Ok, I didn't understand what you wanted to do precisely, now I think it's clear!
Honestly, I never tried what you're doing here, but I guess you could:
* create a new dataset with the new "difficult" images;
* use finetune_detection.py;
* load the same custom model you used during the first training, and load the
last .param file you saved during the training, something like:
`classes = ["pen", "pencil"]`
`net = gcv.model_zoo.get_model('ssd_512_mobilenet1.0_custom',
classes=classes,
pretrained_base=False, ctx=ctx)`
`net.load_parameters("path_to_param_file", ctx=ctx)`
At that point, you can run the training again on the new dataset, so that ti
doesn't start from scratch, but it goes on from where you left it.
Again, I never really tried, so you can either try or wait for someone else's
opinion who tried. :)
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