Overfitting problem

[m5823779]

@samarth-robo
Hello~ I have run this code with my own dataset, I choose Mapnet to be my model, hole settings are the same(learning rate, step, skip, etc..), but the result always converge to the same point, is this overfitting.
I think the reason is

1. Learning rate still too big?
2. The parameter of skip is too small? (Because my robot move slow)
3. Data repeatability is too high (It means the image is too close but the poses are different because the path of my training data is close)

Following is the Ground Truth of my training data
which do you think is the problem
I really need your help
thanks

[samarth-robo]

Hi @m5823779 , have you tried training PoseNet? If that does not train, you might have some bug in how to represent the data or pose labels. If PoseNet trains fine but MapNet does not, we can think about tuning hyperparameters.

[m5823779]

HI~ @samarth-robo yesterday I have tried Posenet and it is fine, so you mean I can tune beta and gamma?

[samarth-robo]

Can you describe the MapNet behavior in more detail? You said it predicts the same pose for all images? Are you able to overfit to your training dataset?

[m5823779]

@samarth-robo
Hi thanks for your reply
Last time I have a good result, like this

Because I have too many training data, so I cancel some seqence and train again, but it converges to the same position just like the following picture(validation result)

by the way, my learning rate is 0.0001 and step = 3, skips =15

[samarth-robo]

Hmm, it looks to me like you need to increase the importance of absolute pose in the loss function (sax and saq in train.py). One way to do that is reduce beta, but you could also play with the sax and saq values directly.
My guess is that increasing the importance of absolute pose loss will help spread out the predictions more.
On the other extreme, if you train a network with only relative pose loss, it can cheat by predicting small poses around identity. MapNet combines both absolute and relative pose losses.