How do I choose the hyper-parameters for a model to detect different guitar chords?

Question

I need to build a hand detector that recognizes the chord played by a hand on a guitar.

I read this article Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation that looks like what I need (hand gesture recognition).

I think my task is (from my point of view) a little more difficult than that in the paper, because I think it is more difficult to distinguish between two chords than between a punch and a palm.

What I don't understand clearly is how to choose the best parameters for this more complex task: is it better to have more/less convolutional layers? A higher or lower number of poolings? Max or avg pooling?

The input will be more or less like this one:

There will be a first net (MobileNetV2 trained on EgoHands) that will find the bounding box, crops the image and then passes the saturated blending between the original one and Frei&Chen edges to the second net (unfortunately I don't have a processed picture yet, I will post an example as soon as I get it)

Answer 1

The ideal hyperparameters is usually dependent on your dataset and will differ on a case by case basis. Go for trial and error to determine the hyperparameters that works best for you.

Few research papers similar to your use case is listed below.

• CNN transfer learning for visual guitar chord classification
• A Study of Left Fingering Detection Using CNN for Guitar Learning
• A 3D Guitar Fingering Assessing System Based on CNN-Hand Pose Estimation and SVR-Assessment
• Applying Deep Learning Techniques to Estimate Patterns of Musical Gesture : forearm gestures on violin playing