如何为全卷积网络格式化数据集? [英] How to format a data set for fully convolutional networks?

问题描述

我正在尝试为完全卷积网络准备数据集.我已经浏览了一些数据集，但很难确定如何格式化它.例如，在 Kitti 数据集中，有这两个图像， 培训文件夹中的文本文件:

I am trying to prepare my data set for fully convolutional network. I've looked through some data sets and I'm having a really hard time figuring out how to format it. For instance, in the Kitti data set, there are these 2 images and this text file in the training folder:

图片1

图片2

文本

P0:7.215377000000e + 02 0.000000000000e + 00 6.095593000000e + 02 0.000000000000e + 00 0.000000000000e + 00 7.215377000000e + 02 1.728540000000e + 02 0.000000000000e + 00 0.000000000000e + 00 0.000000000000e + 00 1.000000000000e + 00 0.000000000000 e + 00 P1:7.215377000000e + 02 0.000000000000e + 00 6.095593000000e + 02 -3.875744000000e + 02 0.000000000000e + 00 7.215377000000e + 02 1.728540000000e + 02 0.000000000000e + 00 0.000000000000e + 00 0.000000000000e + 00 1.000000000000e + 00 0.000000000000e + 00 P2:7.215377000000e + 02 0.000000000000e + 00 6.095593000000e + 02 4.485728000000e + 01 0.000000000000e + 00 7.215377000000e + 02 1.728540000000e + 02 2.163791000000e-01 0.000000000000e + 00 0.000000000000e + 00 1.000000000000e + 00 2.745884000000e-03 P3:7.215377000000e + 02 0.000000000000e + 00 6.095593000000e + 02 -3.395242000000000000 + 02 0.000000000000e + 00 7.215377000000e + 02 1.728540000000e + 02 2.199936000000e + 00 0.000000000000e + 00 0.000000000000e + 00 1.000000000000e + 00 2.729905000000e- 03 R0_rect:9.999239000000e-01 9.837760000000e-03 -7.445048000000e-03 -9.869795000000e-03 9.999421000000e-01 -4.278459000000e-03 7.402527000000e-03 4.351614000000e-03 9.999631000000e-01 Tr_velo_to_cam:7.533745000000e-03 -9.999714000000e-01 -6.166020000000e-04 -4.069766000000e-03 1.480249000000e-02 7.280733000000e-04 -9.998902000000e-01 -7.631618000000e-02 9.998621000000e-01 7.523790000000e-03 1.480755000000e- 02 -2.717806000000e-01 Tr_imu_to_velo:9.999976000000e-01 7.553071000000e-04 -2.035826000000e-03 -8.086759000000e-01 -7.854027000000e-04 9.998898000000e-01 -1.482298000000e-02 3.195559000000e-01 2.024406000000e-03 1.482454000000e-02 9.998881000000e-01 -7.997231000000e-01 Tr_cam_to_road:9.999570839814e-01 -5.508724949246e-03 -7.452906591504e-03 9.610489538319e-03 5.425697507328e-03 9.999234779341e-01 -1.111504746388e-02 -1.597134401910e + 00 7.513565886504e-03 1.107413060494e-02 9.999104059534e-01 2.788606298060e-01

P0: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 0.000000000000e+00 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00 P1: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 -3.875744000000e+02 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 0.000000000000e+00 P2: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 4.485728000000e+01 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 2.163791000000e-01 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 2.745884000000e-03 P3: 7.215377000000e+02 0.000000000000e+00 6.095593000000e+02 -3.395242000000e+02 0.000000000000e+00 7.215377000000e+02 1.728540000000e+02 2.199936000000e+00 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00 2.729905000000e-03 R0_rect: 9.999239000000e-01 9.837760000000e-03 -7.445048000000e-03 -9.869795000000e-03 9.999421000000e-01 -4.278459000000e-03 7.402527000000e-03 4.351614000000e-03 9.999631000000e-01 Tr_velo_to_cam: 7.533745000000e-03 -9.999714000000e-01 -6.166020000000e-04 -4.069766000000e-03 1.480249000000e-02 7.280733000000e-04 -9.998902000000e-01 -7.631618000000e-02 9.998621000000e-01 7.523790000000e-03 1.480755000000e-02 -2.717806000000e-01 Tr_imu_to_velo: 9.999976000000e-01 7.553071000000e-04 -2.035826000000e-03 -8.086759000000e-01 -7.854027000000e-04 9.998898000000e-01 -1.482298000000e-02 3.195559000000e-01 2.024406000000e-03 1.482454000000e-02 9.998881000000e-01 -7.997231000000e-01 Tr_cam_to_road: 9.999570839814e-01 -5.508724949246e-03 -7.452906591504e-03 9.610489538319e-03 5.425697507328e-03 9.999234779341e-01 -1.111504746388e-02 -1.597134401910e+00 7.513565886504e-03 1.107413060494e-02 9.999104059534e-01 2.788606298060e-01

此数据集与我见过的用于CNN的常规数据集有很大不同.因此，我有以下问题:

This data set is very different from the regular data sets I've seen being used for CNNs. Hence, I had the following questions:

1. 文本文件中发生了什么事?
2. 如何生成带有纯色像素的第二张图像?
Fli提出的优点之一是能够馈送任意大小的输入图像.我可以将输入图像缩小到多少-50x50太小?我寻找了一些与此相关的文献，但找不到很多.

1. What is happening in the text file?
2. How to generate the 2nd image with solid colored pixels?
3. One of the proposed advantages of FCNs is the ability to feed input images of arbitrary sizes. How small can I make the input images - is 50x50 too small? I looked for some literature surrounding this but couldn't find much.

基本上，我正在尝试创建一个数据集以使用此github上的网络.其中只有两个要训练的文件夹:training_img_lmdb和training_label_lmdb.因此，我不确定文本文件或像素化图像是否位于标签文件夹中.任何帮助将不胜感激！

Essentially, I'm trying to create a data set to use this network from this github. Which has only 2 folders for training: training_img_lmdb and training_label_lmdb. So, I'm not exactly sure if the text file or the pixelated image goes in the label folder. Any help would be greatly appreciated!!

推荐答案

1. 看起来像遥测，例如Tr_cam_to_road，Tr_velo_to_cam等...通常，数据集将包含文档

1. Looks like some kind of telemetry, from Tr_cam_to_road, Tr_velo_to_cam, etc... usually the dataset will have documentation

请澄清.您发布了图像.您肯定知道如何加载图像吗?

Please clarify. You posted the image. Surely you know how to load an image?

您是正确的，但是任何纯卷积网络的最小输入大小将等于单个输出像素的输入邻域大小.

You are correct, however any purely convolutional network will have a minimum input size equivalent to the input neighborhood size of a single output pixel.

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