Image Classifier
A deep learning-based image classification system built on the CIFAR-10 dataset using a 2D Convolutional Neural Network (CNN). The project evaluates model performance with accuracy, precision, recall, and F1 score, emphasizing model optimization and overfitting analysis.
Domain
Python, ML and AI
The Goal:
The primary goal of this project was to build a robust image classification model capable of accurately identifying objects from the CIFAR-10 dataset. Leveraging a Convolutional Neural Network architecture, the project aimed to explore how well deep learning models can generalize across diverse image classes while maintaining high performance.
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The Challenge:
One of the main challenges was managing overfitting during training. The dataset's complexity and variability made it difficult to maintain generalization without loss of accuracy. Careful tuning of hyperparameters, including the learning rate and number of epochs, as well as the use of normalization and one-hot encoding, were crucial in developing a stable model.
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The Result
The trained CNN model achieved competitive performance metrics, showcasing strong accuracy and balanced precision-recall values. The final model demonstrated that, with the right architecture and optimization strategy, even a relatively simple deep learning setup can yield reliable results on real-world image classification tasks.
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