NET you can load a frozen TensorFlow model. This is particularly useful while building JS UIs on top of TensorFlow (for example with Oracle JET). We have seen the Generative Adversarial Nets (GAN) model in the previous post. In a previous post, I built an image classification model for mushrooms using CustomVision. API example - get a working API for image recognition on top of our example. This repository contains machine learning models implemented inTensorFlow. After you have exported your TensorFlow model from the Custom Vision Service, this quickstart will show you how to use this model locally to classify images. NET image classification model. The models are hosted on NPM and unpkg so they can be used in any project out of the box. Welcome to Practical Machine Learning with TensorFlow 2. Learn how to transfer the knowledge from an existing TensorFlow model into a new ML. We aim to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. A written version of the tutorial is available at. Visit Quick, Draw! The Data for more information. Run your training job on a single worker instance in the cloud. TensorFlow provides the SavedModel format as a universal format for exporting models. TensorSpace is also compatible to mobile browsers. Real-time object detection is a challenging task, and most models are optimized to run fast on powerful GPU-powered computers with optimized code. ResNet50 achieves lowest training time when Tensorflow is used. Many guides are written as Jupyter notebooks and run directly in Google Colab—a hosted notebook environment that requires no setup. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python. Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices. Please see my new post on structuring models instead of this one. A model's state (topology, and optionally, trained weights) can be restored from various formats. js (deeplearn. Image Processing — OpenCV and Node. How to make a simple model in TensorFlow for recognizing handwritten digits from the MNIST data-set. layers, etc. 0 nature, we'll need to convert TensorFlow model into concrete function and then do a conversion to TensorFlow Lite (more about it here). Inside a Docker container from the image, the following steps need to be taken. TensorFlow Model Analysis (TFMA) is a library for evaluating TensorFlow models. At this time, we recommend that Keras users who use multi-backend Keras with the TensorFlow backend switch to tf. The API allows you to iterate quickly and adapt models to your own datasets without major code overhauls. x is a powerful framework that enables practitioners to build and run deep learning models at massive scale. For a simple example on MNIST, read the official tutorial, but keep in mind that some of the techniques are not recommended for big projects (they use placeholders instead of the new tf. js models that can be used in any project out of the box. GitHub Gist: instantly share code, notes, and snippets. If you are compiling a model from the TensorFlow Model Zoo for use with the NCSDK and Neural Compute API, follow the Guidance for Compiling TensorFlow™ Model Zoo Networks. We also provide tensorflow code for building the DELF model, which could then be used to train models for other types of objects. Models built with TensorFlow TensorFlow Models. Cell "Keras Model" All of our models will be straight sequences of layers so we can use the tf. I have also provided the pre-trained ckpt model and SavedModel in the GitHub repository. The TensorFlow team has been on a journey to make the training, deployment, managing, and scaling of machine learning Machine Learning models as easy as possible. Building effective machine learning models means asking a lot of questions. In the meantime, if you want to experiment this on a web browser, check out the TensorFlow. Intel® Optimization for TensorFlow* This open source, deep learning framework is optimized for Intel® Xeon® Scalable processors, and allows researchers and engineers to solve new business, engineering, and societal problems. How to make a simple model in TensorFlow for recognizing handwritten digits from the MNIST data-set. The command below provides a location to the models repository and to the last checkpoint. Please see my new post on structuring models instead of this one. As tensorflow is a low-level library when compared to Keras , many new functions can be implemented in a better way in tensorflow than in Keras for example , any activation fucntion etc… And also the fine-tuning and tweaking of the model is very flexible in tensorflow than in Keras due to much more parameters being available. Now with tensorflow installed, we now try to implement our first model on tensorflow. gz Objectives. To propose a model for inclusion, please submit a pullrequest. save to save the model. adversarial_crypto: protecting communications with adversarial neural cryptography. For questions / typos / bugs, use Piazza. Run your training job on a single worker instance in the cloud. Tutorial: Run TensorFlow model in Python. https://github. 