DEEP LEARNING
DOG IDENTITY
This project focuses on building a robust deep learning model for accurately identifying dog breeds from images. By leveraging advanced machine learning techniques and transfer learning with pre-trained models, the system can classify dog breeds with high accuracy, providing a practical solution for applications such as pet adoption, veterinary services, and animal recognition platforms.
KEY FEATURES
- Key Features:
+ Implemented Convolutional Neural Networks (CNNs) to extract features and classify images of dog breeds.
+ Utilized transfer learning with pre-trained models such as VGG16 and ResNet50, improving accuracy while minimizing training time.
+ Employed data augmentation techniques to increase dataset variability, enhancing model generalization.
+ Fine-tuned the model to achieve high accuracy in classifying over 100 dog breeds.
+ Evaluated model performance using metrics such as accuracy, precision, recall, and F1 score.
Technologies Used
+ Languages: Python
+ Frameworks: TensorFlow, Keras, PyTorch
+ Tools: Jupyter Notebook, NumPy, Pandas
+ Techniques: Transfer Learning, CNNs, Image Preprocessing, Data Augmentation
Developed using Python with TensorFlow, Keras, and PyTorch for deep learning, along with Jupyter Notebook, NumPy, and Pandas for data analysis and manipulation.
Outcome
Outcome
The model successfully identifies dog breeds from images with a high degree of accuracy, offering potential applications in areas such as pet recognition apps, veterinary clinics, and breed-specific research.
FUTURE DIRECTION