12、results.png train/box_loss:YOLO V5使用 GIOU Loss作为bounding box的损失,Box推测为GIoU损失函数均值,越小方框越准; train/obj_loss:推测为目标检测loss均值,越小目标检测越准; train/cls_loss:推测为分类loss均值,越小分类越准;单类检测,此值为0 precision:精确率(找对的正类/所有找到的正类); recall...
if ni <= nw: xi = [0, nw] # x interp # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) for j, x in enumerate(optimizer.param_groups): # bias lr ...
for obj in root.iter('object'): difficult = obj.find('difficult').text #difficult = obj.find('Difficult').text cls = obj.find('name').text if cls not in classes or int(difficult) == 1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (float(xmlbox.fin...
#打印Print一些信息包括当前epoch、显存、损失(box、obj、cls、total)、当前batch的target的数量和图片的size等信息 ifRANKin{-1,0}: mloss=(mloss*i+loss_items)/(i+1)#updatemeanlosses pbar.set_description(("%11s"+"%11.4g"*5)%(f"{epoch}/{epochs-1}",*mloss,targets.shape[0],imgs.shape[...
train/box_loss, train/obj_loss, train/cls_loss metrics/precision,metrics/recall,metrics/mAP_0.5,metrics/mAP_0.5:0.95 val/box_loss, val/obj_loss,val/cls_loss,x/lr0, x/lr1, x/lr2 12、results Box_loss:YOLO V5使用 GIOU Loss作为bounding box的损失,Box推测为GIoU损失函数均值,越小方框越准;...
(1, 0.02, 0.2), # box loss gain 'cls': (1, 0.2, 4.0), # cls loss gain 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight 'iou_t': (0, ...
mindspore/ccsrc/runtime/graph_scheduler/actor/data_source_actor.cc:320 OnMemoryAllocFinish 四、进一步问题 该报错行与issue:#IAT9OH:求助,nn.TrainOneStepWithLossScaleCell出错:RuntimeError: Compile graph kernel_graph0 failed.报错函数一致,是否存在统一的问题? @fangwenyi...
model.gr= 1.0#iou loss ratio (obj_loss = 1.0 or iou)#根据labels初始化图片采样权重(图像类别所占比例高的采样频率低)model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)#attach class weights#获取类别的名字model.names = names ...
2021-11-11 17:00:54 [INFO] [TRAIN] Epoch=2/270, Step=6/14, loss_xy=16.747334, loss_wh=16.774042, loss_obj=64.807220, loss_cls=1.962115, loss=100.290710, lr=0.000125, time_each_step=0.73s, eta=0:45:33 2021-11-11 17:00:59 [INFO] [TRAIN] Epoch 2 finished, loss_xy=15.715854,...
EfficientDet (Obj Det, Semantic soon) - https://github.com/rwightman/efficientdet-pytorch Computer Vision / Image Augmentation Albumentations - https://github.com/albumentations-team/albumentations Kornia - https://github.com/kornia/kornia Knowledge Distillation RepDistiller - https://github.com/Hobbit...