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In the past 40 years, the Electronic and Optoelectronic System Research Laboratories undertakes the missions for the researches and developments of Semiconductor, Packaging, LED/OLED/Micro LED, Telecommunications, Flexible Electronics, 3D Imaging, Flexible Display and Transparent Display Application System technologies.

ITRI provides a welcoming work environment that stimulates the full potential of ITRI’s members, allowing them to excel and to perform their best in application-oriented researches. We fully cultivate the capability of independent innovation through participating in international cooperation and academic programs. These collaborative activities are pursued so as to aid in the success of technological incubation and entrepreneur start-ups. We strive to strengthen the technological innovation and value creation ability for Taiwan’s industry in order to remain the competitiveness globally.

2019/11/06 【AOI瑕疵分類】報名步驟說明

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Introduction

Automated optical inspection (AOI) [1] is an automated visual inspection of printed circuit board (PCB) (or LCD,transistor) manufacture where a camera autonomously scans the device under test for both catastrophic failure (e.g. missing component) and quality defects (e.g. fillet size or shape or component skew). It is commonly used in the manufacturing process because it is a non-contact test method. It is implemented at many stages through the manufacturing process including bare board inspection, solder paste inspection (SPI), pre-reflow and post-reflow as well as other stages. The Institute of Electronics and Optoelectronics in Industrial Technology Research Institute(ITRI) has spent years on developing flexible displays, hoping to elevate the production quality with AOI technology during the pilot run. This time we have invited experts from different fields to join us, and focus on identifying defect classifications of AOI image data that are offered so as to elevate the identification efficiency of AOI through statistical science.

This is an open-ended problem that can be solved consistently over a long period of time, and has offered milestone rewards for participants. Baseline is the best score of the previous milestone, and Private Leaderboard will be updated occasionally.

Reference
[1] https://en.wikipedia.org/wiki/Automated_optical_inspection

Activity time

The milestone is set at 23:59:59 on 12/17/2019, with its Private Leaderboard being announced at 00:00:00 on 12/18/2019. The grade would be viewed as a basis for awards. Private Leaderboard will be updated occasionally before the milestone.

During the period from 06/06 to 12/17, Private Leaderboard will be updated once two weeks.

Prize Information

The final score should be uploaded before 23:59:59 on the day of milestone. The top three exceeding Baseline score (0.998521) on Private Leaderboard would be awarded as follows:


The first place: 100,000 discount points of hicloud

The second place: 100,000 discount points of hicloud

The third place: 100,000 discount points of hicloud

You will be awarded a certificate if your score outperforms Baseline on the Private Leaderboard and you pass model verification process


Rewards were provided by Chunghwa Telecom

Note:

1. Hicloud points can redeem service charge. After the discount, the price will be charged at 30% off based on the list price automatically.

2. Chunghwa Telecom deserves the right to make changes to the terms and conditions herein.

3. A model verification process will be performed to verify eligibility of awardees. Only those who pass model verification process will be awarded a certificate and/or prize.

Evaluation Criteria

After classification models of defect prediction are offered by researchers participating in the issue, the back-end of the system would process them in bathces regularly to calculate the score. Evaluations are conducted by calculating the corresponding accuracy rate of the actual value.

The following is the formula:

$$Accuracy = {\text{Number of correct predictions} \over \text{Number of total predictions}}$$

Data description

There are six categories included in image data that the issue offers (1 normal category + 5 defect categories)

The download data file (aoi_data.zip) includes:

  • train_images.zip: image data for training (PNG format), 2,528 images in total.
  • train.csv: includes 2 columns, ID and Label.
    • ID: the image filename
    • Label: defect classification category (0: normal, 1: void, 2: horizontal defect, 3: vertical defect, 4: edge defect, 5: particle)
  • test_images.zip: image data for testing (PNG format), 10,142 images in total.
  • test.csv: includes 2 columns, ID and Label.
    • ID: the image filename
    • Label: defect classification category (the value can be only one of the following numbers: 0, 1, 2, 3, 4, 5)

Upload format description

Please upload the file in CSV format, separate with commas, and save in one file. The content must correspond to number order of ID columns and include the following information.

