Contents
What is a good accuracy for MNIST?
The MNIST Handwritten Digits dataset is considered as the “Hello World” of Computer Vision. Most standard implementations of neural networks achieve an accuracy of ~(98–99) percent in correctly classifying the handwritten digits.
Who is the best in MNIST?
MNIST 50 results collected
| Result | Method | Venue |
|---|---|---|
| 0.46% | Deep Convolutional Neural Networks as Generic Feature Extractors | IJCNN 2015 |
| 0.47% | Network in Network | ICLR 2014 |
| 0.52 % | Trainable COSFIRE filters for keypoint detection and pattern recognition | PAMI 2013 |
| 0.53% | What is the Best Multi-Stage Architecture for Object Recognition? | ICCV 2009 |
Why is MNIST important?
It is an extremely good database for people who want to try machine learning techniques and pattern recognition methods on real-world data while spending minimal time and effort on data preprocessing and formatting. Its simplicity and ease of use are what make this dataset so widely used and deeply understood.
How is the MNIST handwritten digit classification problem used?
The MNIST handwritten digit classification problem is a standard dataset used in computer vision and deep learning. Although the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch.
What is the best we can get with MNIST?
I’d like to determine the maximum accuracy we can hope with only a standard NN, (a few fully-connected hidden layers + activation function), with the MNIST digit database. I get a max of ~96.2% accuracy with: I read in the past that it’s possible that to get 98% even with a standard NN.
Which is the best neural network for MNIST?
Top-performing models are deep learning convolutional neural networks that achieve a classification accuracy of above 99%, with an error rate between 0.4 %and 0.2% on the hold out test dataset. The example below loads the MNIST dataset using the Keras API and creates a plot of the first nine images in the training dataset.
How is the MNIST database used in machine learning?
MNIST database. The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning.