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.
Related
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
My goal is to test out Google's BERT algorithm in Google Colab.
I'd like to use a pre-trained custom model for Finnish (https://github.com/TurkuNLP/FinBERT). The model can not be found on TFHub library. I have not found a way to load model with Tensorflow Hub.
Is there a neat way to load and use a custom model with Tensorflow Hub?
Fundamentally: yes. Everyone can create the kind of models that TF Hub hosts, and I hope authors of interesting models do consider that.
For TF1 and the hub.Module format tailored to it, see
https://www.tensorflow.org/hub/tf1_hub_module#creating_a_new_module
For TF2 and its revised SavedModel format, see
https://www.tensorflow.org/hub/tf2_saved_model#creating_savedmodels_for_tf_hub
That said, a sophisticated model like BERT requires a bit of attention to export it with all bells and whistles, so it helps to have some tooling to build on. The BERT reference implementation for TF2 at https://github.com/tensorflow/models/tree/master/official/nlp/bert comes with an open-sourced export_tfhub.py script, and anyone can use that to export custom BERT instances created from that code base.
However, I understand from https://github.com/TurkuNLP/FinBERT/blob/master/nlpl_tutorial/training_bert.md#general-info that you are using Nvidia's fork of the original TF1 implementation of BERT. There are Hub modules created from the original research code, but the tooling to that end has not been open-sourced, and Nvidia doesn't seem to have added their own either.
If that's not changing, you'll probably have to resort to doing things the pedestrian way and get acquainted with their codebase and load their checkpoints into it.
I want to use pre trained models in my go application. Especially the Inception-ResNet-v2 model.
This model seems to be only available via tensorflow hub (https://www.tensorflow.org/hub/).
However I could not find any documentation how to use tensorflow hub with the go language bindings for tensorflow.
How can I download and use these models in go?
So after a lot of work in the past few days I finally found a way.
At first I wanted to just use Python to do all the Tensorflow stuff and then provide the results via a rest service. However it turned out that the number of models provided by Tensorflow Hub is very small. This was a problem for me because I had to try out different models and compare them.
Thus I switched to using models from https://github.com/tensorflow/models. There are several tutorials how to export the data to .pb files. Those files can then be loaded in Go using gocv.
It requires a lot of work to convert the files, but in the end I think this is the best way to use Tensorflow models in go.
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 trying to find a worked example of neural network pruning for the Faster-RCNN architecture.
My core stack is Tensorflow 1.12, its object_detection API (link) on Python3.5.2 in Ubuntu 16.04 LTS. I came across some Neural Network Pruning repos (e.g. link, implementing NVIDIA's pruning paper with Taylor expansion link - looking the most promising however (a) implemented in Pytorch and (b) on classification networks rather than detectors).
I am also aware of the existence of a pruning functionality within TensorFlow under this package (link), but could only run an example found in the comments of the following StackOverflow question (link) to train and prune (not thoroughly tested) a simple Neural Network for hand written digits classification using MNIST dataset.
I am looking for a worked example and not reporting any bugs or issues in code.
Can someone point to me a worked example of pruning Faster-RCNN -or other detectors- found on the TensorFlow's object detection API (link), preferably using TensorFlow's pruning package (link)?
Pruning is orthogonal to the meta-architecture used for object detection. When we talk about the TensorFlow Object Detection API, it heavily relies on builders that read the config and create corresponding nets, classes etc. I believe you want to prune the feature extractor as the most heavy part. If so, you need to first prune some feature extractor from slim (let's say, Inception-V2), give it a name, add its pruned version to models, adjust proto config and many more. Shortly speaking, you need to introduce a new type of feature extractor. But I am not aware of any existing examples on that.