This project involves developing an advanced image classification system leveraging deep learning techniques, specifically the ResNet50 architecture, and integrating it with a user-friendly Streamlit application. The project demonstrates the application of transfer learning, extensive data augmentation, and integration with generative AI models to enhance functionality and user experience.
Image classification is a crucial task in computer vision, involving categorizing and labeling images based on their content. This project utilizes ResNet50, a deep residual network known for its effectiveness and accuracy in image classification tasks, and Streamlit, an open-source Python library, to create an interactive web application for model deployment.
- Implement ResNet50 for accurate image classification.
- Develop a user-friendly Streamlit application for model interaction.
- Integrate generative AI for advanced image and text analysis.
Image classification involves assigning labels to images based on their content using convolutional neural networks (CNNs). ResNet50, with its 50-layer architecture, is utilized for its ability to learn deep representations and achieve high accuracy.
Image classification is applied in various fields, including healthcare, autonomous systems, retail, security, and environmental monitoring, enabling tasks such as disease diagnosis, object recognition, and surveillance.
Advantages: High accuracy, scalability, automation, and versatility.
Challenges: Data dependency, computational intensity, vulnerability to attacks, and interpretability issues.
This project aims to develop a robust image classification system using the ResNet50 model, fine-tuned on a custom dataset. The project incorporates data augmentation techniques and integrates a Streamlit-based user interface for enhanced user interaction.
The dataset consists of images organized into training, validation, and test sets, stored in a structured directory format essential for data loading and augmentation using TensorFlow's ImageDataGenerator.
Effective data preparation involves image preprocessing and augmentation to improve the model's generalization capabilities. Images are resized to 224x224 pixels and normalized. Data augmentation techniques include horizontal flipping, zooming, shifting, shearing, and rotation.
Transfer learning leverages the pre-trained ResNet50 model on the ImageNet dataset. Initial layers are frozen to retain learned features, while custom layers (GlobalAveragePooling2D, Dropout, Dense Layers) are added for the specific classification task.
The model is compiled with the Adam optimizer, categorical cross-entropy loss function, and accuracy metric. Callbacks for learning rate scheduling and early stopping enhance the training process.
The model is initially trained with frozen base layers, followed by fine-tuning with lower learning rates to improve performance. This approach balances retaining learned features and adapting to the new dataset.
The model's performance is evaluated on the test set, with metrics such as loss and accuracy computed. Visualizations of training/validation accuracy and loss over epochs help in understanding training dynamics.
Plots showcasing the model's accuracy and loss are included, along with an analysis of class distribution and potential biases.
The Streamlit application provides an interactive platform for users to upload images and receive classification results. The interface includes features for detailed analysis and user interaction.
Generative AI models (e.g., Gemini Pro Vision for image analysis and Gemini Pro for text insights) are integrated to enhance the application's capabilities, offering users deeper insights and comprehensive interaction options.
Innovative aspects include unique data augmentation strategies, regularization techniques, and a confidence threshold mechanism to handle uncertain samples. The user-centric design adds practical value to the project.
Future directions include exploring different pretrained models (e.g., InceptionV3, ResNet101), expanding the dataset for better generalization, and improving the application interface.
The project utilizes established Python libraries (TensorFlow, Keras) and runs on personal laptops, demonstrating technical feasibility without specialized hardware.
Using open-source libraries and tools ensures no direct costs, making the project economically feasible.
The project acknowledges longer processing times due to computational demands but remains feasible within typical development timelines.
This project demonstrates the potential of deep learning and generative AI in developing robust image classification systems. The combined approach of using transfer learning, data augmentation, and an interactive AI-powered application highlights the project's technical rigor and practical value.
Potential enhancements include exploring additional pretrained models, expanding the dataset, and refining the Streamlit application for better user experience.
https://arxiv.org/pdf/1512.03385