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Considering that most of the posted linked outdated, I suggest looking into the TensorFlow hub (https://www.tensorflow.org/hub) for more recent pre-trained models.
Related
I'm kinda new to TensorFlow and Keras, so please excuse any accidental stupidity, but I have an issue. I've been trying to load in models from the TensorFlow Detection Zoo, but haven't had much success.
I can't figure out how to read these saved_model folders (they contain a saved_model.pb file, and an assets and variables folder), so that they're accepted by Keras. Nor can I figure out a way to convert these models so that they may be loaded in. I've tried converting the SavedModel to ONNX, and then convert the ONNX-model to Keras, but that didn't work. Trying to load the original model as a saved_model, and then trying to to save this loaded model in another format gave me no success either.
Since you are new to Tensorflow (and I guess deep learning) I would suggest you stick with the API because the detection zoo models best interface with the object detection API. If you have already downloaded the model, you just need to export it using the exporter_main_v2.py script. This article explains it very well link.
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.
In Github repository, we have tensorflow/models having slim and we also have slim in tensorflow.contrib.slim
They both have similar names, functionality and structure. They provide similar nets. For example, inception_v1
any reference for this brain split?
why they did not just git sub
module? any discussion link?
which is the most usable/stable/maintained?
which is the real net used to train pre-trained data? this one or this one
which one of those two is the real slim shady?
https://github.com/tensorflow/models/blob/master/research/slim/slim_walkthrough.ipynb under the section titled Installation and setup :
Since the stable release of TF 1.0, the latest version of slim has been available as tf.contrib.slim, although, to use TF-Slim for image classification (as we do in this notebook), you also have to install the TF-Slim image models library from here.
Are there any resnet implementations in tensorflow? I came across a few (e.g. https://github.com/ry/tensorflow-resnet, https://github.com/xuyuwei/resnet-tf) but these implementations have some bugs (e.g. see the Issues section on the respective github page). I am looking to train imagenet using resnet and looking for tensorflow implementations.
There are some (50/101/152) in tensorflow:models/slim.
The example notebook shows how to get a pre-trained inception running, res-net is probably no different.
I implemented a cifar10 version of ResNet with tensorflow. The validation errors of ResNet-32, ResNet-56 and ResNet-110 are 6.7%, 6.5% and 6.2% respectively. (You can modify the number of layers easily as hyper-parameters.)
I tried to be friendly with new ResNet fan and wrote everything straightforward. You can run the cifar10_train.py file directly without any downloads.
https://github.com/wenxinxu/resnet_in_tensorflow
I implemented Resnet by use of ronnie.ai and keras. Both of tool are great.
While ronnie is more easy from scratch.