Contact Card Extractor
The Client.
Golfers who constantly lose their golf balls.
The Problem.
Thomas and I play golf on a regular basis and often find ourselves spending too much time trying to locate where our golf balls landed. It's frustrating to say the least, and often results in wasted time out on the course: Not great.
The Solution.
Use an iPhone mounted on a golf cart, combined with object detection algorithms and camera to find where the golf ball went. Using the right tools, I went from 0 to 1 on an embedded ML iPhone application within the span of 1 hour. How did I do it?
The Process.
I love golf AND I didn’t have my contacts on when I played last week 😣. It was incredibly difficult to find the ball, so I thought: OK, MVP for golf ball object detector on iPhone.
3…2..1.GO.
I hopped on Roboflow, which is my dataset annotation platform of choice, grabbed a small, public golf ball dataset. I then ran a quick fine-tuning training on YOLOV2 using Apple's CreateML framework, then stuffed that model into an iPhone App for inference. Within 1 hr later, voila!
The Result.
Proof-of-concept in 1 hour. From here, it’s just adding numbers and variety to the dataset (couple hundred). I’m always telling people that the end-to-end “synthesis” of a product is most important; Once you have that, then you can go make each piece as good as it needs to be.
Leading a project without first having something end-to-end is leading the project to nowhere. Start with fast spin-up, get feedback, iterate, improve quality, … → go to market.
