain-a82b1fe3.pth' persistent_workers = False model settings model = dict( type='YOLOWorldDetector', mm_neck=True, ...
你好, 基于独立数据集训练时,bbox_loss始终是0,模型无法预估。 训练如下, inferece时报错, 我的train config文件是: test_haomi_v06101440.txt 我的train.json文件是: train_direct.json 我的label.json文件是: label.json 求助, 辛苦帮忙看下为什么bbox loss始终