AshkanGanj/CIFAR-10-classification-with-ConvNet-Architecture
CIFAR image classification with convolutional network architecture
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.
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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.
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MIT
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Last pushed
Nov 01, 2021
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