54 FPS with the SSD MobileNet V1 model and 300 x 300 input image. Freeze the model and use Tranform graph tool provided by tensorflow (you'll have to build it from source with bazel) Compile the C++ API tensorflow library to use it in your project. It collected more than 1K Github stars in a month. And refer to the README. Magenta is distributed as an open source Python library, powered by TensorFlow. NET model makes use of part of the TensorFlow model in its pipeline to train a model to classify images into 3 categories. Download this GitHub repository containing samples for getting started with deep learning across TensorFlow, CNTK, Theano, and more. The demo app supports both the quantized model and the float model. Skip-Gram word vectors. Link to GitHub Repo: https://github. This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. How to make a simple model in TensorFlow for recognizing handwritten digits from the MNIST data-set. Defining your models in TensorFlow can easily result in one huge wall of code. This set of articles describes the use of the core low-level TensorFlow API. GitHub Gist: instantly share code, notes, and snippets. Graph 1 – TensorFlow Operations Supported by SYCL. This API can be used to detect, with bounding boxes, objects in images and/or video using either some of the pre-trained models made available or through models you can train on your own (which the API also makes easier). 07/03/2019; 3 minutes to read +5; In this article. The predict method needs to take input_fn which will return a input from a generator to the model upon being called. TensorFlow Object Detection Model Training. js (deeplearn. Unfortunately, although Tensorflow has been around for about two years, I still cannot find a bashing of Tensorflow that leaves me fully satisfied. /scripts/download_model_binary. NET model makes use of part of the TensorFlow model in its pipeline to train a model to classify images into 3 categories. This repository contains a number of different models implemented in TensorFlow: The official models are a collection of example models that use TensorFlow's high-level APIs. Running Distributed TensorFlow on Compute Engine This tutorial shows how to use a distributed configuration of TensorFlow on multiple Compute Engine instances to train a convolutional neural network model using the MNIST dataset. For a simple example on MNIST, read the official tutorial, but keep in mind that some of the techniques are not recommended for big projects (they use placeholders instead of the new tf. TensorFlow was originally developed by Google Brain Team and it is published on the public domain like GitHub. Because of TensorFlow 2. Oct 8, 2017. The TensorFlow model was trained to classify images into a thousand categories. TensorFlow provides a built-in API for these models so it doesn. I published my code on GitHub and as a PyPI package that lets you create your own language model in just a few lines of code: import tensorflow as tf from tensorlm import CharLM with tf. In the meantime, if you want to experiment this on a web browser, check out the TensorFlow. For mobile users on a cellular data connection: The size of this first demo is around 5 MB of data. DLTK comes with introduction tutorials and basic sample applications, including scripts to download data. This comment has been. Here is the list of other posts. We could just as easily have used Gated Recurrent Units (GRUs), Recurrent Highway Networks (RHNs), or some other seq2seq cell. Sep 23, 2017 · Is there a way to set the initial hiddden state and cell memory in Keras or Tensorflow? If so reference? suriyadeepan. Post-training tooling. Type string // Name by which the added operation will be referred to in the Graph. In our blog post we will use the pretrained model to classify, annotate and segment images into these 1000 classes. , you should definetely have a look at this article. TensorFlow supports multiple languages, but most common one is Python. Is capable of running on top of multiple back-ends including TensorFlow, CNTK, or Theano. The predict method needs to take input_fn which will return a input from a generator to the model upon being called. Currently supported visualizations include:. Real-time object detection is a challenging task, and most models are optimized to run fast on powerful GPU-powered computers with optimized code. TensorFlow Research Models. This Tensorflow Github project uses tensorflow to convert speech to text. Second part shows how to convert a dataset to tfrecord file without defining a computational graph and only by employing some built-in tensorflow functions. The set of classes is very diverse. Please see my new post on structuring models instead of this one. This set of articles describes the use of the core low-level TensorFlow API. The model will consist of one convolution layer followed by max pooling and another convolution layer. TFLearn: Deep learning library featuring a higher-level API for TensorFlow. Prerequisites. Compiling TensorFlow Networks. It demonstrates how to use mostly python code to optimize a caffe model and run inferencing with TensorRT. The object detection API makes it extremely easy to train your own object detection model for a large variety of different applications. The TensorFlow model was trained to classify images into a thousand categories. 