AmirhosseinHonardoust/Image-Classification-CNN
Image classification with PyTorch using Convolutional Neural Networks (CNNs). Trains on MNIST with convolution, pooling, and fully connected layers. Achieves over 99% accuracy with early stopping and checkpoints. Includes training/evaluation scripts, metrics, confusion matrix, training curves, and sample prediction visualizations.
This project helps machine learning engineers and researchers quickly set up and train a Convolutional Neural Network (CNN) for image classification. You provide the image dataset, and it outputs a trained model, performance metrics like accuracy, and visualizations such as training curves and confusion matrices. It's ideal for those working on computer vision tasks.
No commits in the last 6 months.
Use this if you are a machine learning engineer needing a robust, high-accuracy baseline for classifying handwritten digits or similar image data.
Not ideal if you need a pre-trained model for immediate inference without any custom training or model development.
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Language
Python
License
MIT
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Last pushed
Sep 11, 2025
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