Egg Damage Detection (Computer Vision Project)

Built a deep learning CNN model to classify egg images as damaged or non-damaged, aiming to automate quality inspection and reduce manual effort.

The dataset includes 634 damaged and 162 non-damaged egg images.

Applied data augmentation, image preprocessing/normalization, and EarlyStopping to improve generalization and reduce overfitting.

Tools used: Python, TensorFlowKeras Google Colab.