Dear Teklia team,
I successfully retrained a new model with Pylaia (in commande line) for our handwritten documents. I re-generate the training data with the atr-data-generator tool using, as you suggested, the option “polygon” for the parameter image.extraction_mode.
In the logs, I can see the CER goes from 0.22 (previous training) to 0.25. It is better but still too weak. Should I be attentive to any other relevant information in the training logs? There is a lot of information but not clear what should I look for to go forward.
As you suggested, I plan now to fine-tune a the Belfort project model with our data. I still using default parameter while training.
Do you have any specific recommendation on hyperparamer tuning at this stage ?
Thanks in advance,
Carmen (from EHESS)
Hi Carmen,
Thanks for getting in touch!
The CER measures the character error rate, so the lower the better. A 25% CER seems really high for handwriting recognition. You should aim for 5-10% on the validation set.
I wouldn’t necessarily recommend changing the hyperparameters for now. Our team generally uses PyLaia’s default parameters, and we manage to get satisfactory results on most datasets.
If PyLaia does not learn, you might want to check your dataset (number of lines, annotation quality, segmentation quality, etc). If your dataset is small (< 1000 training lines), fine-tuning the Belfort model is a good idea.
Finally, I recommend using Weights & Biases to log and display metrics during training. You won’t have to read the logs anymore, as everything will be summarized in the interface.
Best,
Solène
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