Insect Species Classification, a deep learning based computer vision system
designed to identify and classify multiple insect species from image datasets.
The project focuses on image preprocessing, feature extraction,
and Convolutional Neural Network (CNN) based classification
to achieve accurate species prediction for biodiversity and ecological analysis tasks.
Developed an image classification pipeline
for automated insect species recognition using deep learning techniques.
Processed and analyzed large image datasets,
including image loading, resizing, normalization,
and label encoding workflows.
Applied data augmentation techniques such as rotation,
flipping, zooming, and scaling to improve model generalization.
Designed and trained Convolutional Neural Network (CNN) models
for multi-class insect species classification tasks.
Utilized TensorFlow and Keras frameworks
with GPU acceleration for efficient deep learning model training.
Evaluated model performance using training and validation accuracy metrics,
achieving approximately 98% training accuracy and 91% validation accuracy and 90% test accuracy.
Optimized hyperparameters including batch size,
image dimensions, epochs, and augmentation strategies
for improved classification performance.