DL Image Classification Model

• Tech Stack: Tensorflow, Keras, Python
• Github URL: Project Link

The genesis of my project stems from the innovative feature introduced by Apple, which allows for isolating images by subtracting the background. Inspired by this advancement, I embarked on a journey to revolutionize the identification of Indian bird species using deep learning techniques. Through the integration of ensemble learning, image augmentation, and background subtraction, I aimed to develop a robust model capable of precisely recognizing these avian species from images. This initiative addresses the pressing need within the research, conservation, and bird enthusiast communities for accurate and efficient methods of species identification, particularly in the context of India's rich and diverse avifauna.

To achieve this goal, I meticulously curated a comprehensive dataset of Indian bird species, ensuring adequate representation across training, validation, and testing subsets. Building upon Apple's innovative feature, I implemented sophisticated image preprocessing techniques, including custom augmentation and background subtraction, to optimize the dataset for training the model. Leveraging ensemble learning, I fused ResNet50 and MobileNet models, fine-tuning them to specialize in bird species classification.

This project has been instrumental in honing my skills in several key areas. Firstly, it has deepened my understanding of deep learning methodologies, particularly in the context of image classification tasks. Through hands-on experience with data curation, preprocessing, and model development, I have gained valuable expertise in handling real-world datasets and implementing advanced algorithms. Additionally, the project has sharpened my problem-solving skills, as I encountered and addressed various challenges throughout the development process. Overall, this project has provided a platform for me to apply theoretical knowledge to practical problems, significantly enhancing my proficiency in the field of deep learning and computer vision.