Yes but in some cases when the car should keep the lane(or is keeping the lane/its not making any lane change) I get a 76% that it should make a lane change. Thats why I am confused, and for more information:
Pred=model(data)
PredS = nn.Sigmoid(Pred)*100
Print(PredS) # and here when I give the data of a car that is not making a lane change i get 76% for all its data points/frames. But being honest I don't know if this 76% is for lane changing or lane keeping.
After training the model I saved the weights. Then I loaded the weight to the model and gave the model a sequence of the data to see/visualize the output of the model on images(basically if the ego vehicle should make a lane change or nor) and even when it should keep the lane change I get a 49% it should change the lane. I am trying to predict if the ego vehicle should make a lane change or not in some data(even if it hasn't been trained on all that data)
Yes the class accuracy is 93%. And yes i agree with you that it can be 0.49 to 0.51. But does this mean the model is not learning? Or that by luck it gets 93% accuracy because after the activation functions it rounds 0.51 to 1, and 0.49 to 0? Or what is the best way to evaluate/inference a model(my model in this case)?
Beneficial_Law_5613 OP t1_ixm3fpz wrote
Reply to comment by jellyfishwhisperer in [D] inference on GNN by Beneficial_Law_5613
Yes but in some cases when the car should keep the lane(or is keeping the lane/its not making any lane change) I get a 76% that it should make a lane change. Thats why I am confused, and for more information: Pred=model(data) PredS = nn.Sigmoid(Pred)*100 Print(PredS) # and here when I give the data of a car that is not making a lane change i get 76% for all its data points/frames. But being honest I don't know if this 76% is for lane changing or lane keeping.