Project Overview: Ground-Up Model Development Using Transformers

Inspiration

The inspiration for this project stemmed from the desire to explore the capabilities of Transformer models without relying on any pretrained generative models. We aimed to build a model from the ground up, focusing on training it solely on our datasets to understand its potential in image analysis.

Learning Outcomes

Throughout this project, I gained valuable insights into:

• The intricacies of training Transformer models from scratch.
• The importance of data quality and variety in achieving better model performance.
• The challenges of working with unlabeled datasets and the impact of limited computational resources.

Project Development

The project was built around a Transformer architecture specifically designed for image analysis. Here's how we approached the development:

1. Data Collection:

   • We compiled a dataset of approximately 2,000 to 30,000 images, ensuring diversity in the content to facilitate generalization.
   • Due to time constraints and limited GPU availability, we worked with unlabeled datasets, which posed additional challenges in the training process.

2. Model Training:

   • We implemented a Transformer model architecture tailored for image classification tasks.
   • The training involved feeding the model a random selection of images, where we posed a singular question about each image to guide the learning process.
   • The focus was on developing the model’s ability to generalize across various types of images, despite the limited dataset size.

3. Performance Measurement:

   • After training, we evaluated the model's accuracy, achieving a result of 63%. This indicated a promising foundation for further improvements and refinements.

Challenges Faced

The journey was not without its challenges:

• Limited Resources: Our lack of access to powerful GPUs hindered our ability to train on larger datasets or for extended periods, restricting the model's learning capacity.
• Unlabeled Data: Training on unlabeled datasets made it difficult to assess and optimize the model effectively, as we had to rely on indirect methods for evaluating its performance.
• Generalization: Achieving a model that could generalize well across different image types was a key challenge, requiring careful consideration of our training questions and image selection.

Conclusion

This project allowed me to delve deep into the mechanics of Transformer models and the intricacies of image analysis without relying on pretrained models. While we achieved an accuracy of 63%, this experience highlighted the potential for further enhancements and the importance of robust training strategies in future projects. I look forward to applying these lessons in subsequent endeavors to refine our approach and improve model performance.

Built With

• python
• sql
• transformer