r-cnn - tauweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/rcnn.pdfย ยท r-cnn test time per...
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![Page 1: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/1.jpg)
![Page 2: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/2.jpg)
R-CNN
![Page 3: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/3.jpg)
R-CNN
Over 2180
citations !
![Page 4: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/4.jpg)
R-CNN
![Page 5: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/5.jpg)
R-CNN
๐ด๐๐๐๐๐ โ ๐ฉ๐ โ ๐ท๐๐๐ ๐๐๐๐๐ ๐ฉ๐๐๐๐ ๐๐๐ ๐ฉ๐๐
๐ช๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐๐ ๐๐๐ ๐ ๐๐๐๐๐๐๐๐
๐ฎ๐๐๐๐๐ ๐ป๐๐๐๐ โ ๐ฉ๐๐ โ ๐จ๐๐๐๐๐ ๐ฉ๐๐๐๐ ๐๐๐ ๐ฉ๐๐
![Page 6: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/6.jpg)
R-CNN
๐จ๐๐๐ ๐ถ๐๐๐๐๐๐ โ ๐ฐ๐๐ผ โ๐จ๐๐๐(๐ฉ๐ โฉ ๐ฉ๐๐)
๐จ๐๐๐(๐ฉ๐ โช ๐ฉ๐๐)
๐ด๐๐๐๐๐ โ ๐ฉ๐ โ ๐ท๐๐๐ ๐๐๐๐๐ ๐ฉ๐๐๐๐ ๐๐๐ ๐ฉ๐๐
๐ช๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐๐ ๐๐๐ ๐ ๐๐๐๐๐๐๐๐
๐ฎ๐๐๐๐๐ ๐ป๐๐๐๐ โ ๐ฉ๐๐ โ ๐จ๐๐๐๐๐ ๐ฉ๐๐๐๐ ๐๐๐ ๐ฉ๐๐
๐ช๐๐๐๐๐๐ ๐ซ๐๐๐๐๐๐๐๐: ๐ฐ๐๐ผ >๐
๐
![Page 7: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/7.jpg)
R-CNN
๐ด๐๐๐๐๐ โ ๐ฉ๐ โ ๐ท๐๐๐ ๐๐๐๐๐ ๐ฉ๐๐๐๐ ๐๐๐ ๐ฉ๐๐
๐ช๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐๐ ๐๐๐ ๐ ๐๐๐๐๐๐๐๐
๐ฎ๐๐๐๐๐ ๐ป๐๐๐๐ โ ๐ฉ๐๐ โ ๐จ๐๐๐๐๐ ๐ฉ๐๐๐๐ ๐๐๐ ๐ฉ๐๐
๐จ๐๐๐๐๐๐ ๐ท๐๐๐๐๐๐๐๐ โ ๐จ๐ท
๐จ๐๐๐ ๐ถ๐๐๐๐๐๐ โ ๐ฐ๐๐ผ โ๐จ๐๐๐(๐ฉ๐ โฉ ๐ฉ๐๐)
๐จ๐๐๐(๐ฉ๐ โช ๐ฉ๐๐)
๐ช๐๐๐๐๐๐ ๐ซ๐๐๐๐๐๐๐๐: ๐ฐ๐๐ผ >๐
๐
![Page 8: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/8.jpg)
R-CNN
๐ด๐๐๐๐๐ โ ๐ฉ๐ โ ๐ท๐๐๐ ๐๐๐๐๐ ๐ฉ๐๐๐๐ ๐๐๐ ๐ฉ๐๐
๐ช๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐๐ ๐๐๐ ๐ ๐๐๐๐๐๐๐๐
๐ฎ๐๐๐๐๐ ๐ป๐๐๐๐ โ ๐ฉ๐๐ โ ๐จ๐๐๐๐๐ ๐ฉ๐๐๐๐ ๐๐๐ ๐ฉ๐๐
๐ด๐๐๐ ๐จ๐๐๐๐๐๐ ๐ท๐๐๐๐๐๐๐๐ โ ๐๐จ๐ท โ๐ด๐๐๐( ๐จ๐ท ๐๐๐๐ ๐๐๐ ๐๐๐๐๐ )
๐จ๐๐๐๐๐๐ ๐ท๐๐๐๐๐๐๐๐ โ ๐จ๐ท
๐จ๐๐๐ ๐ถ๐๐๐๐๐๐ โ ๐ฐ๐๐ผ โ๐จ๐๐๐(๐ฉ๐ โฉ ๐ฉ๐๐)
๐จ๐๐๐(๐ฉ๐ โช ๐ฉ๐๐)
๐ช๐๐๐๐๐๐ ๐ซ๐๐๐๐๐๐๐๐: ๐ฐ๐๐ผ >๐
๐
![Page 9: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/9.jpg)
R-CNN
![Page 10: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/10.jpg)
R-CNN
R-CNN
![Page 11: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/11.jpg)
R-CNN
![Page 12: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/12.jpg)
R-CNN
Input image
![Page 13: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/13.jpg)
R-CNN
Input image
Regions of interest (ROI)
from a proposal method
(~2k)
![Page 14: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/14.jpg)
R-CNN
Input image
Warped image regions
Regions of interest (ROI)
from a proposal method
(~2k)
![Page 15: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/15.jpg)
R-CNN
Input image
Forward each region
through ConvNet
Warped image regions
Regions of interest (ROI)
from a proposal method
(~2k)
![Page 16: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/16.jpg)
R-CNN
Classify each region with SVMs
Regions of interest (ROI)
from a proposal method
