初涉 Deep Drive Dataset

Berkeley 大学最近推出的针对自动驾驶的街景数据集,号称比 Cityscapes 数据量更大,可泛化性更好。

语义实例分割(Semantic Instance Segmentation)

数据集一共有 40 种物体类别

与 Cityscapes 的对比

街景数据来自 US 的城市

模型更熟悉美国的街景。

图片标签

时间:daytime, nighttime, dawn/dusk;

场景:Residential,High-way, City street, Parking lot, Gas station, Tunnel;

天气:Clear, Partly cloudy, Over-case, Rainy, Snowy, Foggy;

Label Maps

语义分割使用标签映射(Label Maps),不是训练索引(Training Indices)。

更高的可泛化性

使用 Dilate Residual Network (Hyper parameter 相同)测试两个数据集时发现下表的关系:

Train Test Accuracy
deepDriver deepDriver High
deepDriver Cityscapes Low
Cityscapes deepDriver Low
Cityscapes Cityscapes High

在同样的数据集下训练结果都很好,但交叉使用不同测试集时精度下降显著。使用 deepDriver 训练的模型在 Cityscapes 测试集上的表现虽然较差,但有部分训练结果比在特定场景训练的结果要好。这意味着该数据集涵盖场景更多,训练出的模型的可泛化性会比较好。

以上参考:https://arxiv.org/abs/1805.04687

数据集详情

文件结构:

bdd100k
|   seg
|    |  images
|    |    |  train
|    |    |  val
|    |    |  test
|    |  color_labels
|    |    |  train
|    |    |  val
|    |  labels
|    |    |  train
|    |    |  val

检查数据集完整性的 python3 脚本

import os
import sys 

if  len(sys.argv) !=  2:
    print (‘Usage: python checkdata.py <train|val>‘)
    exit(-1)

dataset_category = sys.argv[1]
if dataset_category not  in {‘train‘, ‘val‘}:
    print (f‘Invalid argument "{dataset_category}"‘)
    exit(-2)

data_size = 7000 if dataset_category == ‘train‘ else 1000

dir_root =  ‘.‘
dir_color = os.path.join(dir_root, ‘color_labels‘, dataset_category)
dir_imgs = os.path.join(dir_root, ‘images‘, dataset_category)
dir_label = os.path.join(dir_root, ‘labels‘, dataset_category)

color_names = os.listdir(dir_color)
img_names = os.listdir(dir_imgs)
label_names = os.listdir(dir_label)

assert len(color_names) ==  len(img_names) ==  len(label_names) == data_size

for i in range(len(color_names)):
    prefix_color = color_names[i].split(‘_‘)[0]
    prefix_img = img_names[i].split(‘.‘)[0]
    prefix_label = label_names[i].split(‘_‘)[0]
    assert prefix_color == prefix_img == prefix_label, f‘{prefix_color}, {prefix_img}, {prefix_label}‘

print (‘All Good!‘)

包含分割多边形信息的 Json 文件目前还没有公开,因此只能做segmentation,不能做 detection + segmentation。但是单纯的 detection 数据文件已经是提供好的,可以使用查看工具查看标注矩形框和三种图片标签(时间、场景、天气)

官方代码目前的坑

https://github.com/ucbdrive/bdd-data/issues/17

https://github.com/ucbdrive/bdd-data/issues/5

https://github.com/ucbdrive/bdd-data/issues/15

其中,#15 issue 目前还未解决。



Written with StackEdit.

原文地址:https://www.cnblogs.com/LexLuc/p/9653229.html

时间: 2024-11-01 15:00:33

初涉 Deep Drive Dataset的相关文章

数据集搜集整理

1. CIFAR-10 & CIFAR-100 CIFAR-10包含10个类别,50,000个训练图像,彩色图像大小:32x32,10,000个测试图像. (类别:airplane,automobile, bird, cat, deer, dog, frog, horse, ship, truck) (作者:Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton) (数据格式:Python版本.Matlab版本.二进制版本<for C程序>)

词组习语3057组

superword是一个Java实现的英文单词分析软件,主要研究英语单词音近形似转化规律.前缀后缀规律.词之间的相似性规律等等. 1 Anointing of the Sick British English 2 Civvy Street Clerk of the Closet 3 I mean I must say 4 I suppose so I will thank you to do something 5 Incoming mail server Lithium battery 6 M

fashion datasets图像检索实践project

Using Siamese Networks and Pre-Trained Convolutional Neural Networks (CNNs) for Fashion Similarity Matching Resources Code for the project is available here. Deep Fashion dataset is available here. Data Science Virtual Machine documentation. Visual S

Machine and Deep Learning with Python

Machine and Deep Learning with Python Education Tutorials and courses Supervised learning superstitions cheat sheet Introduction to Deep Learning with Python How to implement a neural network How to build and run your first deep learning network Neur

Growing Pains for Deep Learning

Growing Pains for Deep Learning Advances in theory and computer hardware have allowed neural networks to become a core part of online services such as Microsoft's Bing, driving their image-search and speech-recognition systems. The companies offering

【深度学习Deep Learning】资料大全

转载:http://www.cnblogs.com/charlotte77/p/5485438.html 最近在学深度学习相关的东西,在网上搜集到了一些不错的资料,现在汇总一下: Free Online Books Deep Learning66 by Yoshua Bengio, Ian Goodfellow and Aaron Courville Neural Networks and Deep Learning42 by Michael Nielsen Deep Learning27 by

Joint Deep Learning for Pedestrian Detection笔记

1.结构图 Introduction Feature extraction, deformation handling, occlusion handling, and classification are four important components in pedestrian detection. Existing methods learn or design these components either individually or sequentially. The inte

Classifying plankton with deep neural networks

Classifying plankton with deep neural networks The National Data Science Bowl, a data science competition where the goal was to classify images of plankton, has just ended. I participated with six other members of my research lab, the Reservoir lab o

[C4] Andrew Ng - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

About this Course This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good res