The Flower Classification using Convolutional Neural Networks (CNN) project employs advanced computer vision and machine learning techniques to automatically identify and classify different flower species based on visual features.Through training on diverse datasets, the model becomes adept at generalizing across a wide range of flowers, making it a versatile solution for ecological research, education, and beyond. In essence, the project democratizes botanical knowledge, offering a powerful resource for individuals interested in exploring the diverse world of flowers.
The Flower Classification project employs a meticulous approach, starting with the curation of a diverse and well-labeled dataset for five flower species. Leveraging pre-trained CNN architectures like Xception, the model is designed with a custom classification head for precise identification. Data augmentation techniques enhance the dataset, and the model undergoes training and evaluation, with a focus on robust metrics. The user-friendly interface allows individuals to upload images for real-time classification, and the deployed model, accessible through web or mobile applications, showcases a seamless integration of advanced data science and deep learning techniques, providing a powerful tool for accurate flower species identification.The project also emphasizes robust evaluation metrics, including accuracy, precision, recall, and F1 score, to thoroughly assess the model's performance on both validation and test datasets.
- Importing Libraries
- Data Collection and Preparation
- Utilize Pre-trained Architectures (Xception)
- Feature Extraction and Model Architecture
- Data Augmentation and Preprocessing
- Model Training
- Model Evaluation
- User Interaction and Engagement
- Deployment and Integration
Training Accuracy - 98%
Testing Accuracy - 94%
Total Images:5000
Total Classes:5
Dataset: https://www.kaggle.com/datasets/kausthubkannan/5-flower-types-classification-dataset/data
We acknowledge Aditya Patil for their contributions to the project's accessibility and user-friendliness, making it more inclusive to a wider audience.
The code and content in this repository are licensed under MIT