Mask_RCNN模型在COCO数据集上预训练权重mask_rcnn_coco.h5

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.

在stata中运用熵值法求权重，可以运用于面板数据，非常方便。

1、pytorch框架YOLOv3在Visdrone开源数据集的训练权重和代码； 2、3个训练好的模型YOLOv3、yolov3-tinyt、YOLOv3-spp； 3、map、PR、recall、loss、等各种训练曲线图； 4、Ultralytics 版YOLOv3版的代码。

1、YOLOv3电塔绝缘子训练好的模型，压缩包内有代码，代码为ultralytics版YOLOv3， 2、classes: Insulator 3、内有map、pr、recall、loss等曲线图

Visdrone数据集YOLOv5训练权重： 1、包括训练好的YOLOv5s-visdrone.pt 和yolov5m-visdrone.pt两个模型 2、包含各种训练曲线 3、包含相关场景测试视频 4、附上了yolov5-5.0的代码

yolov4文章，yolov4程序，以及yolov4预训练权重文件，结合opencv可以实时检测,效果相当的好。

stata做空间计量回归，简单易学，里面有详细的操作说明，小白也能学会哦。拍下即送地级市以及省级权重矩阵（4种都送），经济距离、反距离矩阵、经济地理矩阵、0 1矩阵

autoware1.14的YOLO2、YOLO3权重文件

运用VBA代码实现层次分析法权重计算，可以自动计算权重向量，并进行一致性检验，从而减少计算权重向量的重复性工作，使层次分析法可以便于计算