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BaiduXJTU_BigData_2019_Semi-Final

Urban Region Function Classification Top18 Solution

Competetition Intro.

Team Brief Intro.

Team Name:

  • 浑南摸鱼队

Key Members:

  • 王德君，东北大学计算机本科大二在读; (Team Leader)
  • 姚来刚，东北大学计算机系硕士，研究方向为机器学习与数据挖掘. (Key Teammate)

Contest Rankings:

  • Preliminary: Rank 17
  • Semi-Final: Rank 18

Mission Descriptions

Build models to classify the functions of urban areas with data of satellite images({Area_ID}.jpg) and user behavior({Area_ID}.txt) from given geographical areas.

  • Tables of the functions of urban areas:
CategoryID Functions of Areas
001 Residential area
002 School
003 Industrial park
004 Railway station
005 Airport
006 Park
007 Shopping area
008 Administrative district
009 Hospital

For more Detailed Task descriptions, please go to 赛题详情

Environmental Requirements

OS & GPU Configurations

  • Ubuntu 18.04.1 LTS
  • GTX 1080Ti x 1 + GTX 1060M x 1
  • Baidu AI Studio (Tesla V100 x 36, For Model I 36 Networks Training)

Python Package Requirements

  • Anaconda 4.7.10
  • python 3.6
  • pytorch 1.1.0
  • keras 2.2.4
  • opencv3
  • sklearn
  • numpy
  • matplotlib

Submission Timeline

Model Baseline Acc Top Result
Model I None 77.08%
Model II 77.08% 81.6200%
Model III 81.6200%
81.2440%		 82.1800%

Detailed Solution

Model I: Several Netural Network Stacking (DeepLearning)

# Model Descriptions:
8 Nets
5 folds Stacking
Trained Networks = 7*5+1 (We only trained fold1 on Net6 because of Its Low Local Acc).
# Result:
After 36 NN Stacking, we reached a top online acc of 77.08%.
NetWork Name Baseline Descriptions Online Top1-Acc on Test of 5 folds Merged Result
Net1_raw DPN26+Resnext50 Train with RAW NPYs (40w 182x24 Npys) 76.21%
Net2_1 Net1 Introduced Resampled Folds_Split for Train About 76%
Net3_w Net1 Class_Ratio were considered into Loss Calculation About 76%
Net4_TTA Net1 Introduced TTA (Test Time Augumentation) About 76%
Net5_HR Net1 Introduced HighResample (linear) About 76%
Net6_Features DenseNet Introduced Feture Engineering (Features: 175) 61.19%
Net7_MS Net1 Introduced MultiScale About 76%
Net8_MS_cat Net1 Introduced MultiScale & Concatenate About 76%

Model II: Txt Processing (Feature Engineering)

# Steps:
1) Txt Identical Check (Completed)
2) Multivoters based on Total Times A user Appeared in Same Category (Completed)
3) Multivoters based on Total Hours A user Appeared in Same Category (Only 3/2000 json files were processed)
# Result:
After above 3 steps, we got a submission of 81.62%. (81.6200%.txt)
# Notes:
[1] Step 3 was not completed Because of Limited Time and Computation Resources, only 3/2000 data was processed.
[2] In this project we abbreviate {Preliminary,Semi-Final}-{Train,Test}-Datasets as {P,S}{Tr,Te} ==> {PTr,PTe,STr,STe}.
Steps Content Descriptions Oringinal Score After Improved Source Code
(1) Utilize Identical txts' Categories in PTr & STr to provide answers for STe 77.04% 78.74% SelfDuplicateCheck
(2) Multivoters based on Total Times A user Appeared in Same Category 78.74% 81.62% MergeVotes
(3) Multivoters based on Total Hours A user Appeared in Same Category 81.62% - AdvM2_train

Model III: Merge & Rebalance the Predicts in Submissions (Post-processing）

Directly Modify Submission.txt:
We Compared predicts in 81.2440%.txt and 81.6200%.txt, finding that 001 was TOO MANY (4k more than True Value), 003/005 were a bit more-predicted, and others were all less-predicted.
  • Category Distributions in Our Submissions (Take 81.6200%.txt for Example)
Category Total Predicts in 81.6200%.txt Estimated True Value Difference (Pred-Estimated) Desc
001 34542 30092 +4450 Too Much More
002 22026 22763 -737 Much Less
003 13247 12753 +494 More
004 1510 1647 -137 Little Less
005 4314 4123 +191 More
006 12978 15671 -2693 Much Less
007 4986 5283 -297 Little Less
008 2247 3295 -1048 Much Less
009 4150 4370 -220 Little Less 
Therefore, We Merged the Predicts among our ex-Top2 Submissions. (81.6200%.txt & 81.2440%.txt)
Strategy & Rules:
1) Compare & Merge the predicts in the two txt file by Replacing those '001's to other less-predicted categories.
2) While the two gives the same prediction or both predictions are in More-Predicted Categories ['001','003','005'], Choose the answer in 81.6200%.txt as result Beacause of its Higher Acc.
After this operation, we got our final best submission 82.1800%.txt, which reached 82.18%.

def MergeDict(Dict1,Dict2,ModCates):
    Merge_Dict = {}
    _identical,_new_choice,_prior = 0,0,0
    for key,val1 in Dict1.items():
        val2 = Dict2[key]
        if val1==val2:
            Merge_Dict[key] = val1
        elif '001' in [val1,val2] and '003' not in [val1,val2] and '005' not in [val1,val2]:
            Merge_Dict[key] = val2 if val1=='001' else val1
        elif val2 in ModCates:
            Merge_Dict[key] = val2
        else:
            Merge_Dict[key] = val1
    return Merge_Dict

txt1 = '../81.6200%.txt'
txt2 = '../81.2440%.txt'
Dict1 = LoadDictFromTxt(txt1)
Dict2 = LoadDictFromTxt(txt2)

priorlist = ['001',
#             '006',
#             '003',
#             '008'
            ]
submit_txt = MergeName(txt1,txt2,'{}_MOD'.format('_'.join(priorlist)))
Merge_Dict = MergeDict(Dict1,Dict2,[])
##Merge_Dict = MergeDict(Dict1,Dict2,['006'])
##Merge_Dict = MergeDict(Dict1,Dict2,['003','008'])
WriteDictToTxt(submit_txt,Merge_Dict)
Statistics(Merge_Dict)