  • ID: the image filename
  • Label: defect classification category (the value can be only one of the following numbers: 0, 1, 2, 3, 4, 5)

Rules

  • Upload limit is 5 entries per day.
  • The evaluation result would be based on the final version that you upload. If two teams (or more) got the same evaluation scores, the time they upload would determine the ranking.
  • If there is cheating or fraud during the activity, the team that cheats will be disqualified from the activity and the vacancy would be filled up by other teams in the ranking order.
  • This topic does not offer the team-up option by request of the topic provider. If there are answers identified the same during topic examination and audit, participants with identical answers would be forced to withdraw the activity, with all uploading data of the topic being eliminated.
  • Keep the personal results in a safe place, lest the data be stolen or plagiarized, leading to the impairment of individual rights.
  • Awarding: We consider an account to belong to an individual, thus the prize should be collected by only one person. Multiple winners of one award are no longer acceptable in the platform.
  • After uploading, the answers of test data would be divided into two parts to calculate the score:
    • Before the deadline of the activity: The system refers to partial Ground Truth of test data to examine and calculate the score. The result will be posted on Public Leaderboard as reference for the final score and ranking. This data accounts for 40% of the entire test data.
    • After the deadline of the activity: The system refers to Ground Truth of the remaining test data to examine and calculate the score. The result will be posted on Public Leaderboard as reference for the final score and ranking.
  • Any form of manual labeling is forbidden. A model verification process will be performed to verify eligibility of awardees. Only those who pass model verification process will be awarded a certificate and/or prize.

