下游任务:RepViT在目标检测和实例分割(使用Mask-RCNN框架)以及语义分割(使用Semantic FPN框架)任务中,均展现出优越的性能与延迟平衡。例如,RepViT-M1.1在MS COCO 2017数据集上的APbox和APmask指标均优于EfficientFormer-L1,且延迟更小;在ADE20K数据集上,RepViT-M1.1的mIoU指标比EfficientFormer-L1高出1.7,同时速度...
YoloV8改进策略:Block改进|轻量级的Mamba打造优秀的YoloV8|即插即用,简单易懂|附Block结构图|检测、分割、关键点均适用(独家原创) YoloV8改进策略:Block改进|改进HCF-Net的MDCR模块|附结构图|多种改进方法(独家改进) YoloV8改进策略:Block改进|HCF-Net的PPA模块|附结构图|(独家原创,全网首发) YoloV8改进策略:...
m = self # self.model[-1] # Detect() module # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1 # ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency for a...
In the neck network, a CF-FPN (ment network for tiny object deteciton) feature fusion structure is adopted to enhance the detection accuracy of small targets by combining contextual information and suppressing conflicts between features at different scales. Finally, the original ...
log(5 / self.nc / (640 / s) ** 2) # 类别偏置初始化 # 其他检测头类(如Detect_AFPN_P345等)可以类似地进行提取和注释核心部分说明:Detect_DyHead 类:实现了YOLOv8的动态检测头,负责处理输入特征并输出边界框和类别概率。 初始化方法:设置了类别数量、隐藏层通道数、DFL通道数等,并定义了卷积层和动态...
[YoloV8改进策略:全新特征融合模块AFPN,更换YoloV8的Neck](https://blog.csdn.net/m0_47867638/article/details/133799120) # YoloV8改进策略:EfficientViT,高效的视觉transformer与级联组注意力提升YoloV8的速度和精度,打造高效的YoloV8 [YoloV8改进策略:EfficientViT,高效的视觉transformer与级联组注意力提升YoloV8的速...
a) CSPDarknet53 network used by Backbone; b) FPN + PAN pyramid structure used by Neck; c) decoupled header structure used by Head. The YOLOv8 model comprises four parts: Input, Backbone, Neck, and Head. These serve as input image, feature extraction, multi-feature fusion, and prediction...
state_dict(), pretrain_weight)) return model def fasternet_s(weights=None, cfg='ultralytics/nn/backbone/faster_cfgg/fasternet_s.yaml'): with open(cfg) as f: cfg = yaml.load(f, Loader=yaml.SafeLoader) model = FasterNet(**cfg) if weights is not None: pretrain_weight = torch....
(3)Neck:FPN+PAN结构 (4)Prediction:输出和之前类似主要 损失函数GIOU_Loss 和预测框损失 DIOU_nms 输入端 输入图像大小为--img-size default=[640, 640] 1)Mosaic数据增强(训练使用) Yolov5的输入端仍然采用了和Yolov4一样的Mosaic数据增强的方式。Mosaic数据增强则采用了4张图片,随机缩放、随机裁剪、随机排布...
YoloV8改进策略:全新特征融合模块AFPN,更换YoloV8的Neck YoloV8改进策略:轻量级Slim Neck打造极致的YoloV8 YoloV8改进策略:增加分支,减少漏检 YoloV8改进策略:注意力改进、Neck层改进|自研全新的Mamba注意力|即插即用,简单易懂|附结构图|检测、分割、关键点均适用(独家原创,全世界首发) YoloV8改进策略:Neck层改进|...