This project aimed to develop a deep learning model for binary image classification. Leveraging the power of transfer learning, the base model utilized in this project was ResNet50 pre-trained on the 'imagenet' dataset. ResNet50 was chosen due to its deep architecture and proven effectiveness in various computer vision tasks.
- Base Model: ResNet50, a deep convolutional neural network renowned for its ability to handle complex visual data.
- Feature Extractor: ResNet50 served as the feature extractor, enabling the model to capture high-level features from input images efficiently.
- Residual Blocks: ResNet's residual blocks were pivotal in overcoming the vanishing gradient problem, facilitating smoother and more effective training.
- Transfer Learning: The project employed transfer learning to fine-tune the model on the specific dataset. By leveraging pre-trained weights from ResNet50, the model could effectively learn class-specific features while requiring less computational resources and data.
- Custom Head Layers: The custom head layers were added to adapt the model to the nuances of the dataset. These layers included:
- Average Pooling
- Flatten Layer
- Dense Layer (256 units, ReLU activation)
- Dropout Layer (50%)
- Output Layer (Softmax activation for classes)
The dataset used for this project consisted of binary images, with each image belonging to one of two classes. The dataset was carefully curated and pre-processed to ensure uniformity and quality, essential for effective model training.
The model was trained on the dataset using a suitable optimizer and loss function. The training process involved fine-tuning the custom head layers while keeping the weights of the ResNet50 base model fixed initially. Subsequently, the entire model was fine-tuned to further improve performance.
The performance of the model was evaluated using standard evaluation metrics such as accuracy, precision, recall, and F1 score. Additionally, visual inspection of confusion matrices and ROC curves provided insights into the model's performance and its ability to generalize to unseen data.
The trained model demonstrated robust performance in binary image classification, achieving high accuracy and other relevant metrics. The results were promising, indicating the effectiveness of the chosen model architecture and training approach.
In conclusion, this project showcased the efficacy of transfer learning and deep learning techniques in binary image classification tasks. The model architecture, leveraging ResNet50 as a feature extractor and custom head layers for fine-tuning, proved successful in capturing intricate patterns within the dataset. The project's codebase, along with trained models, is available on GitHub for further exploration and potential deployment in real-world scenarios.