A Python 3 and Keras 2 implementation of MobileNet V2 and provide train method. For now, there is a caffe model zoo which has a collection of models with verified performance,. For more information, see the documentation for multi_gpu_model. Aliases: Module tf. This code allows to port pretrained imagenet weights from original MobileNet v2 models to a keras model. It was developed with a focus on enabling fast experimentation. What is an adversarial example. / is the directory where the inference graph file should be generated. In the first part of this tutorial, we'll discuss the concept of an input shape tensor and the role it plays with input image dimensions to a CNN. layers is a flattened list of the layers comprising the model. MobileNet model architecture. My work is based on wonderful project by penny4860, SVHN yolo-v2 digit detector. You can use this code to convert all the MobileNets from tensorflow to keras, with pretrained weights. 13, as well as Theano and CNTK. They are extracted from open source Python projects. macOS: Download the. applications. cpp があったので試してみた。 オリジナルでは、カメラからの画像入力にたいして、検出と分類を行っているが、SSDのサンプルと同じように指定した画像ファイルを対象にするように修正した。. MobileNet V2 for example is a very good convolutional architecture that stays reasonable in size. I had trouble using Keras's built-in MobileNet & code so I mimicked the structure with the appropriate layers. Mobilenet V2 does not apply the feature depth percentage to the bottleneck layer. For an example showing how to define a custom regression output layer and specify a loss function, see Define Custom Regression Output Layer (Deep Learning Toolbox). cromwellcv dusty_nv said: The 2nd link from my post above is in C++ (and Python) and can load SSD-Mobilenet-v2 in addition to SSD-Mobilenet-v1 and SSD-Inception-v1. 528Hz Tranquility Music For Self Healing & Mindfulness Love Yourself - Light Music For The Soul - Duration: 3:00:06. Keras Applications are deep learning models that are made available alongside pre-trained weights. 0, but I could not manage to make it work : from keras. File live ks mobile net yolo m3u8 2017 tax file live ks mobile net yolo m3u8 2017 tax. Requirement. models import Sequential from keras. I've also tested this script with the Theano backend and confirmed that the implementation will work with Theano as well. interpolation: Interpolation method used to resample the image if the target size is different from that of the loaded image. preprocess_input(x) Defined in tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2. save_format: Format to use for saving sample images (if `save_to_dir` is set). I will then retrain Mobilenet and employ transfer learning such that it can correctly classify the same input image. applications. Here, as our PyTorch model we will consider Light-Weight RefineNet with the MobileNet-v2 backbone pre-trained on PASCAL VOC for semantic image segmentation. Windows: Download the. I am using the following piece of code. 4 How did Keras implement Batch Normalization over time? Keras has changed the behavior of Batch Normalization several times but the most recent significant update happened in Keras 2. This article is an introductory tutorial to deploy TFLite models with Relay. Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. 0 which brings a number of key improvements to the package. In May 2017, Google announced a software stack specifically for mobile development, TensorFlow Lite. Keras-OpenFace is a project converting OpenFace from Torch implementation to a Keras version Keras Yolov3 Mobilenet ⭐ 401 I transfer the backend of yolov3 into Mobilenetv1,VGG16,ResNet101 and ResNeXt101. See example below. def VGG_16(weights_path=None):. preprocess_input(x) Defined in tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2. layers import Dense, GlobalAveragePooling2D from keras import backend as K # create the base pre-trained model base_model = InceptionV3(weights='imagenet', include_top=False) # add a global spatial average pooling layer x = base_model. concatenate(). TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. Added sample_weight parameter to flow_images_from_data() Use native Keras implementation (rather than SciPy) for image_array_save() Default layer_flatten() data_format argument to NULL (which defaults to global Keras config). Explore and download deep learning models that you can use directly with MATLAB. probabilities that a certain object is present in the image, then we can use ELI5 to check what is it in the image that made the model predict a certain class score. image import ImageDataGenerator: from keras. application_resnet50() ResNet50 model for Keras. See example below. MobileNetアーキテクチャをインスタンス化します。 load_modelを介してMobileNetモデルをロードするには、カスタムオブジェクトrelu6をインポートし、 custom_objectsパラメータにcustom_objectsます。 例:model = load_model( 'mobilenet. Applications. dmg file or run brew cask install netron. File live ks mobile net yolo m3u8 2017 tax file live ks mobile net yolo m3u8 2017 tax. It defaults to the `image_data_format` value found in your Keras config file at `~/. Since we are planning to use the converted model in the browser, it is better to provide smaller inputs. 