How do you work with other computer vision pros?

As a data scientist, I've learned that it's not enough to just have a clear definition of the problem; each stakeholder must also understand and agree with it. Having regular meetings to talk about the project and going over the problem statement makes sure that the team doesn't get off track. As a project goes on, the problem might change, so it's important to be adaptable and make sure everyone is on the same page.

Having a complete set of tools is important, but it's also crucial to make sure that each team member is knowledgeable about and at ease using those tools. By doing this, the learning curve is reduced and productivity is increased. Additionally, keep an eye out for any updates to existing tools or emerging technologies, as these can bring about efficiencies or new features that will be very helpful to the project.

In my experience of artificial intelligence and computer vision, selecting the right tools is akin to assembling a perfect orchestra. Each project requires specific instruments for a harmonious workflow, considering factors like project scope, team expertise, and communication needs. For remote teams, like mine operating across different time zones, these tools are vital. They serve as bridges, enabling seamless collaboration despite geographical distances. Effective communication, real-time project tracking, and resource sharing are paramount, emphasizing the importance of well-chosen tools in ensuring smooth teamwork and exceptional outcomes.

In my experience, data sharing involves more than just technical considerations. Building trust among team members is another aspect of it. It's crucial to inform the team about the significance of the data and the repercussions of improper handling before sharing it, especially if it's sensitive data. Additionally, clear documentation of the source, structure, and anomalies of the data can save countless hours of model training time.

Collaborative work in the realm of computer vision is greatly facilitated by sharing data. Through collaborative platforms and secure data sharing methods, professionals can collectively analyze, annotate, and improve datasets. Collaborative tools like Google Colab enable real-time joint efforts on complex projects, fostering a collaborative environment where insights are shared, and expertise is pooled. This collaborative approach not only refines models but also accelerates the pace of innovation, leading to groundbreaking advancements in the field of computer vision. By responsibly sharing data and insights, professionals can collectively push the boundaries of what AI and computer vision can achieve.

I like to think of code reviews as a chance for both the reviewer and the author to learn new things continually. It serves as a platform for knowledge sharing, allowing both experienced and inexperienced team members to gain insight into innovative approaches. Fostering an atmosphere of openness and respect for one another is essential for making the most of it.

In my experience, the review of code/model is an opportunity to learn and understand the coding standards. It is considered a knowledge-sharing base for every team member regardless of designation. This act of review eventually leads to high productivity, reusability, and immense confidence.

Code review is not just about improving code quality; it's also a powerful tool for knowledge-sharing among cv professionals. By reviewing each other's code, team members exchange diverse coding styles, problem-solving techniques, and innovative approaches. This fosters a culture of learning and continuous improvement within the team. Constructive feedback and discussions during code reviews enable the transfer of knowledge, helping team members understand complex algorithms, enhance coding skills, and stay updated with industry best practices. In essence, code review becomes a collaborative learning process, strengthening individual expertise and elevating collective knowledge, promoting excellence in cv development.

Testing is an ongoing process rather than just a once-off phase. Models should continue to be tested and calibrated after deployment in order to take changing circumstances into account. This guarantees that the model will continue to be accurate and relevant over time. Involving domain experts during testing is also a good idea because they can offer perspectives that a team comprised only of specialists might miss.

Productionizing CV models lies beyond the accuracy and speed of the model, but also the utilization of resources. If the business objective requires real-time model serving, do allocate sufficient amount of computing resources to ensure the streaming and inferencing goes smoothly. This is where the pros can advise you to the right direction. With that being said, one has to take account of the cost of operation as well to ensure that we obtain the right tradeoff between performance and cost. It's requires a tem effort from different domains to ensure the production is successful.

I find that presenting a computer vision project poses a genuine challenge because customers may not necessarily be concerned with the real-time performance or accuracy of the computer vision model. Instead, their primary interest lies in the analytics derived from the model and how effectively these insights are conveyed. In essence, you must adopt the role of a storyteller for the end-user. There are many interactive tools that you can use to display analytics from the algorithm-generated results, i.e. Streamlit, Dash, Plotly, Matplotlib, Seaborn, etc. This is an iterative process, the more iterate you will it, the better you can show the analysis to the end user.

Collaborate with computer vision experts on hands-on projects which encompass various frameworks, tools and Technologies. I recommend open source contributions to projects as it would build your team work, development skills and coding abilities. Effective Communication, Collaboration and Contributions are handy ways to work with computer vision pros, in my opinion. Also, review others's codes and get your codes reviewed to understand the feedback and how well your computer vision projects have performed.