Python 全栈工程师核心面试 300 问深入解析(2020 版)----全文预览
Python 全栈工程师核心面试 300 问深入解析(2020 版)----欢迎订阅
Python错误SyntaxError: unexpected EOF while parsing
含义是：解释器到底了都没找到它要找到的东西
出现以上错误，仔细检查代码后还是没有找到错误，一般是以下原因：


















1 语法有问题，一般都是少了print最后的一个括号，我当时碰到是因为print（d.drop(）少打了一个括号，别盲目的相信自动补全自己认真检查一下，肯定这行代码少了或者多了东西，希望有用。
2 输入语法肯定有问题，有时候忘了切换中英文输入法，可以删除了重新输入

Scene Parsing



Problem

segment and parse an image into different image regions associated with semantic categories



Evaluation

mean of the pixel-wise accuracy 

the ratio of pixels which are correctly predicted.
class-wise IoU 

the Intersection of Union of pixels averaged over all the semantic categories.
Dataset

Stanford Background 

S. Gould, R. Fulton, and D. Koller. Decomposing a scene into geometric and semantically consistent regions. In Computer Vision, 2009 IEEE 12th International Conference on, pages 1–8, Sept 2009.
SIFT Flow 

C. Liu, J. Yuen, and A. Torralba. Nonparametric scene parsing via label transfer. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(12):2368–2382, Dec 2011.
PASCAL-Context 

Mottaghi, Roozbeh, et al. “The role of context for object detection and semantic segmentation in the wild.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
ADE20K 

Semantic Understanding of Scenes through ADE20K Dataset. B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso and A. Torralba. arXiv:1608.05442
Dataset
  
Stanford Background
  
SIFT Flow
  
ADE20K
No. of images
  
715
  
2688
  
25562
No. of train set
  
572
  
2488
  
20210
No. of val set
  
0
  
0
  
2000
No. of test set
  
143
  
200
  
3352
No. of classes
  
8
  
33
  
150
Samples of ADE20K

http://sceneparsing.csail.mit.edu/browse.php/?dirname=training/







Result



Stanford Background

Method
  
Pixel Acc.
  
Class Acc.
  
averaged computing time per image
Single-scale ConvNet
  
66
  
56.5
  
0.35 (GPU)
Augmented CNNs
  
71.97
  
66.16
  
-
Superparsing
  
77.5
  
-
  
10 to 300
Deep 2D LSTM (window 5x5)
  
77.73
  
68.26
  
1.3 (CPU)
Deep 2D LSTM (window 3x3)
  
78.56
  
68.79
  
3.7 (CPU)
Multi-scale ConvNet
  
78.8
  
72.4
  
0.6 (CPU)
RCNN2 (3 instances)
  
80.2
  
69.9
  
10.7 (GPU)
N-ReNet
  
80.4
  
71.8
  
0.07 (GPU)
Multi-CNN + rCPN Fast
  
80.9
  
78.8
  
0.37 (GPU)
multiscale net + CRF on gPb
  
81.4
  
76.0
  
60.5 (CPU)
Zoom-out
  
82.1
  
77.3
  
-
HGDN
  
82.41
  
72.98
  
0.02 (GPU)
RCNN_NIPS2015
  
83.1
  
74.8
  
0.03 (GPU)
SIFT Flow

Method
  
Pixel Acc.
  
Class Acc.
  
mean IU
  
f.w. IU
  
averaged computing time per image
Augmented CNNs
  
49.39
  
44.54
  
-
  
-
  
-
Deep 2D LSTM (window 5x5)
  
68.74
  
22.59
  
-
  
-
  
1.2 (CPU)
Deep 2D LSTM (window 3x3)
  
70.11
  
20.90
  
-
  
-
  
3.1 (CPU)
RCNN2 (3 instances)
  
77.7
  
29.8
  
-
  
-
  
-
multiscale net + cover1
  
72.3
  
50.8
  
-
  
-
  
-
multiscale net + cover2
  
78.5
  
29.6
  
-
  
-
  
-
RCNN (balanced)
  
79.3
  
57.1
  
-
  
-
  
0.03 (GPU)
HGDN
  
79.68
  
51.26
  
-
  
-
  
0.03 (GPU)
RCNN-large
  
84.3
  
41.0
  
-
  
-
  
0.04 (GPU)
FCN-16s
  
85.2
  
51.7
  
-
  
-
  
0.175 (GPUs)
VGG-conv5-DAG-RNN(8)
  
85.3
  
55.7
  
-
  
-
  
-
FCN-8s
  
85.9
  
53.9
  
41.2
  
77.2
  
-
patch CRF+CNN
  
88.1
  
53.4
  
-
  
-
  
-
PASCAL-Context

Method
  
Pixel Acc.
  
Class Acc.
  
mean IU
  
f.w. IU
CFM
  
-
  
-
  
18.1
  
-
CFM
  
-
  
-
  
34.4
  
-
FCN-32s
  
65.5
  
49.1
  
36.7
  
50.9
FCN-16s
  
66.9
  
51.3
  
38.4
  
52.3
FCN-8s
  
67.5
  
52.3
  
39.1
  
53.0
patch CRF+CNN
  
71.5
  
53.9
  
-
  
-
Reference

Method
  
Year
  
Conference
  
Reference Paper
Superparsing
  
2010
  
ECCV
  
Superparsing: Scalable nonparametric image parsing with superpixels
Single-scale ConvNet
  
2013
  
PAMI
  
Learning hierarchical features for scene labeling
multiscale net
  
2013
  
PAMI
  
Learning hierarchical features for scene labeling
Augmented CNNs
  
2014
  
BMVC
  
Contextually constrained deep networks for scene labeling
RCNN2 (3 instances)
  
2014
  
ICML
  
Recurrent convolutional neural networks for scene labeling
Multi-CNN + rCPN Fast
  
2014
  
NIPS
  
Recursive context propagation network for semantic scene labeling
RCNN (balanced)
  
2015
  
NIPS
  
Convolutional Neural Networks with Intra-layer
RCNN-large
  
2015
  
NIPS
  
Convolutional Neural Networks with Intra-layer
Deep 2D LSTM
  
2015
  
CVPR
  
Scene Labeling with LSTM Recurrent Neural Networks
Zoom-out
  
2015
  
CVPR
  
Feedforward semantic segmentation with zoom-out features
FCN-16s
  
2015
  
CVPR
  
Fully convolutional networks for semantic segmentation
N-ReNet
  
2016
  

  
Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation
HGDN
  
2016
  
CVPR
  
Hierarchically Gated Deep Networks for Semantic Segmentation
VGG-conv5-DAG-RNN(8)
  
2016
  
CVPR
  
DAG-Recurrent Neural Networks For Scene Labeling
patch CRF+CNN
  
2016
  
CVPR
  
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation