HeyLongHoang/VGG16_CIFAR-10_pytorch
Pytorch implementation of VGG16 on CIFAR-10 | 90% accuracy
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.
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May 21, 2023
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