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I am training a SSD model for detecting mobile cranes. The training dataset contains 1,000 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.

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  • $\begingroup$ 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. $\endgroup$ Oct 3 '18 at 12:47
  • $\begingroup$ Thank you, @FrancescoPegoraro. Do you mean ResNet-50? $\endgroup$
    – IPRNG
    Oct 3 '18 at 14:46
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In general, the best way to increase performance to increase training data and to train longer.

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