1
$\begingroup$

I am training a SSD model for detecting mobile cranes. The training dataset contains 1000 images and test set over 400 images. About 200 epochs gave mAP 83%, but my target is 90%. So I trained SSD-ResNet-101 and it gave less accuracy.

I assume that it is because ResNet-101 is too deep for the size of my dataset. I consider using ResNet-50 and Inception. But I don't have time to experiment all the models with different parameter settings.

Is there anyone who has experience in this direction? Any advice is welcome.

Thanks in advance.

$\endgroup$
  • $\begingroup$ Yes as you said the dataset is likely to be too small for that amount of layers and the model is likely to overfit. I would try with shallower architectures. Inception and ResNet are better for fine tuning in your case. $\endgroup$ – Francesco Pegoraro Oct 3 '18 at 12:47
  • $\begingroup$ Thank you, @FrancescoPegoraro. Do you mean ResNet-50? $\endgroup$ – IPRNG Oct 3 '18 at 14:46

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.