code-alchemist01/CIFAR10-Deep-Learning-Comparison--
🖼️ CIFAR10 image classification project comparing ResNet18, ResNet34, and EfficientNet-B0 models with interactive Streamlit web application. Built with PyTorch, featuring real-time predictions and model performance analysis.
This tool helps you quickly classify images into 10 common categories like 'airplane', 'dog', or 'truck' by comparing how well different deep learning models perform. You upload an image, and it tells you what the image depicts, along with how confident each model is in its prediction. It's designed for anyone working with image datasets who needs to understand and compare the accuracy and efficiency of different image classification models.
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Use this if you need to evaluate and compare the performance of various deep learning models for image classification on a fixed set of common object categories.
Not ideal if you need to classify images outside of the 10 predefined CIFAR10 categories or if you need to train models on custom image datasets.
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Python
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MIT
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Sep 30, 2025
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