HeyLongHoang/VGG16_CIFAR-10_pytorch

Pytorch implementation of VGG16 on CIFAR-10 | 90% accuracy

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Experimental

This project helps machine learning practitioners efficiently classify small images, like those found in basic object recognition datasets. You input a collection of color images (32x32 pixels) from the CIFAR-10 dataset, and it outputs predictions for what object is in each image, such as 'airplane' or 'cat'. It's designed for researchers and students working with foundational image classification tasks.

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Use this if you need a straightforward, effective way to classify small, common objects in images using a well-known neural network architecture.

Not ideal if you're working with very large images, highly complex classification tasks, or require state-of-the-art accuracy on more specialized datasets.

image-classification deep-learning-education computer-vision-research object-recognition neural-network-implementation
No License Stale 6m No Package No Dependents
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Adoption 5 / 25
Maturity 8 / 25
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Last pushed

May 21, 2023

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