Plant Identification Using Convolution Neural Network and Vision Transformer-Based Models

Recently, my team published a vision paper, providing valuable insights and lessons which will benefit our future work. Here I highlight those key experiences and challenges: 

Plant Identification Using Convolution Neural Network and Vision Transformer-Based Models

• First, we grappled with open-ended questions in our problem statement, requiring us to think critically and flexibly.
• Second, we used past experiences, research approaches, and current vision models to craft our unique approach for this paper.
• Third, was the phase of experimenting which we had to analyze, timebox, and finalize.
• We also faced data challenges, drawing inspiration from similar research papers to overcome this hurdle.
• An important achievement for us was reaching state-of-art accuracy in our findings.
• We considered the scalability of our approach, contemplating how it can be implemented as we include multiple categories/classes.
• Focus was directed toward developing a repeatable architecture and effectively capturing feedback for continuous improvement.
• A significant portion of our time was dedicated to extensive documentation, conducting numerous experiments, and evaluating metrics.
• We navigated through the publication process, ensuring our work reached the right platforms.
• Lastly, we sought collaboration with like-minded clients, with whom we could work on making our learning reusable.

This experience has been thoroughly enriching for our team and we remain excited about our journey ahead

Keep Learning!!!