As a newbie to machine learning, I’m excited to share my journey of building a custom CNN model trained on real-time canvas drawings. I wanted to ditch the pre-made image datasets and instead, focus on fetching and preprocessing data in real-time for more accurate recognition and prediction. But, I hit a roadblock – how to collect and process data without relying on pre-existing datasets?
My solution? Use my phone as an input device to collect multiple base images. But, I needed a way to access my project on my phone over Wi-Fi. A fellow Reddit user, deepseek, suggested binding my dev server to my PC’s local IP (instead of localhost) to achieve this. Sounds simple, right? Well, it didn’t quite work out for me.
The Challenge
The main issue I faced was accessing my project on my phone over Wi-Fi. I tried binding my dev server to my PC’s local IP, but it didn’t work as expected. I was left wondering if I was going wrong somewhere or if there’s a better alternative to this approach.
Alternative Approaches
After some research, I discovered a few alternative approaches that could help overcome this challenge:
- Use a cloud-based platform: Services like Google Colab or AWS SageMaker can provide a cloud-based environment to collect and process data in real-time.
- Create a mobile app: Developing a mobile app that can collect and send images to my dev server could be a viable solution.
- Use a webcam: If I had a webcam, I could use it as an input device to collect images and process them in real-time.
Lessons Learned
Through this experience, I learned the importance of being flexible and open to alternative approaches when building a custom CNN model. Sometimes, what seems like a straightforward solution might not work as expected. But, with persistence and creativity, we can overcome these challenges and build a more accurate and effective model.
If you’re facing similar challenges or have any insights to share, I’d love to hear from you in the comments below!