Building models for computer vision is similar to exploring a new frontier in technology. The adventure is not only thrilling but also fraught with challenges and surprises. We’ve had our fair share of victories and setbacks, and we would like to share our journey with you.
We tried to build a reliable computer vision model. We started with a theoretical approach, making use of countless academic articles, online courses, and textbooks. The method seemed fail-safe. Armed with knowledge and cutting-edge techniques, we began training our model.
And guess what? It did not go as planned. Our algorithm began to ‘hallucinate.’ Traditionally, we use this term when our model starts to see objects in images that aren’t there. Imagine an AI that sees a ‘cat’ in a picture of a desert. No matter how we tweaked the model or fiddled with the parameters, the results remained unsatisfactory. It may sound comical in retrospect, but it was a frustrating predicament for us.
The Pivot
When the theoretical approach didn’t work, we learned that it was time to pivot. We had to mix and match our strategies to continue moving forward. So we adopted an empirical approach and began experimenting with different architectures, just to see what would work. We tested different pre-processing techniques and switching to varied loss functions, only to be met with mixed successes.
Training a model that could look at an image and correctly interpret it proved especially difficult due to the ever-present chasm between the real-world and what our model was perceiving. It was akin to communicating between two different universes. Bridging that gap was our greatest challenge.
Lessons Gleaned
As we navigated through our series of trials and errors, we realized that a hybrid approach of using both theory and practice was the way forward. We made it a point to combine our knowledge from our research with actual hands-on experimentation. This mix enabled us to test various models and to analyze their strengths and weaknesses. We also understood the importance of adjusting our model to cater to the specific needs of the project at hand.
We also learned that in computer vision, and perhaps in many aspects of AI, there are rarely universal solutions. What works for one project might not work for another. This could be due to a multitude of factors, including the uniqueness of each project’s data or the varying goals of each undertaking. The key therefore is adaptability and winning could mean trying out different methods until you stumble upon the right mix.
Our adventure in building a reliable computer vision model was a thrilling roller-coaster ride, filled with twists and turns. Despite the setbacks we faced along the way, we placed persistence above all else, learning from every misstep and using those lessons to guide us forward.
It is important to remember that in the uncharted territories of AI and computer vision, the only sure failure is giving up too soon. It’s this very journey of trying, failing, learning, and retrying that ultimately leads us towards innovation and success in this exciting field.
For more in-depth insights on our journey, check out the original article here.
