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: 真实为positive...
results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) # 设置学习率衰减所进行到的轮次,即使打断训练,使用resume接着训练也能正常衔接之前的训练进行学习率衰减 scheduler.last_epoch = start_epoch - 1 # do not move # scaler = flow.cuda.amp.Gra...
0, 15, 15) window. """ base_anchor = np.array([1, 1, base_size, base_size]) - 1 ##设置基础anchor,左上坐标为(1,1),右下坐标为(16,16),即宽为15 ratio_anchors = _ratio_
train_loss = 0.0 if global_step % args.checkpointing_steps == 0: if accelerator.is_main_process: save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") accelerator.save_state(save_path) logger.info(f"Saved state to {save_path}") logs = {"ste...
loss import CtdetLoss import torch.optim as optim from utils.utils import AverageMeter from config import * from optimizer import * assert BASE_MODEL in MODEL_SUPPORT,"not support this model !!!" def init_seeds(seed=0, cuda_deterministic=True): random.seed(seed) np.random.seed(seed) torch...
# Lossofrpn_cls&rpn_boxes(self.feed('rpn_conv/3x3').conv(1,1,len(anchor_scales)*3*4,1,1,padding='VALID',relu=False,name='rpn_bbox_pred'))#===RoI Proposal===(self.feed('rpn_cls_score').reshape_layer(2,name='rpn_cls_score_reshape').softmax(name='rpn_cls_prob'))(self....
其余元素为负例 # labels : [0,1,2,3] labels = paddle.arange(0, query_cls_embedding.shape[0], dtype='int64') # labels : [[0],[1],[2],[3]] labels = paddle.reshape(labels, shape=[-1, 1]) # 交叉熵损失函数 loss = F.cross_entropy(input=cosine_sim, label=labels) return ...
running_loss = 0 overall_step += 1 piece_num += 1print('saving model for epoch {}'.format(epoch + 1)) if not os.path.exists(output_dir + 'model_epoch{}'.format(epoch + 1)): os.mkdir(output_dir + 'model_epoch{}'.format(epoch + 1)) ...
(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, ...
print('cls weights: {}, aa weights: {}'.format( model.mA.parameters().next().norm().data[0], model.mAAa.parameters().next().norm().data[0])) top1, _ = trainer.validate(train_loader, model, criterion, epochs, opt)foriinrange(5): ...