How much accuracy can a CNN get with CIFAR-10?

How much accuracy can a CNN get with CIFAR-10?

A very simple CNN with just one or two convolutional layers can likewise get to the same level of accuracy. I’m not sure about your NNet architecture, but I can get you to 78% test accuracy on CIFAR-10 with the following architecture (which is comparatively simpler and has fewer weights).

How many epochs does CIFAR 10 need to be validated?

Update the question so it’s on-topic for Cross Validated. Closed 2 years ago. Training after 15 epochs on the CIFAR-10 dataset seems to make the validation loss no longer decrease, sticking around 1.4 (with 60% validation accuracy).

How to calculate CIFAR 10 object recognition accuracy?

The label classes in the dataset are: The classes are completely mutually exclusive. It was collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. Let us visualize few of the images of test set using the python snippet given below. (x_train, y_train), (x_test, y_test) = cifar10.load_data ()

Can a neural network get above 60% accuracy?

The same architecture achieves 99.7% accuracy on test sets for MNIST. Please see the architecture below: (Note: I have tried increasing dropout and increasing/decreasing learning rate of the Adam optimizer to prevent overfitting, all this does is prevent overfitting but with both training and test set now having similar low accuracy around 60%).

When is classification accuracy is not enough information?

Classification accuracy alone is typically not enough information to make this decision. In this post, we will look at Precision and Recall performance measures you can use to evaluate your model for a binary classification problem. The breast cancer dataset is a standard machine learning dataset.

What should my accuracy be after clearing up data?

After clearing up the data now my accuracy goes up to %69. Still not enough to be good, but at least I can now work my way up from here now that the data is clear.