I want to use universal sentence encoder but the problem is that Google's pretrained versions doesn't support my language (not even multilingual version: https://tfhub.dev/google/universal-sentence-encoder-multilingual/3)
Is there any tutorial or way how to train my own universal sentence encoder from scratch with my own corpus?
According to the issue opened here(https://github.com/tensorflow/hub/issues/36) it seems that the model was not released to open source. You need to build it by yourself or fine-tuned to specific task.
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I'm looking into training an object detection network using Tensorflow, and I had a look at the TF2 Model Zoo. I noticed that there are noticeably less models there than in the directory /models/research/models/, including the MobileDet with SSDLite developed for the jetson xavier.
To clarify, the readme says that there is a MobileDet GPU with SSDLite, and that the model and checkpoints trained on COCO are provided, yet I couldn't find them anywhere in the repo.
How is one supposed to use those models?
I already have a custom-trained MobileDetv3 for image classification, and I was hoping to see a way to turn the network into an object detection network, in accordance with the MobileDetv3 paper. If this is not straightforward, training one network from scratch could be ok too, I just need to know where to even start from.
If you plan to use the object detection API, you can't use your existing model. You have to choose from a list of models here for v2 and here for v1
The documentation is very well maintained and the steps to train or validate or run inference (test) on custom data is very well explained here by the TensorFlow team. The link is meant for TensorFlow version v2. However, if you wish to use v1, the process is fairly similar and there are numerous blogs/videos explaining how to go about it
I used CreateML do train a new custom ObjectDector.
Everything worked well so far.
Now I am just wondering, what kind of Network is trained in the background?
Is it something like YOLO or Mobilenet?
I did not found anything on the official documentation:
https://developer.apple.com/documentation/createml#overview
There are two options:
TinyYOLOv2
Using transfer learning. This uses a built-in feature extractor model (VisionFeaturePrint.Objects). This is available with Create ML in Xcode 12.
Let's assume I made an app that has machine learning in it using a tflite file.
Is it possible that I could retrain this model right inside the app?
I have tried to use the Model Maker which is provided by TensorFlow, but, without this, i don't think there's any other way to retrain your model with just the app i made.
Do you mean training on the device when the app is deployed? If yes, TFLite currently doesn't support training in general. But there's some experimental work in this direction with limited support as shown by https://github.com/tensorflow/examples/blob/master/lite/examples/model_personalization.
Currently, the retraining of a TFLite model, as you found out w/ Model Maker, has to happen offline w/ TF before the app is deployed.
I would like to create a custom Tensorflow topology, specifically a custom Tensorflow topology out of a binary encoding of a neural network. I have attached a picture of what I mean by 'binary encoding of a neural network' below.
Binary Encoding of ANN (Source: Yao99)
Unfortunately am I only familiar with how to use Tensorflow using complete layers, mostly via the Keras API, though I don't know how to create the whole topology in a custom way from scratch.
I don't require a complete solution, but would already highly appreciate links to tutorials on how to create such custom topologies from scratch. The final translation on how to map the binary encoding to the custom graph creation I can do myself. Unfortunately am I unable to find resources for such custom topologies online. Thank you for your help!
I am starting to work with Intel movidius neural compute stick.
To start working, in my case, it is necessary to download pretrained models.
In the tutorials they refer to http://download.tensorflow.org/models/.
However, there is not a list that shows all the models available for download.
If the latest version of a net, lets say inception_v4, is not known, I cannot download the corresponding .tar.gz file.
Does anyone know a method to have an updated list of the .tar.gz files of the pretrained models available for download?
Thanks
The following two links may help
detection_model_zoo
TensorFlow-Slim image classification model library
keras.applications makes it easy to load models and their pretrained weights if you can use Keras
Considering that most of the posted linked outdated, I suggest looking into the TensorFlow hub (https://www.tensorflow.org/hub) for more recent pre-trained models.