Datasets:
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Update README.md
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README.md
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@@ -62,7 +62,7 @@ The notMNIST dataset is a collection of images of letters from A to J in various
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The dataset is split into a training set and a test set. Each class has its own subdirectory containing images of that class. The directory structure is as follows:
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notMNIST/
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|-- train/
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| |-- A/
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| |-- B/
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| |-- A/
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| |-- B/
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| |-- ...
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-
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## Acknowledgements
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This is a pretty good dataset to train classifiers! According to Yaroslav:
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Judging by the examples, one would expect this to be a harder task
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than MNIST. This seems to be the case -- logistic regression on top of
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stacked auto-encoder with fine-tuning gets about 89% accuracy whereas
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same approach gives got 98% on MNIST. Dataset consists of small
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hand-cleaned part, about 19k instances, and large uncleaned dataset,
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500k instances. Two parts have approximately 0.5% and 6.5% label error
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rate. I got this by looking through glyphs and counting how often my
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guess of the letter didn't match it's unicode value in the font file.
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The dataset is split into a training set and a test set. Each class has its own subdirectory containing images of that class. The directory structure is as follows:
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```notMNIST/
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|-- train/
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| |-- A/
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| |-- B/
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| |-- A/
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| |-- B/
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| |-- ...
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| |-- J/```
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## Acknowledgements
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This is a pretty good dataset to train classifiers! According to Yaroslav:
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+
> "Judging by the examples, one would expect this to be a harder task
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than MNIST. This seems to be the case -- logistic regression on top of
|
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stacked auto-encoder with fine-tuning gets about 89% accuracy whereas
|
92 |
same approach gives got 98% on MNIST. Dataset consists of small
|
93 |
hand-cleaned part, about 19k instances, and large uncleaned dataset,
|
94 |
500k instances. Two parts have approximately 0.5% and 6.5% label error
|
95 |
rate. I got this by looking through glyphs and counting how often my
|
96 |
+
guess of the letter didn't match it's unicode value in the font file."
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