Classic MNIST digit recognition hitting 99.25% accuracy with a basic Keras CNN setup - 2 conv layers (32 and 64 filters), maxpooling, dropout regularization (0.25 and 0.5), and softmax output. Architecture is straightforward: Conv2D → Conv2D → MaxPool → Dropout → Flatten → Dense(128) → Dense(10).
The model trains for 12 epochs with batch size 128 using Adadelta optimizer and categorical crossentropy loss. Input normalization divides pixel values by 255 to get [0,1] range.
Pushing to 99.699% accuracy requires tweaks like data augmentation (rotation, translation), deeper architectures, batch normalization, or ensemble methods. MNIST remains the "hello world" benchmark for computer vision - if your model can't crack 99%+ here, something's fundamentally broken in your pipeline.
Fun fact: human error rate on MNIST is around 0.2%, so we're approaching biological performance with relatively simple architectures.
The model trains for 12 epochs with batch size 128 using Adadelta optimizer and categorical crossentropy loss. Input normalization divides pixel values by 255 to get [0,1] range.
Pushing to 99.699% accuracy requires tweaks like data augmentation (rotation, translation), deeper architectures, batch normalization, or ensemble methods. MNIST remains the "hello world" benchmark for computer vision - if your model can't crack 99%+ here, something's fundamentally broken in your pipeline.
Fun fact: human error rate on MNIST is around 0.2%, so we're approaching biological performance with relatively simple architectures.