(~2k)
Warped image regions
Forward each region
through ConvNet
Input image
![Page 17: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/17.jpg)
R-CNN
![Page 18: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/18.jpg)
R-CNN
mini batch size
of 128
![Page 19: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/19.jpg)
R-CNN
![Page 20: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/20.jpg)
R-CNN
Better
mAP of
3-5%
![Page 21: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/21.jpg)
R-CNN
![Page 22: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/22.jpg)
R-CNN
![Page 23: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/23.jpg)
R-CNN
![Page 24: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/24.jpg)
R-CNN
Input image
Regions of interest
(ROI) from a proposal
method (~2k)
Warped image regions
Forward each region
through ConvNet
Classify each region with
SVMsApply
bounding box
regressors
![Page 25: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/25.jpg)
R-CNN
![Page 26: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/26.jpg)
R-CNN
![Page 27: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/27.jpg)
R-CNN
![Page 28: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/28.jpg)
R-CNN
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R-CNN
![Page 30: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/30.jpg)
R-CNN
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R-CNN
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R-CNN
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R-CNN
![Page 34: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/34.jpg)
R-CNN
arXiv: 1504.08083 (2015):
By: Ross Girshick, Microsoft Reasearch
![Page 35: R-CNN - TAUweb.eng.tau.ac.il/deep_learn/wp-content/uploads/2017/01/RCNN.pdfย ยท R-CNN Test Time per Image using VGG-16 Detection mAP on PASCAL VOC 2007 2010 2012 R-CNN 62.4 53.7 58.5](https://reader031.vdocuments.mx/reader031/viewer/2022022119/5e3869dcf0241d3b6e5952fa/html5/thumbnails/35.jpg)
R-CNN
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R-CNN
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R-CNN
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R-CNN
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R-CNN
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R-CNN
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R-CNN
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R-CNN
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R-CNN
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R-CNN
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R-CNN
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R-CNN
๐ณ ๐, ๐, ๐๐, ๐ = ๐ณ๐๐๐(๐, ๐) + ๐บ โ ๐ โฅ ๐ โ ๐ณ๐๐๐(๐๐, ๐)
p = ๐0, ๐1, โฆ , ๐๐พ
๐ก๐ = ๐ก๐ฅ๐ , ๐ก๐ฆ
๐ , ๐ก๐ค๐ , ๐กโ
๐
over K + 1 categories
For each of the K object classes, indexed by k
