I want to use tensorflow.js in my ReactNative app for machine learning.
The model creation api of tfjs seems quite intimidating, while on the other hand i found the AutoML and Model Builder of ML.NET quite interesting.
Is there a way to convert an ML.NET model to a tensorflow.js model and how?
Related
I have to create a workflow that uses pytorch models or torch script models in c++. However, I can't find the binary format. I tried looking through the serializer but to no avail. Does anybody have documentation or code that gives me a clue as to the model format?
PyTorch has a great tutorial of Tracing/Scripting models and Loading them via C++.
Guys I am trying to look for demo for object detection using YOLOv4 Tiny on every day objects. Therefore I am looking pre-trained model for YOLOv4 Tiny pre-trained on COCO dataset.After hours of searching I am only come across the similar tutorial on training on custom dataset from beginning using google Colab, I couldn't seem to find any pre-trained model for YOLOv4 Tiny model on COCO dataset.Can you give any reference where can I find a such a model?
Also I am trying to implement the model using OpenCV
I have used transfer learning of pytorch to trained a model. It has an extension of .pth. I want to use it to recognize object in video. I have always been using opencv in video processing so I want to use it this time too. However, the dnn module from opencv does not accept model with .pth extension. Can I generate model with pytorch that can be accepted by opencv? Or can I use model with .pth in opencv?
Here's how I saved the trained model
torch.save(the_model.state_dict(), PATH)
I have read that post How should I save the model of PyTorch if I want it loadable by OpenCV dnn module but it is not helpful
You could always try exporting using onnx, which looks like it could be read by OpenCV
The Machine Learning Model I download from Apple's website, can be used to recognise lots of different object through image.
I want to know, say I need to detect all different kinds of trees. Some can be recognised by the model whereas some cannot. Then how would I know how many trees are being trained in this model? in another word, How would I know all the possible tree recognition in this model? How would I find out? Can I decode the model? or parse it or something?
These models are trained on the ImageNet dataset. Here is a list of classes that these models can detect: https://github.com/HoldenCaulfieldRye/caffe/blob/master/data/ilsvrc12/synset_words.txt
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