yolov5 v6.0权重文件 yolov5l.pt yolov5l6.pt m m6 n n6 s s6 x x6

yolov5 v6.0 pt权重文件 yolov5n.pt 5s 5m 5l 5x 5n6 5s6 5m6 5l6 5x6

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

autoware1.14的YOLO2、YOLO3权重文件

OpenPCDet权重文件pointpillar_7728.pth

OpenPCDet权重文件PartA2_free_7872.pth

OpenPCDet权重文件pointrcnn_iou_7875.pth

OpenPCDet权重文件second_iou7909.pth

适合使用YOLOX训练自己的数据集的人群

keras-yolov3权重文件