Deep learning performance on classifying simple geometric figures
$begingroup$
I'm trying to find papers or just performance data on classifying simple geometric shapes, e.g. the first six convex regular polygons. The input data is computer graphics generated, producing high contrast, clean images of convex regular polygons in various scales and orientations. One polygon per image. There is no limit, other than reasonable CPU time, on number of training images used. Testing is also done on randomly generated CG images.
Regular polygons are just an example. You can add circles, ellipses, non-regular polygons, etc. Basically any simple geometric shape, which a competent elementary school student can easily classify with 100% accuracy.
deep-learning image-classification
asked 3 mins ago
Paul JurczakPaul Jurczak
1164
$endgroup$
add a comment |
$begingroup$
I'm trying to find papers or just performance data on classifying simple geometric shapes, e.g. the first six convex regular polygons. The input data is computer graphics generated, producing high contrast, clean images of convex regular polygons in various scales and orientations. One polygon per image. There is no limit, other than reasonable CPU time, on number of training images used. Testing is also done on randomly generated CG images.
Regular polygons are just an example. You can add circles, ellipses, non-regular polygons, etc. Basically any simple geometric shape, which a competent elementary school student can easily classify with 100% accuracy.
deep-learning image-classification
asked 3 mins ago
Paul JurczakPaul Jurczak
1164
$endgroup$
add a comment |
$begingroup$
I'm trying to find papers or just performance data on classifying simple geometric shapes, e.g. the first six convex regular polygons. The input data is computer graphics generated, producing high contrast, clean images of convex regular polygons in various scales and orientations. One polygon per image. There is no limit, other than reasonable CPU time, on number of training images used. Testing is also done on randomly generated CG images.
Regular polygons are just an example. You can add circles, ellipses, non-regular polygons, etc. Basically any simple geometric shape, which a competent elementary school student can easily classify with 100% accuracy.
deep-learning image-classification
asked 3 mins ago
Paul JurczakPaul Jurczak
1164
$endgroup$
I'm trying to find papers or just performance data on classifying simple geometric shapes, e.g. the first six convex regular polygons. The input data is computer graphics generated, producing high contrast, clean images of convex regular polygons in various scales and orientations. One polygon per image. There is no limit, other than reasonable CPU time, on number of training images used. Testing is also done on randomly generated CG images.
Regular polygons are just an example. You can add circles, ellipses, non-regular polygons, etc. Basically any simple geometric shape, which a competent elementary school student can easily classify with 100% accuracy.
deep-learning image-classification
deep-learning image-classification
asked 3 mins ago
Paul JurczakPaul Jurczak
1164
asked 3 mins ago
Paul JurczakPaul Jurczak
1164
asked 3 mins ago
Paul JurczakPaul Jurczak
1164
asked 3 mins ago
Paul JurczakPaul Jurczak
1164
asked 3 mins ago
Paul JurczakPaul Jurczak
1164
1164
add a comment |
add a comment |
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