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I am working on a Vehicle detector using semi-supervised data. I created the data by applying another very performant Object Detector (which is slow and I can't use it for real-time detection).

I created data for multiple Videos of different streets. I have around 10 different videos and more than 35K frames. I created a training and validation datasets by splitting data into 80%-20%. I got around 90% for precision and recall on validation set.

But when I tested on two videos that were not used to create the train-validation dataset I get only 15%. So, my model kind of learning the sceneries that I used in training and that's why I get good results on validation with the same sceneries, but poor results on new sceneries.

I tried adding more videos, the model improved from 15% to 18% but it is still not enough.

I don't think adding more videos will really help. What can you propose me to do to make my model more generalizable ?

Wassim Bouatay
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  • You are likely just overfitting the data. Does the object detector have regularization parameters? You can potentially set those to something more strict. – neuroguy123 Jun 10 '21 at 15:14
  • take account of [this point](https://datascience.stackexchange.com/questions/96492/graph-neural-network-fails-at-generalizing-on-unseen-graph-topologies/96531#96531) concerning generalisation in machine learning – Nikos M. Jun 12 '21 at 09:37

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