Ranking Team name Grade of Result
1 blessxu 0.9992601
2 cwhuang1021 0.9982737
3 sikadeer 0.9980271
4 stephan.kuo 0.9975339
5 sln95303 0.9970406
6 fhwang 0.9967940
7 Colmar 0.9965474
8 aiedward 0.9965474
9 INER_AILab 0.9963008
10 42zhazha 0.9955610
11 tw48964896 0.9953144
12 yuchi 0.9953144
13 jesse1029 0.9950678
14 K0Yang 0.9950678
15 youzhi 0.9948212
16 LiuPiu 0.9945745
17 chengjiun.ma@gmail.com 0.9943279
18 jummy1124@gmail.com 0.9943279
19 Yozi 0.9943279
20 Msdp 0.9943279
21 SnorLax 0.9938347
22 teds 0.9938347
23 JackKao 0.9935881
24 Jiang 0.9935881
25 jang_jian 0.9935881
26 gn00665219 0.9933415
27 wen_long 0.9933415
28 ridicon 0.9930949
29 tueswking511 0.9928483
30 hsh0703 0.9928483
31 Ted_Bear 0.9926017
32 desyandriani 0.9923551
33 pacude881 0.9918618
34 107378057 0.9918618
35 supertortoise 0.9918618
36 vincentwu 0.9918618
37 Davidwill 0.9916152
38 sampon0223 0.9913686
39 TeriTeri 0.9913686
40 cyl02 0.9913686
41 linan 0.9911220
42 Rsys 0.9911220
43 lucas85062055 0.9908754
44 inture 0.9906288
45 Cat_MeowMeow 0.9906288
46 A610050A 0.9903822
47 XiangChen 0.9903822
48 zolo1122 0.9901356
49 TerrenceChou 0.9901356
50 Tony_CHU 0.9901356
51 painting 0.9898890
52 johnny88850tw 0.9896424
53 chihyuu 0.9893958
54 slot9004 0.9893958
55 PDFwithData 0.9891491
56 7777 0.9891491
57 hmyeh 0.9891491
58 ab30310 0.9886559
59 0000 0.9886559
60 franky 0.9884093
61 86oujohnny 0.9884093
62 royce 0.9881627
63 105021044 0.9879161
64 benq581 0.9876695
65 andysu840520 0.9874229
66 evankao 0.9874229
67 Colin 0.9871763
68 itri456133 0.9869297
69 Data-AI 0.9866831
70 J4James 0.9864364
71 wenlong 0.9864364
72 kevin32111@gmail.com 0.9859432
73 ERR1121 0.9854500
74 p692584k 0.9854500
75 Miles 0.9849568
76 a138b4a4 0.9842170
77 Hsing_Ting 0.9842170
78 tommyrpg1010 0.9839704
79 chaoziyin 0.9837237
80 jamychen1126 0.9832305
81 panzer 0.9824907
82 jakea91137 0.9824907
83 paul369xd 0.9819975
84 Sero8139 0.9819975
85 thanhphat1221 0.9815043
86 VanSuHuynh 0.9812577
87 hsieh 0.9812577
88 yonglee 0.9807644
89 hlping1248 0.9800246
90 m10817021 0.9800246
91 Allen 0.9797780
92 cheney235689 0.9797780
93 tommy860823 0.9797780
94 pups2468 0.9795314
95 mingj 0.9792848
96 taurus12079 0.9787916
97 M10813059 0.9787916
98 Lucas7246 0.9782983
99 heysun0728 0.9780517
100 xu4c941i6 0.9780517
101 Wiwi 0.9778051
102 ee255852 0.9775585
103 ian06013101799 0.9775585
104 lunaseaman 0.9773119
105 flovebrs 0.9768187
106 WindDYTING 0.9768187
107 igormorawski 0.9763255
108 chtte13 0.9760789
109 ChenJH 0.9755856
110 onesmall 0.9750924
111 yi.ting 0.9748458
112 m10817003 0.9743526
113 ning364 0.9741060
114 uun_rio 0.9741060
115 electron606b 0.9738594
116 RayChang 0.9736128
117 ProL 0.9731196
118 htsc108 0.9721331
119 tcglarry 0.9718865
120 Feng 0.9718865
121 HTSC_1011a_lab 0.9713933
122 wayne9458 0.9709001
123 larry 0.9704069
124 y987141 0.9699136
125 wyp8711 0.9696670
126 katychou 0.9689272
127 YCLo 0.9689272
128 b10556037 0.9686806
129 mimi75323 0.9686806
130 chris880622 0.9681874
131 gash8234 0.9667077
132 redsunmirage 0.9654747
133 cacycacy 0.9652281
134 GinaChen 0.9649815
135 shyuan 0.9642416
136 RogerHung 0.9639950
137 suryakumara33 0.9637484
138 amberliu1985 0.9635018
139 qwe846132 0.9635018
140 edonovanto 0.9612823
141 sevillachea 0.9610357
142 socrateschch 0.9605425
143 yomingjayism 0.9600493
144 logomacaca 0.9598027
145 jackevin 0.9590628
146 hsin8824 0.9580764
147 sleepingpandaaa 0.9573366
148 Hsiao7033 0.9573366
149 boomdeyadah 0.9546239
150 PeterCheng 0.9546239
151 build0220 0.9543773
152 t107598068 0.9543773
153 Fredericklee602 0.9521578
154 htchutw 0.9491985
155 kaede10263 0.9472256
156 aditya 0.9467324
157 Vivek 0.9467324
158 joker 0.9442663
159 geniuspingping 0.9442663
160 franco0088 0.9430332
161 ag150300 0.9408138
162 Sandra 0.9400739
163 Jack9512 0.9390875
164 yuanyuanjerry 0.9383477
165 Overcomer 0.9361282
166 jack8566662 0.9353884
167 M10813061 0.9351418
168 imlm0001 0.9324290
169 chouchienu 0.9324290
170 JasonDong 0.9270036
171 small77 0.9119605
172 doraemon 0.9097410
173 ES29 0.9092478
174 ping27942 0.9067817
175 cowboss779 0.9008631
176 gene3706 0.8939580
177 yuzjzj 0.8905055
178 Rjun 0.8658446
179 b10556007 0.8604192
180 ainglee 0.8579531
181 siyu026019 0.8453760
182 ccccu33333 0.8268803
183 cianyu150cjo 0.8256473
184 w10041656 0.7972872
185 gcpyobcmst001 0.7819975
186 syue 0.7778051
187 et10man 0.6912453
188 be3575 0.6848335
189 DuyDinh 0.4623921
190 pek9527 0.4545006
191 B10456005 0.3447595
192 taurus12079@gmail.com 0.3282367
193 or624 0.2663378
194 Mingyenchu 0.2660912
195 cynthiapr 0.2547472
196 evandio 0.2199753
197 timsimtho 0.2140567
198 Felixgunawan 0.2110974
199 Adam0800 0.2069050
200 irfan.frans 0.2046855
201 cwhuang.1021 0.1637484
202 hadean92 0.0009864
For the results announcement, please refer to the event time.