BERT-CNN Text Classification and ResNet Image Classification
Inspiration:
Our project's inspiration came from exploring innovative approaches for text and image classification tasks. We were inspired by the recent advancements in deep learning, particularly the effectiveness of models like BERT and ResNet in capturing complex patterns in text and images, respectively. Our goal was to leverage the strengths of these models to build robust classifiers for real-world applications.
What We Learned:
Throughout the project, we learned valuable lessons about:
- The importance of preprocessing and data augmentation techniques for enhancing model performance.
- The significance of fine-tuning pre-trained models like BERT and ResNet for domain-specific tasks.
- The challenges and considerations involved in integrating different deep learning architectures into cohesive pipelines for classification tasks.
- The impact of hyperparameter tuning, optimizer selection, and learning rate scheduling on model convergence and performance.
How We Built Our Project:
Our project involved several key steps:
- Data Preparation: We collected and preprocessed datasets for both text and image classification tasks, ensuring data quality and compatibility with the chosen models.
- Model Architecture: We designed and implemented a BERT-CNN architecture for text classification, leveraging BERT embeddings and CNN layers for feature extraction. For image classification, we utilized a pre-trained ResNet model with modifications to the final classification layer.
- Training and Evaluation: We trained our models using appropriate loss functions, optimizers, and learning rate schedules. We evaluated model performance on validation datasets and fine-tuned hyperparameters for optimal results.
- Challenges: Throughout the project, we encountered challenges such as model convergence, resource constraints, and fine-tuning complexities. However, we addressed these challenges through experimentation, research, and collaboration.
- Results and Iteration: We analyzed the results of our trained models, iterated on our approaches, and implemented enhancements to improve classification accuracy and robustness.
Challenges We Faced:
Some of the main challenges we encountered during the project include:
- Optimizing computational resources for training large-scale models like BERT and ResNet.
- Tuning hyperparameters effectively to balance model complexity and performance.
- Handling class imbalances and dataset biases in both text and image datasets.
- Addressing issues related to overfitting and underfitting during model training.
Next Steps:
Looking ahead, we plan to:
- Explore advanced techniques for text preprocessing and feature extraction to enhance the performance of our BERT-CNN model further.
- Investigate transfer learning strategies for image classification tasks, including fine-tuning on larger datasets or experimenting with different backbone architectures.
- Extend our project to incorporate multimodal learning approaches, combining text and image data for more comprehensive classification tasks.
We hope to contribute to the collective knowledge and advancement of deep learning applications in classification tasks by sharing our journey, insights, and outcomes.