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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)

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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
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 Msdp 0.9945745
18 chengjiun.ma@gmail.com 0.9943279
19 jummy1124@gmail.com 0.9943279
20 Yozi 0.9943279
21 vincentwu 0.9943279
22 Hsing_Ting 0.9943279
23 0000 0.9940813
24 SnorLax 0.9938347
25 teds 0.9938347
26 JackKao 0.9935881
27 Jiang 0.9935881
28 jang_jian 0.9935881
29 gn00665219 0.9933415
30 wen_long 0.9933415
31 ridicon 0.9930949
32 tueswking511 0.9928483
33 hsh0703 0.9928483
34 Ted_Bear 0.9926017
35 desyandriani 0.9923551
36 pacude881 0.9918618
37 107378057 0.9918618
38 supertortoise 0.9918618
39 Davidwill 0.9916152
40 sampon0223 0.9913686
41 TeriTeri 0.9913686
42 linan 0.9911220
43 Rsys 0.9911220
44 lucas85062055 0.9908754
45 inture 0.9906288
46 Cat_MeowMeow 0.9906288
47 A610050A 0.9903822
48 XiangChen 0.9903822
49 zolo1122 0.9901356
50 TerrenceChou 0.9901356
51 Tony_CHU 0.9901356
52 painting 0.9898890
53 johnny88850tw 0.9896424
54 chihyuu 0.9893958
55 slot9004 0.9893958
56 PDFwithData 0.9891491
57 hmyeh 0.9891491
58 ab30310 0.9886559
59 franky 0.9884093
60 86oujohnny 0.9884093
61 royce 0.9881627
62 7777 0.9881627
63 cyl02 0.9881627
64 105021044 0.9879161
65 evankao 0.9879161
66 benq581 0.9876695
67 andysu840520 0.9874229
68 Colin 0.9871763
69 itri456133 0.9869297
70 Data-AI 0.9866831
71 J4James 0.9864364
72 wenlong 0.9864364
73 kevin32111@gmail.com 0.9859432
74 jakea91137 0.9859432
75 ERR1121 0.9854500
76 p692584k 0.9854500
77 Miles 0.9849568
78 a138b4a4 0.9842170
79 tommyrpg1010 0.9839704
80 chaoziyin 0.9837237
81 jamychen1126 0.9832305
82 panzer 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 gino6178 0.9797780
95 pups2468 0.9795314
96 mingj 0.9792848
97 ProL 0.9790382
98 taurus12079 0.9787916
99 M10813059 0.9787916
100 Lucas7246 0.9782983
101 heysun0728 0.9780517
102 xu4c941i6 0.9780517
103 Wiwi 0.9778051
104 epriwahyu 0.9778051
105 suryakumara33 0.9778051
106 ee255852 0.9775585
107 ian06013101799 0.9775585
108 lunaseaman 0.9773119
109 flovebrs 0.9768187
110 WindDYTING 0.9768187
111 igormorawski 0.9763255
112 chtte13 0.9760789
113 CnnClassProject 0.9758323
114 ChenJH 0.9755856
115 cwhuang.1021 0.9753390
116 onesmall 0.9750924
117 yi.ting 0.9748458
118 CSIE 0.9748458
119 m10817003 0.9743526
120 ning364 0.9741060
121 uun_rio 0.9741060
122 electron606b 0.9738594
123 RayChang 0.9736128
124 johnny5151 0.9723797
125 htsc108 0.9721331
126 tcglarry 0.9718865
127 Feng 0.9718865
128 HTSC_1011a_lab 0.9713933
129 wayne9458 0.9709001
130 larry 0.9704069
131 y987141 0.9699136
132 wyp8711 0.9696670
133 M10813061 0.9694204
134 katychou 0.9689272
135 YCLo 0.9689272
136 b10556037 0.9686806
137 mimi75323 0.9686806
138 chris880622 0.9681874
139 gash8234 0.9667077
140 syue 0.9657213
141 redsunmirage 0.9654747
142 cacycacy 0.9652281
143 GinaChen 0.9649815
144 shyuan 0.9642416
145 RogerHung 0.9639950
146 amberliu1985 0.9635018
147 qwe846132 0.9635018
148 edonovanto 0.9612823
149 sevillachea 0.9610357
150 doraemon 0.9607891
151 socrateschch 0.9605425
152 yomingjayism 0.9600493
153 logomacaca 0.9598027
154 jackevin 0.9590628
155 hsin8824 0.9580764
156 sleepingpandaaa 0.9573366
157 Hsiao7033 0.9573366
158 boomdeyadah 0.9546239
159 PeterCheng 0.9546239
160 build0220 0.9543773
161 t107598068 0.9543773
162 ag150300 0.9528976
163 Fredericklee602 0.9521578
164 kaede10263 0.9472256
165 aditya 0.9467324
166 Vivek 0.9467324
167 joker 0.9442663
168 geniuspingping 0.9442663
169 franco0088 0.9430332
170 Sandra 0.9400739
171 Jack9512 0.9390875
172 yuanyuanjerry 0.9383477
173 htchutw 0.9378545
174 Overcomer 0.9361282
175 jack8566662 0.9353884
176 imlm0001 0.9324290
177 chouchienu 0.9324290
178 JasonDong 0.9270036
179 small77 0.9119605
180 ES29 0.9092478
181 azb43210 0.9092478
182 park10817002 0.9087546
183 parker 0.9087546
184 ping27942 0.9067817
185 cowboss779 0.9008631
186 gene3706 0.8939580
187 yuzjzj 0.8905055
188 Plateman 0.8744759
189 Rjun 0.8658446
190 b10556007 0.8604192
191 ainglee 0.8579531
192 siyu026019 0.8453760
193 azb40321 0.8350184
194 ccccu33333 0.8268803
195 cianyu150cjo 0.8256473
196 w10041656 0.7972872
197 BradTing 0.7945745
198 M10817031 0.7861898
199 gcpyobcmst001 0.7819975
200 et10man 0.6912453
201 be3575 0.6848335
202 DuyDinh 0.4623921
203 pek9527 0.4545006
204 B10456005 0.3447595
205 taurus12079@gmail.com 0.3282367
206 or624 0.2663378
207 Mingyenchu 0.2660912
208 cynthiapr 0.2547472
209 evandio 0.2199753
210 timsimtho 0.2140567
211 Felixgunawan 0.2110974
212 Adam0800 0.2069050
213 irfan.frans 0.2046855
214 qwe794652 0.1346485
215 hadean92 0.0009864
For the results announcement, please refer to the event time.