14 training application and validate it locally. Matrix Factorization with Tensorflow Mar 11, 2016 · 9 minute read · Comments I've been working on building a content recommender in TensorFlow using matrix factorization, following the approach described in the article Matrix Factorization Techniques for Recommender Systems (MFTRS). Compiling TensorFlow Networks. Profiling, tuning, and compiling a DNN model on a development computer (host system) with the tools provided in the NCSDK. In terms of output performance, there is a significant amount of lag with a. x is a powerful framework that enables practitioners to build and run deep learning models at massive scale. Tutorial: Apply machine learning models in Azure Functions with Python and TensorFlow. Generative Adversarial Nets in TensorFlow. For mobile users on a cellular data connection: The size of this first demo is around 5 MB of data. Deploy a model to support prediction. TensorFlow 模型优化工具包是一套能够优化机器学习模型以便于部署和执行的工具。该工具包用途很多,其中包括支持用于以下方面的技术: 降低云和边缘设备(例如移动设备和 IoT 设备)的延迟时间和推断成本。. You can use it to train your own sequence tagging model. As an example, we show how we can label a set of images from a stock neural network model that was already trained. Sep 14, 2018. Compatible with TensorBoard, Jupyter and Colaboratory notebooks. Oct 8, 2017. Session() as session: # Create a new model. Click the Run in Google Colab button. Please report bugs (actually broken code, not usage questions) to the tensorflow/models GitHub issue tracker, prefixing the issue name with "object_detection". Retrieval-Based bots. My hardware is jetson tx2 and i installed Jetpack 3. TensorFlow 1. The TensorSpace. js (Part 3). It lets you run machine-learned models on mobile devices with low latency, so you can take advantage of them. Pre-trained TensorFlow. Fortunately, TensorFlow was developed for production and it provides a solution for model deployment — TensorFlow Serving. Read on if you want to learn about additional installation options, including installing a version of TensorFlow that takes advantage of Nvidia GPUs if you have the correct CUDA libraries installed. The R interface to TensorFlow lets you work productively using the high-level Keras and Estimator APIs, and when you need more control provides full access to the core TensorFlow API:. TensorFlow RNN Tutorial Building, Training, and Improving on Existing Recurrent Neural Networks | March 23rd, 2017. In order to train the TensorFlow model, we will need to two files — custom object Model files (. A web-based tool for visualizing neural network architectures (or technically, any directed acyclic graph). To try out Sketch-RNN, visit the Magenta GitHub for instructions. For a simple example on MNIST, read the official tutorial, but keep in mind that some of the techniques are not recommended for big projects (they use placeholders instead of the new tf. In TensorFlow's GitHub repository you can find a large variety of pre-trained models for various machine learning tasks, and one excellent resource is their object detection API. View this gist on GitHub Because of TensorFlow 2. The latest Tweets from TensorFlow (@TensorFlow). It currently supports Caffe's prototxt format. DSSM is a Deep Neural Network (DNN) used to model semantic similarity between a pair of strings. I published my code on GitHub and as a PyPI package that lets you create your own language model in just a few lines of code: import tensorflow as tf from tensorlm import CharLM with tf. Everytime you change the model in the demo, you will use another 5 MB of data. API example - get a working API for image recognition on top of our example. These notes and tutorials are meant to complement the material of Stanford’s class CS230 (Deep Learning) taught by Prof. The single-file implementation is available as pix2pix-tensorflow on github. Built on TensorFlow, it enables fast prototyping and is simply installed via pypi: pip install dltk. An easy, fast, and fun way to get started with TensorFlow is to build an image classifier: an offline and simplified alternative to Google's Cloud Vision API where our Android device can detect and recognize objects from an image (or directly from the camera. Fast Inference Protocols. This article demonstrates how Azure Functions allows you to use Python and TensorFlow with a machine learning model to classify an image based on its contents. If you cannot use a pre-trained model for your application, try using TensorFlow Lite post-training quantization tools during TensorFlow Lite conversion, which can optimize your already-trained TensorFlow model. Prerequisites. tf-seq2seq is a general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more. The