AshkanGanj/CIFAR-10-classification-with-ConvNet-Architecture

CIFAR image classification with convolutional network architecture

20
/ 100
Experimental

This project helps machine learning engineers and researchers classify small color images into one of ten categories, such as 'airplane,' 'dog,' or 'truck.' It takes a dataset of 32x32 pixel color images as input and outputs a trained convolutional neural network model capable of accurately predicting the category of new, unseen images. This is for someone building or evaluating image classification systems.

No commits in the last 6 months.

Use this if you are a machine learning practitioner looking for a straightforward example of image classification using a convolutional neural network on a standard dataset.

Not ideal if you are looking for a solution to classify images larger than 32x32 pixels or need a model for a custom, domain-specific image dataset without any foundational examples.

image-classification machine-learning-research computer-vision neural-networks model-training
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 0 / 25

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Language

Jupyter Notebook

License

MIT

Last pushed

Nov 01, 2021

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