๐ be the ground truth class of the RoI
๐ be the ground truth bounding box
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R-CNN
๐ณ ๐, ๐, ๐๐, ๐ = ๐ณ๐๐๐(๐, ๐) + ๐บ โ ๐ โฅ ๐ โ ๐ณ๐๐๐(๐๐, ๐)
๐ณ๐๐๐ ๐, ๐ = โ๐๐๐ ๐๐
๐บ โ ๐น๐๐๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐
๐ โฅ ๐ โ ๐ญ๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐
๐ณ๐๐๐ ๐๐, ๐ =
๐โ ๐,๐,๐,๐
๐๐๐๐๐๐๐ณ๐(๐๐๐ โ ๐๐)
๐๐๐๐๐๐๐ณ๐ ๐ = ๐. ๐ โ ๐๐, ๐ < ๐๐ โ ๐. ๐, ๐ โฅ ๐
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R-CNN
๐๐๐ = ๐๐ โ(๐,๐)
๐ โ (๐, ๐) = ๐๐ซ๐ ๐ฆ๐๐ฑ๐โฒโ ๐ก ๐,๐
๐๐โฒ
๐๐ณ
๐๐๐=
๐
๐
[๐ = ๐โ(๐, ๐)]๐๐ณ
๐๐๐๐
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R-CNN
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R-CNN
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R-CNN
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R-CNN
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R-CNN
Neural Information Processing Systems (NIPS), 2015:
By: S. Ren, K. He, R. Girshick, J. Sun, Microsoft Research
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R-CNN
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R-CNN
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R-CNN
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R-CNN
OR
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R-CNN
๐ณ ๐๐ , ๐๐ =๐
๐ต๐๐๐
๐
๐ณ๐๐๐(๐๐, ๐๐โ) + ๐บ โ
๐
๐ต๐๐๐
๐
๐๐โ โ ๐ณ๐๐๐(๐๐, ๐๐
โ)
OR
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R-CNN
๐ณ ๐๐ , ๐๐ =๐
๐ต๐๐๐
๐
๐ณ๐๐๐(๐๐, ๐๐โ) + ๐บ โ
๐
๐ต๐๐๐
๐
๐๐โ โ ๐ณ๐๐๐(๐๐, ๐๐
โ)
๐ โ ๐๐๐๐๐๐ ๐๐๐ ๐๐
๐๐ โ ๐๐๐๐ ๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐ ๐๐ ๐๐๐๐๐๐ ๐ ๐๐๐๐๐ ๐๐ ๐๐๐๐๐๐
๐๐โ =
๐ , ๐๐๐๐๐๐๐๐ ๐ ๐๐ ๐ท๐๐๐๐๐๐๐๐ , ๐๐๐๐๐๐๐๐ ๐ ๐๐ ๐ต๐๐๐๐๐๐๐
๐ณ๐๐๐ ๐๐, ๐๐โ โ ๐๐๐ ๐๐๐๐ ๐๐๐๐ ๐๐๐ ๐๐๐๐๐๐๐
๐ต๐๐๐ โ ๐๐๐ ๐๐๐๐ โ ๐๐๐๐๐ ๐๐๐๐ (๐๐๐)
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R-CNN
๐ณ ๐๐ , ๐๐ =๐
๐ต๐๐๐
๐
๐ณ๐๐๐(๐๐, ๐๐โ) + ๐บ โ
๐
๐ต๐๐๐
๐
๐๐โ โ ๐ณ๐๐๐(๐๐, ๐๐
โ)
๐ณ๐๐๐ ๐๐, ๐๐โ = ๐๐๐๐๐๐๐ณ๐(๐๐ โ ๐๐
โ)
๐ก๐ฅ = ๐ฅ โ ๐ฅ๐ /๐ค๐
๐ก๐ฅโ = ๐ฅโ โ ๐ฅ๐ /๐ค๐
๐ก๐ฆ = ๐ฆ โ ๐ฆ๐ /โ๐
๐ก๐ฆโ = ๐ฆโ โ ๐ฆ๐ /โ๐
๐ก๐ค = ๐๐๐ ๐ค/๐ค๐
๐ก๐คโ = ๐๐๐ ๐คโ/๐ค๐
๐กโ = ๐๐๐ โ/โ๐
๐กโโ = ๐๐๐ โโ/โ๐
๐ต๐๐๐ โ ๐๐๐ ๐๐๐๐๐๐ ๐๐ ๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐ (~๐, ๐๐๐)
๐ท๐๐๐๐๐๐๐๐๐๐๐๐๐๐๐๐ ๐๐ ๐๐๐ ๐๐๐ ๐๐ ๐๐๐๐๐ ๐๐๐ ๐๐๐๐๐๐๐:
๐ฅ โ ๐กโ๐ ๐๐๐๐๐๐๐ก๐๐ ๐๐๐ ๐๐ก๐๐๐๐ก = (๐ก๐ฅ , ๐ก๐ฆ, ๐ก๐ค , ๐กโ) ๐ฅ๐ โ ๐กโ๐ ๐๐๐โ๐๐ ๐๐๐ ๐๐ก๐๐๐
๐ฅโ โ ๐กโ๐ ๐บ๐ ๐๐๐ ๐๐ก๐๐๐
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R-CNN
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R-CNN
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R-CNN
Test Time per Image
using VGG-16
Detection mAP on
PASCAL VOC
201220102007
47 Sec58.553.762.4R-CNN
300 mSec(Excluding object proposal time
For 2K proposals)
7068.868.4Fast R-CNN
200 mSecOverall time
73.2---70.4Faster R-CNN
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R-CNN
Thank You
For Listening
-
Any Questions ?