folder folder will contain frozen_inference_graph. TensorFlow 2. Link to GitHub Repo: https://github. We could just as easily have used Gated Recurrent Units (GRUs), Recurrent Highway Networks (RHNs), or some other seq2seq cell. I have also provided the pre-trained ckpt model and SavedModel in the GitHub repository. TFlearn is a modular and transparent deep learning library built on top of Tensorflow. This is a summary of this nice tutorial. How to make a simple model in TensorFlow for recognizing handwritten digits from the MNIST data-set. The implementation is gonna be built in Tensorflow and OpenAI gym environment. js can't take full advantage of our computer's GPUs. Please cite our technical report if you use the models. GitHub Gist: instantly share code, notes, and snippets. We have also seen the arch nemesis of GAN, the VAE and its conditional variation: Conditional VAE (CVAE). or the converted TensorFlow Lite model, we can reduce the. How to use Python and TensorFlow to train an image classifier. Among many uses, the toolkit supports techniques used to: Reduce latency and inference cost for cloud and edge devices (e. tf-seq2seq is a general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more. In this article, I. zip Download. How does this work?. 0 builds on the capabilities of TensorFlow 1. 0’s eager execution, model needs to be converted to Concrete Function before the final conversion to TensorFlow Lite. Models can be trained, evaluated, and used for prediction. Running Google Machine Learning Library Tensorflow On ARM 64-bit Platform Feb 15, 2017 TensorFlow is an open source software library for machine learning which was developed by Google and open source to community. We have also seen the arch nemesis of GAN, the VAE and its conditional variation: Conditional VAE (CVAE). Benchmark tensorflow model in Android. Install TensorFlow. 54 FPS with the SSD MobileNet V1 model and 300 x 300 input image. 7% top-5 test accuracy in ImageNet , which is a dataset of over 14 million images belonging to 1000 classes. I created the GitHub Java project "TensorFlow Serving + gRPC + Java + Kafka Streams" to. I have released all of the TensorFlow source code behind this post on GitHub at bamos/dcgan-completion. Practical Machine Learning with TensorFlow 2. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. We'll go through an example of how to adapt a simple graph to do Multi-Task Learning. Learn more about Teams. This default service account is sufficient for most use cases. These posts and this github repository give an optional structure for your final projects. Running Google Machine Learning Library Tensorflow On ARM 64-bit Platform Feb 15, 2017 TensorFlow is an open source software library for machine learning which was developed by Google and open source to community. We are creating a model that can identify hardware tools using by TensorFlow. We aim to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. pb file (also called "frozen graph def" which is essentially a serialized graph_def protocol buffer written to disk) and make predictions with it from C# for scenarios like image classification,. See if any existing TensorFlow Lite pre-optimized models provide the efficiency required by your application. https://github. TensorFlow was originally developed by Google Brain Team and it is published on the public domain like GitHub. Some ImageJ plugins currently use TensorFlow to classify images according to pre-trained models. Problem Description. Sequential style to create them. Because of TensorFlow 2. A model's state (topology, and optionally, trained weights) can be restored from various formats. Tensorflow detection model zoo. Contribute to tensorflow/models development by creating an account on GitHub. The original TensorFlow code that generated this network is available on Magenta's GitHub page, specifically the stylized image transformation model. In TensorFlow's GitHub repository you can find a large variety of pre-trained models for various machine learning tasks, and one excellent resource is their object detection API. 0's eager execution, model needs to be converted to Concrete Function before the final conversion to TensorFlow Lite. At the core of the Graves handwriting model are three Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs). I am new in Tensorflow and I am trying to build model which will be able to perform OCR on my images. TensorSpace. This blogpost was aimed at making the reader comfortable with the implementational details of RNNs in tensorflow. We are creating a model that can identify hardware tools using by TensorFlow. MobileNetV2 is the second iteration of MobileNet released by Google with the goal of being smaller and more lightweight than models like ResNet and. Object detection API. Matrix Factorization with Tensorflow Mar 11, 2016 · 9 minute read · Comments I've been working on building a content recommender in TensorFlow using matrix factorization, following the approach described in the article Matrix Factorization