Question/Issue:
I am using FOMO (Faster Objects, More Objects) with MobileNetV2 0.35 for object detection in a Driver Monitoring System project. My goal is to export the trained model as TensorFlow Lite and deploy it on an NXP i.MX RT1176 EVK.
The model finishes training successfully and reports approximately 70% accuracy, but when I test it using Launch in Browser, the detections are not reliable. The model often fails to detect the expected classes or produces inconsistent results.
I would like to understand what is the issue that causing this outcome.
Project ID:
1064575
Project Link:
Context/Use case:
I am developing a Driver Monitoring System for an embedded device using Edge Impulse.
The model is trained to detect the following classes:
- Awake
- Drowsy
- Yawn
Each class currently contains approximately 600 labeled images.
The final model will be exported as a TensorFlow Lite model and deployed on an NXP i.MX RT1176 EVK.
Steps Taken:
- Initially trained the model with a smaller dataset.
- Increased the dataset size to approximately 600 images per class after reviewing the Edge Impulse documentation.
- Trained the model using:
Training cycles: 40
Learning rate: 0.001
Expected Outcome:
The model should reliably detect the Awake, Drowsy, Yawn and more lables classes during Live Classification and provide similar performance to the reported training metrics.
Actual Outcome:
While I was testing it always shows the Drowsy even though the eyes are open and yawning is detected sometimes only.
Reproducibility:
- Always
Environment:
- Platform: NXP i.MX RT1176 EVK
- Build Environment Details: MCUXpresso IDE
- OS Version: Windows 11