Identify digits from a dataset of tens of thousands of handwritten images
MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.
In this competition, your goal is to correctly identify digits from a dataset of tens of thousands of handwritten images. We’ve curated a set of tutorial-style kernels which cover everything from regression to neural networks. We encourage you to experiment with different algorithms to learn first-hand what works well and how techniques compare.
The data files train.csv and test.csv contain gray-scale images of hand-drawn digits, from zero through nine..
Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.
- train.csv : The training set (42,000 EA)
- test.csv : The test set (28,000 EA)
This competition is evaluated on the categorization accuracy of your predictions (the percentage of images you get correct).
- Model : Keras Sequential
- Kaggle Score : 0.98271
- Kaggle rank : 1,139 / 2,279 (49.9%)