Essential Skills and Strategies for Landing a Computer Vision Job

The other week I received a question from one of the members of the Ground Truth community and this post tries to address it. I'm sure this question resonates with many, and I sincerely hope it will be helpful.

If you have a computer vision question you'd like to direct to the ML Engineers at Superb AI, don't hesitate to submit it here.

Now, let's get down to business.

❓️Question

“Any tips on navigating the computer vision field? I feel I'm going in the right direction; learning Pytorch, and reading books and papers on computer vision(image classification, detection and segmentation), but what’s next after doing all these? There seem to be many subfields. How does one decide what to do/learn next especially if the goal is to learn as fast as possible, in order to get up to date with current trends and get a job?”

🤓 Answer

Navigating the field of computer vision can indeed be daunting, given its considerable breadth and depth. Your journey in this area will be heavily influenced by your previous experience, personal interests, and the specific role you're aiming for in the computer vision industry. The diversity of roles and responsibilities within the industry itself adds to this complexity.

However, there are a few common tips that seasoned professionals in the computer vision field often share. To guide your way, let's break these down into two categories: strategies to land a job in the field and the skills necessary to excel in the industry.

Strategies To Land a Job in the Industry

1. Choosing a Subfield: Given the challenging nature of Computer Vision, passion is your best compass. Identify a subfield in computer vision that genuinely excites you. For instance, if autonomous driving fascinates you, research leading companies in this field, study their job listings and understand the requirements for your desired role.

2. Get your Hands Dirty: Nothing beats practical, hands-on experience. Implementing projects from start to finish, dealing with real-world data, and seeing your models in action can provide invaluable learning opportunities. Implement them the best you can and share them on GitHub.

3. Understand the Business Side: Beyond technical skills, it's crucial to understand the industry where your models will be deployed. This insight can help you develop solutions that are not only technically sound but also deliver real value.

4. Good Old-Fashioned Network: Connect with computer vision engineers to learn about their work and experiences. Attend relevant meetups and events, discuss your projects, and build relationships. These connections can open up future opportunities.

5. Stay Current: The field of computer vision evolves rapidly. Keeping up-to-date with the latest research, techniques, and tools is essential. Regularly reading relevant news sources, and blogs, and participating in online communities can help.

6. Hang in There: The journey may be challenging, but every expert started somewhere. Keep pushing forward, and success will surely follow!

Trends & Skills to Thrive in the Industry

1. Blend Deep Learning with Classical Techniques: Not every computer vision task demands deep learning. Mix and match your deep learning skills with projects that enable you to delve into geometry, camera calibration, image enhancement, and the like. For instance, you could develop a program to stitch panoramic images from single shots, or one that clusters your images by similarity to weed out repetitive shots.

2. Adopt a Data-Centric Mindset: In the real world, datasets are often messy and unorganized, or sometimes non-existent. Embracing a data-centric approach will help you handle such situations, focusing on improving data quality rather than tweaking the model.

3. The Art of Explainability: As AI models become more complex, it's increasingly important to understand and explain how they make decisions. This is especially crucial in the CV field where decisions have significant consequences, like healthcare or autonomous vehicles.

4. Diversify Your Programming Knowledge: While Python and PyTorch are essential, don't limit yourself. C++ appears on almost every CV engineering job listing, so having a good handle on it can be beneficial.

5. Master Optimization Techniques: Learn how to optimize your models for real-world performance. This could involve speeding up inference times, reducing memory usage, or adapting models for different hardware platforms.

That wraps up what I have for you today. 🤗 

I hope this information proves useful in your journey through the vast landscape of computer vision.

To all Ground Truth members who are working in the field of Computer Vision - we'd love to hear about your experiences. How did you carve out your path in the industry? What skills and strategies have you found to be the most valuable? What advice would you offer to those aspiring to venture into this thrilling field? Please share your insights and continue the discussion in the comments section below. 👇️