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Some remarks on your question

... with a total of 10,000 positives and 8188 negatives of a flower ...

• This will simply not work. You need more negatives than positives to be able to differentiate positives from different backgrounds. Also I get the impression you are mixing up the concept of negative images (which is gathered manually) and negative windows (which are indicated by -numNeg and which are automatically retrieved from the negative set you supplied at model size).

... to create 10,000 positives from 250 actual positives and 8188 negatives ...

• Again I fairly advice against this practice. In an actual application this will NOT work. Better use those 250 actual flower images to train a model, then to generate 10.000 artificial samples from that. You will notice if you add the -show parameter to the create samples tool, that many of the samples created are unnatural and will thus never occur in your application. This will just clutter your model

... continue the training further(given my current acceptance ratio)? ...

YES, like being said in other topic, I am convinced that you should train up till the first value that goes below 10e-5. After that you will overfit your data, before your model will be to generic and yield to much false positive detections.

... it takes a lot of time ...

That is due to the fact you have that many samples. And looking at how complex (many features per stage) your model is training it is also due to the fact that your data is fairly complex to seperate. Also alot of time is relative without a proper indication.