Model Training Accuracy Issue

We are trying to detect foreign objects by using custom model. The model has 5 classes and for each class we have taken 48 images. We have trained our ssd model by using jetson-train github repo for around 1200 epoch. However at 1200 epoch our loss was around 1.64. My questions are:

  1. Would it have decreased if we had trained the model more?
  2. How many images should be in the dataset for each class to get a well-trained model?
  3. How much epoch do you suggest us to train our model? I mean should we train our model up until the loss decreases around 0.1 or 0.01 assuming that it will reach that threshold at some point?
  4. We have tested our model by showing the objects that it is trained with to the camera and confidence was around %90-100. However, when we show another object that the model didn’t see before the confidence was decreased around %65. Is there a possibility of overfitting to be occured in our model? If so how can we detect it and how can we prevent our model to be overfitted?


Hi @CostGazelle ,
I would suggest you raise the concern on Jetson Platform to get better assistance