I want to continue training the model.zip file with more images without retraining from the baseline model from scratch, how do I do that?
This isn't possible at the moment. ML.NET's ImageClassificationTrainer already uses a pre-trained model, so you're using transfer learning to create your model. Any additions would have to be "from scratch" on the pre-trained model.
Also, looking at the existing trainers that can be re-trained, the ImageClassificationTrainer isn't listed among them.
Related
I want to build a text2text model. Specifically, I want to transfer some automatically generated scrabbling text pieces into a smooth paragraph within the same language. I've already prepared the text inputs and outputs. So corpus is not the primary problem now.
I want to use hugging face models like:
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese")
model = AutoModelForMaskedLM.from_pretrained("bert-base-chinese")
because it has already obtained the capacity to generate the language, the model is made for masked language, and there's no mature task like mine as it is really customized. So how could I use the hugging face masked language model as a base text2text model without jeopardizing its capacity? I want to fine-tune it to achieve that task/goal. I want to know how.
I'm using pre-trained model for feature extraction of CT image for COVID. Then using a classifier. I need know what are features that will be extracted when pre-trained model is used here.
i have created a model in rapid miner. it is a classification model and save the model in pmml. i want to use this model in H2O.ai to predict further. is there any way i can import this pmml model to H2O.ai an used this for further prediction.
I appreciate your suggestions.
Thanks
H2O offers no support for importing/exporting(*) pmml models.
It is hard to offer a good suggestion without knowing your motivation for wanting to use both RapidMiner and H2O. I've not used RapidMiner in about 6 or 7 years, and I know H2O well, so my first choice would just be to re-build the model in H2O.
If you are doing a lot of pre-processing steps in RapidMiner, and that is why you want to use it, you could still do all that data munging there, then export the prepared data to csv, import that into H2O, then build the model.
*: Though I did just find this tool for converting H2O models to PMML: https://github.com/jpmml/jpmml-h2o But that is the opposite direction for what you want.
I have to retrain the inceptionV3 model from scratch on skin lesion images. How many images per class should i use?
Well the inceptionV3 has a model based on imagenet, so about 1000 should do. Are you sure you need to train it from scratch though?
I made a sentiment analysis model using Standford CoreNLP's library. So I have a bunch of ser.gz files that look like the following:
I was wondering what model to use in my java code, but based on a previous question,
I just used the model with the highest F1 score, which in this case is model-0014-93.73.ser.gz. And in my java code, I pointed to the model I want to use by using the following line:
props.put("sentiment.model", "/path/to/model-0014-93.73.ser.gz.");
However, by referring to just that model, am I excluding the sentiment analysis from the other models that were made? Should I be referring to all the model files to make sure I "covered" all the bases or does the highest scoring model trump everything else?
You should point to only the single highest scoring model. The code has no way to make use of multiple models at the same time.