Could anyone provide an example workflow to train a new segmentation model to run on the Coral? Google has provided pretrained models to run on the Coral, but has not released any documentation on how to train a new segmentation model. Does anyone have a workflow that has worked for segmentation training and inference?
Related
I am currently working on multi-label text classification with BERT and PyTorch Lightning. I am new to machine learning and am confused on how to train my model on AWS.
My questions include which Accelerated Computing instance (Amazon EC2) do I use considering I have a large database with 377 labels. Along with that, will I be able to run my code through Jupyter Notebooks with libraries like torch? Thank you for your time.
Is Ml.net image classification trainer support incremental learning or not and if yes can anybody show me example or topic to read it
If by incremental learning you're referring to taking the weights from a trained model and using that as the starting point to continue training, it's not supported at the moment. Technically though you're not starting from scratch with the image classification trainer since you're using transfer learning to train the last layer of the chosen pretrained image classification network, but incremental learning on your trained model is currently not supported. I would suggest posting an issue in the repo requesting this feature so others who may want this feature as well are able to upvote / comment on it.
https://github.com/dotnet/machinelearning/issues/new/choose
The documentation for porting an already trained TensorFlow Model to iOS is well defined:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/ios
However, nowhere is mentioned if the model:
can be further trained on the device, or
can be created from scratch and trained on the device
Is this possible with TensorFlow?
I am aware of other Swift/C++ libraries that offer on-device training, but I am more interested in this technology.
Starting with CoreML3 and the UpdatableTask, the training on device is now part of the API: https://developer.apple.com/documentation/coreml/mlupdatetask
I have just started exploring CoreML and was wondering if there is a way to train a Binary Classification Model using the same.
Please provide me any references or examples as I am a ML noob.
Core ML doesn't offer any APIs for building or training models. It works with models you've already trained elsewhere (Keras, Caffe, etc) to perform whatever prediction or classification task you built the model for. See Apple's Core ML docs for info on how to convert a model for use with Core ML.
Core ML offers building and training models as of macOS 10.14 (Mojave). In XCode you can train models in various ways.
I don't believe they currently support a binary classifier, but if you can build an image set of X and NOT X you could emulate such.
Apple's Docs: https://developer.apple.com/documentation/create_ml/creating_an_image_classifier_model
I'm using a bunch of images to train my Tensorflow Image recognition project using this tutorial https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.html#4
Actually I need a lot of Cpu to train my model and it takes a lot of time on my laptop.
I have registered a Google ML account and started this tutorial:
https://cloud.google.com/ml/docs/quickstarts/training
Everything is set up and running but this is for mnist sample code. There is no image_retraining sample code like the retrain.py from tensorflow.
Looking for some examples on how to to run the Tensorflow Image Recognition script retrain in Google ML.