AlexNet CNN Image Classifier

Upload an image to classify it into one of the CIFAR-10 categories.

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    <h3>Model Information</h3>
    <p>This model is trained on the CIFAR-10 dataset and can classify images into 10 categories: 
    plane, car, bird, cat, deer, dog, frog, horse, ship, and truck.</p>
    
    <h3>Model Architecture</h3>
    <pre>SimplifiedAlexNet(

(features): Sequential( (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU(inplace=True) (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): ReLU(inplace=True) (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (6): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (7): ReLU(inplace=True) (8): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (9): ReLU(inplace=True) (10): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (classifier): Sequential( (0): Dropout(p=0.5, inplace=False) (1): Linear(in_features=2048, out_features=512, bias=True) (2): ReLU(inplace=True) (3): Dropout(p=0.5, inplace=False) (4): Linear(in_features=512, out_features=10, bias=True) ) )

    <h3>Model Parameters</h3>
    <ul>
        <li>Total parameters: 1,295,050</li>
        <li>Trainable parameters: 1,295,050</li>
    </ul>
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