【建议】代码基于openpcdet框架开发,之前用于openpcdet做过目标检测的同学,可直接入手学习语义分割 Pointcept:github.com/Pointcept/Po 目前最好的模型(CVPR2024Oral Point Transformer V3)代码就来自于此。 【建议】对前沿技术感兴趣的同学可以学习此代码。 1 mmdetection3d 代码链接:github.com/open-mmlab/m 官方文...
如果您想复现Point Transformer2的代码,可以按照以下步骤进行: 1、了解Point Transformer2的模型架构和输入/输出格式。您可以通过阅读相关的论文或文档来了解这些信息。 2、准备数据集。Point Transformer2通常用于处理点云数据,因此您需要准备一个适合该模型的数据集。 3、实现模型。您可以使用深度学习框架(如PyTorch或...
S3DIS，ModelNet40都复现不出来，gap还挺大的
Vision Transformer ConvNext BiFPN BlazeFace-FPN CenterNet-FPN CSP-PAN Custom-PAN FPN ES-PAN HRFPN LC-PAN TTF-FPN YOLO-FPN Smooth-L1 Detr Loss Fairmot Loss Fcos Loss GFocal Loss JDE Loss KeyPoint Loss SoloV2 Loss Focal Loss GIoU/DIoU/CIoU IoUAware SparseRCNN Loss SSD Loss YOLO Loss CT...
Vision Transformer ConvNext BiFPN BlazeFace-FPN CenterNet-FPN CSP-PAN Custom-PAN FPN ES-PAN HRFPN LC-PAN TTF-FPN YOLO-FPN Smooth-L1 Detr Loss Fairmot Loss Fcos Loss GFocal Loss JDE Loss KeyPoint Loss SoloV2 Loss Focal Loss GIoU/DIoU/CIoU IoUAware SparseRCNN Loss SSD Loss YOLO Loss CT...
Vision Transformer ConvNext BiFPN BlazeFace-FPN CenterNet-FPN CSP-PAN Custom-PAN FPN ES-PAN HRFPN LC-PAN TTF-FPN YOLO-FPN Smooth-L1 Detr Loss Fairmot Loss Fcos Loss GFocal Loss JDE Loss KeyPoint Loss SoloV2 Loss Focal Loss GIoU/DIoU/CIoU IoUAware SparseRCNN Loss SSD Loss YOLO Loss CT...
Vision Transformer ConvNext BiFPN BlazeFace-FPN CenterNet-FPN CSP-PAN Custom-PAN FPN ES-PAN HRFPN LC-PAN TTF-FPN YOLO-FPN Smooth-L1 Detr Loss Fairmot Loss Fcos Loss GFocal Loss JDE Loss KeyPoint Loss SoloV2 Loss Focal Loss GIoU/DIoU/CIoU IoUAware SparseRCNN Loss SSD Loss YOLO Loss CT...
Vision Transformer ConvNext BiFPN BlazeFace-FPN CenterNet-FPN CSP-PAN Custom-PAN FPN ES-PAN HRFPN LC-PAN TTF-FPN YOLO-FPN Smooth-L1 Detr Loss Fairmot Loss Fcos Loss GFocal Loss JDE Loss KeyPoint Loss SoloV2 Loss Focal Loss GIoU/DIoU/CIoU IoUAware SparseRCNN Loss SSD Loss YOLO Loss CT...
Vision Transformer ConvNext BiFPN BlazeFace-FPN CenterNet-FPN CSP-PAN Custom-PAN FPN ES-PAN HRFPN LC-PAN TTF-FPN YOLO-FPN Smooth-L1 Detr Loss Fairmot Loss Fcos Loss GFocal Loss JDE Loss KeyPoint Loss SoloV2 Loss Focal Loss GIoU/DIoU/CIoU IoUAware SparseRCNN Loss SSD Loss YOLO Loss CT...
YOLOX和YOLOv5均为基于PaddleDetection复现算法,YOLOv5代码在PaddleYOLO中,参照PaddleYOLO_MODEL 图中模型均可在📱模型库中获取 各移动端模型在COCO数据集上精度mAP和高通骁龙865处理器上预测速度(FPS)对比图。 测试说明(点击展开) 测试数据均使用高通骁龙865(4xA77+4xA55)处理器,batch size为1, 开启4线程测试,测...