The previous model trained was not performing well on the the testing dataset. So, I had to try a new approach for increasing the model accuracy as well as other parameters. When I tried training with other pre trained networks, after one epoch model started to overfit. So, I had to get creative.
I Used a BiT model for training, only the last layer was trainable. The model was able to perform better than pervious pre-trained model based on MobileNet.
Regarding the VR Part. The WebXR setup was successful. I was able to set up a basic WebXR environment. TensorflowJs was used for model inference. I used react-framework for binding TensorflowJS and WebXR together. For access to onboard device sensors the WebXR content has to be served through HTTPS.
Model Training Results
MobileNet Based Model: loss: 0.9046 - acc: 0.7174 - f1_m: 0.2450 - precision_m: 301254.4277 - recall_m: 0.2275 - val_loss: 1.0795 - val_acc: 0.6811 - val_f1_m: 0.2366 - val_precision_m: 248592.8750 - val_recall_m: 0.2270
BiT Based Model:loss: 0.6331 - acc: 0.8733 - f1_m: 0.8899 - precision_m: 195757.4552 - recall_m: 0.9773 - val_loss: 1.2562 - val_acc: 0.7955 - val_f1_m: 0.9079 - val_precision_m: 126641.6953 - val_recall_m: 1.0619
When the SavedModel is converted into TensorflowJS model using the converter few seconds get added up in the inference time. So far, the inference time is not that much and is close if not less than 1 sec.
That's all for day!!
Hope you Had great Week