ppyolo_2x.pt
PyTorch YOLO
在线加载模型地址

确定自己使用的框架并导入对应的库。导入库实现样例代码可参考 文档中心-预训练模型使用教程

在代码中实现加载预训练模型地址

调用模型的实现方法

# 创建模型Backbone = select_backbone(cfg.backbone_type)backbone = Backbone(**cfg.backbone)IouLoss = select_loss(cfg.iou_loss_type)iou_loss = IouLoss(**cfg.iou_loss)iou_aware_loss = Noneif cfg.head['iou_aware']: IouAwareLoss = select_loss(cfg.iou_aware_loss_type) iou_aware_loss = IouAwareLoss(**cfg.iou_aware_loss)Loss = select_loss(cfg.yolo_loss_type)yolo_loss = Loss(iou_loss=iou_loss, iou_aware_loss=iou_aware_loss, **cfg.yolo_loss)Head = select_head(cfg.head_type)head = Head(yolo_loss=yolo_loss, is_train=True, nms_cfg=cfg.nms_cfg, **cfg.head)model = PPYOLO(backbone, head)_decode = Decode(model, class_names, use_gpu, cfg, for_test=False)# 加载权重if cfg.train_cfg['model_path'] is not None: # 加载参数, 跳过形状不匹配的。 load_weights(model, cfg.train_cfg['model_path']) strs = cfg.train_cfg['model_path'].split('step') if len(strs) == 2: iter_id = int(strs[1][:8])