NL2032560B1 - Deep learning model performance evaluation method and system - Google Patents
Deep learning model performance evaluation method and system Download PDFInfo
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- NL2032560B1 NL2032560B1 NL2032560A NL2032560A NL2032560B1 NL 2032560 B1 NL2032560 B1 NL 2032560B1 NL 2032560 A NL2032560 A NL 2032560A NL 2032560 A NL2032560 A NL 2032560A NL 2032560 B1 NL2032560 B1 NL 2032560B1
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- 238000011156 evaluation Methods 0.000 title claims abstract description 62
- 238000013136 deep learning model Methods 0.000 title claims abstract description 41
- 230000014759 maintenance of location Effects 0.000 claims abstract description 12
- 239000011159 matrix material Substances 0.000 claims description 28
- 238000013507 mapping Methods 0.000 claims description 25
- 238000000034 method Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000000750 progressive effect Effects 0.000 claims description 6
- 238000013135 deep learning Methods 0.000 description 23
- 230000011218 segmentation Effects 0.000 description 18
- 238000004519 manufacturing process Methods 0.000 description 11
- 238000012549 training Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000000717 retained effect Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/776—Validation; Performance evaluation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/457—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by analysing connectivity, e.g. edge linking, connected component analysis or slices
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210034162.1A CN114049569B (zh) | 2022-01-13 | 2022-01-13 | 一种深度学习模型性能评价方法及系统 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| NL2032560A NL2032560A (en) | 2023-07-19 |
| NL2032560B1 true NL2032560B1 (en) | 2024-01-08 |
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| Application Number | Title | Priority Date | Filing Date |
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| NL2032560A NL2032560B1 (en) | 2022-01-13 | 2022-07-21 | Deep learning model performance evaluation method and system |
Country Status (2)
| Country | Link |
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| CN (1) | CN114049569B (zh) |
| NL (1) | NL2032560B1 (zh) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114241326B (zh) * | 2022-02-24 | 2022-05-27 | 自然资源部第三地理信息制图院 | 一种渐进式遥感影像地物要素智能生产方法及系统 |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106846344B (zh) * | 2016-12-14 | 2018-12-25 | 国家海洋局第二海洋研究所 | 一种基于边缘完备度的图像分割最优识别方法 |
| CN109446992B (zh) * | 2018-10-30 | 2022-06-17 | 苏州中科天启遥感科技有限公司 | 基于深度学习的遥感影像建筑物提取方法及系统、存储介质、电子设备 |
| CN110232696B (zh) * | 2019-06-20 | 2024-03-08 | 腾讯科技（深圳）有限公司 | 一种图像区域分割的方法、模型训练的方法及装置 |
| WO2021152089A1 (en) * | 2020-01-30 | 2021-08-05 | Vitadx International | Systematic characterization of objects in a biological sample |
| US11830246B2 (en) * | 2020-05-01 | 2023-11-28 | CACI, Inc.—Federal | Systems and methods for extracting and vectorizing features of satellite imagery |
| CN111931782B (zh) * | 2020-08-12 | 2024-03-01 | 中国科学院上海微系统与信息技术研究所 | 语义分割方法、系统、介质及装置 |
| CN113033403A (zh) * | 2021-03-25 | 2021-06-25 | 生态环境部卫星环境应用中心 | 基于影像瓦片的生态保护红线地物目标识别方法及系统 |
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2022
- 2022-01-13 CN CN202210034162.1A patent/CN114049569B/zh active Active
- 2022-07-21 NL NL2032560A patent/NL2032560B1/en active
Also Published As
| Publication number | Publication date |
|---|---|
| CN114049569B (zh) | 2022-03-18 |
| NL2032560A (en) | 2023-07-19 |
| CN114049569A (zh) | 2022-02-15 |
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