Techniques for Recommender Systems (MFTRS). We’ve provided trained models, code for you to train your own models in TensorFlow and a Jupyter notebook tutorial (check it out!) The code release is timed to coincide with a Google Creative Lab data release. save to save the model. Q&A for Work. # Yay us, anyways once these are better supported I will update this gist appropriately. In the previous two posts, I have introduced the algorithms of many deep reinforcement learning models. Graph 1 – TensorFlow Operations Supported by SYCL. prototxt, the model structure with blobs…) to work with Caffe. Install TensorFlow pip package. TensorFlow 2. https://github. This is the fourth post of the image processing series from zero to one. /scripts/download_model_binary. js works well on Chrome, Safari, Firefox. This post will serve as a simple end-to-end example of how to use your own tensorflow-model to do inference in your go-application. I have released all of the TensorFlow source code behind this post on GitHub at bamos/dcgan-completion. Install TensorFlow. To do this (as we saw in Using a pre-trained TensorFlow model on Android), we need to…. Graph 1 – TensorFlow Operations Supported by SYCL. All steps required to train the hand detector are listed there already. What you'll Learn. Doing multi-task learning with Tensorflow requires understanding how computation graphs work - skip if you already know. Tensorflow sucks. # Yay us, anyways once these are better supported I will update this gist appropriately. Compatible with TensorBoard, Jupyter and Colaboratory notebooks. TensorFlow Custom Estimator API Census Sample. The first step to serve an ML model built in TensorFlow is to make sure it is in the right format. 07/03/2019; 3 minutes to read +5; In this article. If you cannot use a pre-trained model for your application, try using TensorFlow Lite post-training quantization tools during TensorFlow Lite conversion, which can optimize your already-trained TensorFlow model. How does this work?. We’ll first interpret images as being samples from a probability distribution. Inside a Docker container from the image, the following steps need to be taken. You can use it to train your own sequence tagging model. Even with the large number of tutorials about deploying Keras models on Android, I had to spend quite some time to sort things out. Image Processing — OpenCV and Node. TensorSpace is also compatible to mobile browsers. The results are improvements in speed, memory usage, and portability on server and mobile platforms. Now we will add the Android project that implements it, so we can do classification with the device's camera. tf-seq2seq is a general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more. Fortunately, TensorFlow was developed for production and it provides a solution for model deployment — TensorFlow Serving. # Yay us, anyways once these are better supported I will update this gist appropriately. This repository contains a number of different models implemented in TensorFlow: The official models are a collection of example models that use TensorFlow's high-level APIs. co/brain presenting work done by the XLA team and Google Brain team. I published my code on GitHub and as a PyPI package that lets you create your own language model in just a few lines of code: import tensorflow as tf from tensorlm import CharLM with tf. But to be honest, I found it quite cumbersome (e. Text Summarization using Sequence-to-Sequence model in Tensorflow and GPU computing: Part I - How to get things running October 17, 2016 December 9, 2016 cyberyu Uncategorized It took me quite an effort to make Tensorflow bidirectional Recurrent Neural Network Text summarization model running on my own NVIDIA graphic card. The TensorFlow team has been on a journey to make the training, deployment, managing, and scaling of machine learning Machine Learning models as easy as possible. Model Optimizer is a cross-platform command-line tool that facilitates the transition between the training and deployment environment, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices. It demonstrates how to use mostly python code to optimize a caffe model and run inferencing with TensorRT. View this gist on GitHub Because of TensorFlow 2. XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that optimizes TensorFlow computations. Design and train a RNN language model; Compare quality of RNN vs. Particularly on edge devices. I have also provided the pre-trained ckpt model and SavedModel in the GitHub repository. If you are comfortable with Keras or any other deep learning framework, feel free to use that. Session() as session: # Create a new model. We will only look at the constrained case of completing missing pixels from images of faces. Most codelabs will step you through the process of building a small application, or adding a new feature to an existing application. The Amazon SageMaker Python SDK TensorFlow estimators and models and the Amazon SageMaker open-source TensorFlow containers make writing a