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This is an ultra-lightweight Convolutional Neural Network (CNN) with only 4.5K parameters, trained on the classic MNIST dataset for handwritten digit recognition.
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This is an ultra-lightweight Convolutional Neural Network (CNN) with only 4.5K parameters, trained on the classic MNIST dataset for handwritten digit recognition.
Despite its minimal size, the model achieves 94.48% accuracy on the test split, making it highly efficient for edge devices or CPU-only environments.
The full training workflow and source code can be found on GitHub and weights can be found on
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1 sourceVérifié 19 août
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2 extraits--- license: mit pipeline_tag: image-classification --- # Lightweight MNIST CNN (4.5K Parameters) This is an ultra-lightweight Convolutional Neural Network (CNN) with only 4.5K parameters, trained on the classic MNIST dataset for handwritten digit recognition. Despite its minimal size, the model achieves **94.48% accuracy** on the test split, making it highly efficient for edge devices or CPU-only environments. The full training workflow and source code can be found on [GitHub](https://github.com/aplominski/mnist-4.5k) and weights can be found on [Hugging Face](https://huggingface.co/aplominski/mnist-4.5k) ## Model Performance - **Task:** Image Classification (Handwritten Digits 0-9) - **Dataset:** MNIST - **Test Accuracy:** 94.48% - **Parameter Count:** ~4,500 ## Quick Start / Usage You can load and use this model directly using the code in github repository
You can load and use this model directly using the code in github repository
Source context: 0 downloads · 0 likes · Pipeline image-classification · Repo aplominski/mnist-4.5k
