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.
2022-01-25 17:24:07
87.07MB
1
OpenPCDet权重文件pointrcnn_iou_7875.pth
2022-01-17 19:01:57
15.54MB
1
pytorch最后的权重文件是.pth格式的。
经常遇到的问题：
进行finutune时，改配置文件中的学习率，发现程序跑起来后竟然保持了以前的学习率， 并没有使用新的学习率。
原因：
首先查看.pth文件中的内容，我们发现它其实是一个字典格式的文件
其中保存了optimizer和scheduler，所以再次加载此文件时会使用之前的学习率。
我们只需要权重，也就是model部分，将其导出就可以了
import torch
original = torch.load('path/to/your/checkpoint.pth')
new = {"model": original["model"
2021-12-14 19:37:54
55KB
1