TensorFlow script and running it in Amazon SageMaker easier. You will have a choice of model architectures, so you can determine the right tradeoff between speed, size and accuracy for your problem. Andrew Ng and Prof. TensorFlow™ enables developers to quickly and easily get started with deep learning in the cloud. download the model weights by. Retrieval-based models have a repository of pre-defined responses they can use, which is unlike generative models that can generate responses they've never seen before. This article shows you how to run your TensorFlow training scripts at scale using Azure Machine Learning's TensorFlow estimator class. TensorFlow World is the first event of its kind - gathering the TensorFlow ecosystem and machine learning developers to share best practices, use cases, and a firsthand look at the latest TensorFlow product developments. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. The results are improvements in speed, memory usage, and portability on server and mobile platforms. Fortunately, TensorFlow was developed for production and it provides a solution for model deployment — TensorFlow Serving. We made an interactive web experiment that lets you draw together with a recurrent neural network model called sketch-rnn. Object detection API. The ImageJ-TensorFlow project enables TensorFlow to be used from ImageJ commands and scripts. How to optimize your model using the TFLite converter. chiphuyen/stanford-tensorflow-tutorials. To propose a model for inclusion, please submit a pull request. Training TensorFlow models in C. They are intended to be well-maintained, tested, and kept up to date with the latest stable TensorFlow API. Everytime you change the model in the demo, you will use another 5 MB of data. Q&A for Work. Particularly on edge devices. Now it is the time to get our hands dirty and practice how to implement the models in the wild. May 22, 2016 · Converting the Keras model to a tensorflow model. It uses "softmax" activation because it is the last layer in a classifier. data pipeline, they don't use tf. mobile, IoT). Optimize your hyperparameters by using hyperparameter tuning. chiphuyen/stanford-tensorflow-tutorials. Guidance for Compiling TensorFlow™ Model Zoo Networks. Note: We've just released Version 2. Sequential style to create them. 0's high-level Keras API to quickly build our image classification model. We’ve provided trained models, code for you to train your own models in TensorFlow and a Jupyter notebook tutorial (check it out!) The code release is timed to coincide with a Google Creative Lab data release. Unfortunately, although Tensorflow has been around for about two years, I still cannot find a bashing of Tensorflow that leaves me fully satisfied. I thought that the results from pix2pix by Isola et al. Within TensorFlow, model is an overloaded term, which can have either of the following two related meanings: The TensorFlow graph that expresses the structure of how a prediction will be computed. NET model makes use of part of the TensorFlow model in its pipeline to train a model to classify images into 3 categories. Particularly on edge devices. It collected more than 1K Github stars in a month. Oct 8, 2017. Every few months I enter the following query into Google: “Tensorflow sucks” or “f*** Tensorflow”, hoping to find like-minded folk on the internet. First part is where you can setup a tensorflow based classifier just to test it out. I published my code on GitHub and as a PyPI package that lets you create your own language model in just a few lines of code: import tensorflow as tf from tensorlm import CharLM with tf. 07/03/2019; 3 minutes to read +5; In this article. Pre-trained TensorFlow. Check out the updated GitHub repo for the source code. Guidance for Compiling TensorFlow™ Model Zoo Networks. The models are released under Creative Commons Attribution License. Hence, it is only proper for us to study conditional variation of GAN, called Conditional GAN or CGAN for. 1 dataset and the iNaturalist Species Detection Dataset. Sowndhariya has 3 jobs listed on their profile. Intel® Optimization for TensorFlow* This open source, deep learning framework is optimized for Intel® Xeon® Scalable processors, and allows researchers and engineers to solve new business, engineering, and societal problems. download the model weights by. The model that we have just downloaded was trained to be able to classify images into 1000 classes. It currently supports Caffe's prototxt format. Whether you’re publishing or browsing, this repository is where hundreds of machine learning models come together in one place. This repository contains a number of different models implemented in TensorFlow: The official models are a collection of example models that use TensorFlow's high-level APIs. MobileNetV2 is the second iteration of MobileNet released by Google with the goal of being smaller and more lightweight than models like ResNet and. Most of us don't have super fast GPUs (especially if you're browsing on mobile) and Tensorflow.