0 which brings a number of key improvements to the package. applications. contrib import util , ndk , graph_runtime as runtime from tvm. mobilenet_v2_decode_predictions() returns a list of data frames with variables class_name, class_description, and score (one data frame per sample in. I had trouble using Keras's built-in MobileNet & code so I mimicked the structure with the appropriate layers. Being able to go from idea to result with the least possible delay is key to doing good research. MobileNet V2's block design gives us the best of both worlds. uff in C++ for our benchmarking, yes I could get that benchmark figures, but that is not a useful use case. Mobilenet V1 did, which made the job of the classification layer harder for small depths. output x = GlobalAveragePooling2D()(x) # let's add a fully-connected layer x = Dense(1024, activation='relu')(x) # and a. mobilenet_decode_predictions() returns a list of data frames with variables class_name , class_description , and score (one data frame per sample in batch input). Kerasテンソルが渡された場合: - self. optimizers import Adam: from keras. Input image shape: (300,300,3). Module for pre-defined neural network models. You can also save this page to your account. dilation_rate: an integer or tuple/list of 3 integers, specifying the dilation rate to use for dilated convolution. Example: tflite_convert --output_file= bazel : In order to run the latest version of the TensorFlow Lite Converter either install the nightly build using pip or clone the TensorFlow repository and use bazel. 0 is the first release of multi-backend Keras that supports TensorFlow 2. For example, to train the smallest version, you’d use --architecture mobilenet_0. To get started, Flatbuffers and TFLite package needs to be installed as prerequisites. I have successfully built several model based on mobileNet using keras. preprocess_input(x) Defined in tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2. The following are code examples for showing how to use keras. Download the pre-trained models $ mmdownload -f keras -n inception_v3 Convert the pre-trained model files into an intermediate representation $ mmtoir -f keras -w imagenet_inception_v3. layers is a flattened list of the layers comprising the model. The weights are large files and thus they are not bundled with Keras. Jetson is able to natively run the full versions of popular machine learning frameworks, including TensorFlow, PyTorch, Caffe2, Keras, and MXNet. applications. Being able to go from idea to result with the least possible delay is key to doing good research. This module contains definitions for the following model architectures: - AlexNet - DenseNet - Inception V3 - ResNet V1 - ResNet V2 - SqueezeNet - VGG - MobileNet - MobileNetV2. A Python 3 and Keras 2 implementation of MobileNet V2 and provide train method. It supports multiple back-. I tried to use that uff model in the jetson-interference Python sample, it said the model is not supported. Once you install the support package MATLAB Coder Interface for Deep Learning Libraries, you can use coder. mobilenet_preprocess_input() returns image input suitable for feeding into a mobilenet model. Clone my github repo for this project. Being able to go from idea to result with the least possible delay is key to doing good research. Download Models. This tutorial focuses on the task of image segmentation, using a modified U-Net. Here is a break down how to make it happen, slightly different from the previous image classification tutorial. applications. The solution to the problem is considered in the following blog. Now classification-models works with both frameworks: keras and tensorflow. After reading this post you will know: How the dropout regularization. Note: Several different licenses govern the use of the weights for these models as the models originate from diverse sources. dmg file or run brew cask install netron. Implementations of the Inception-v4, Inception - Resnet-v1 and v2 Architectures in Keras using the Functional API. They are extracted from open source Python projects. summary() Print a summary of a Keras model. Here is a quick example: from keras. A testing script has been provided and can be found in test_mobilenet. For an example showing how to define a custom regression output layer and specify a loss function, see Define Custom Regression Output Layer (Deep Learning Toolbox). Clone my github repo for this project. Compile TFLite Models¶. You should derive the names based on your own graph. optimizers import Adam: from keras. 528Hz Tranquility Music For Self Healing & Mindfulness Love Yourself - Light Music For The Soul - Duration: 3:00:06. Input image shape: (300,300,3). They are stored at ~/. Download Models. MobileNet-v2-caffe - MobileNet-v2 experimental network description for caffe #opensource. 25の計16パターンのImageNetでの学習済みモデルを用意 仕組み 従来の畳込みフィルターの代わりにDepthwise畳み込みフィルターと1x1の畳み込みフィルターを組み合わせることで計算量を削減.. application_mobilenet_v2() and mobilenet_v2_load_model_hdf5() return a Keras model instance. Example Android app. io Find an R package R language docs Run R in your browser R Notebooks. start('[FILE]'). For an example showing how to define a custom regression output layer and specify a loss function, see Define Custom Regression Output Layer (Deep Learning Toolbox). application_mobilenet: MobileNet model architecture. I will then show you an example when it subtly misclassifies an image of a blue tit. GitHub Gist: star and fork abhisheksoni27's gists by creating an account on GitHub. I am using the following piece of code. What I was trying to do was to edit some files, such that they would work for mobilenet_v2 (mobilenet_v2_1. What is image segmentation? So far you have seen image classification, where the task of the network is to assign a label or class to an input image. Is a flexible, high-performance serving system for machine learning models, designed for production. experimental_run_v2 はストラテジーで各ローカルレプリカからの結果を返し、そしてこの結果を消費する複数の方法があります。. I noticed that MobileNet_V2 as been added in Keras 2. BatchNormalization () Examples. applications. MobileNet v2. Sample model files to. There are two main types of models available in Keras: the Sequential model, and the Model class used with the functional API. Being able to go from idea to result with the least possible delay is key to doing good research. multi_gpu_model, which can produce a data-parallel version of any model, and achieves quasi-linear speedup on up to 8 GPUs. Extract the. From there we'll discuss the example dataset we'll be using in this blog post. The basic idea is to consider detection as a pure regression problem. relay as relay from tvm import rpc from tvm. path: if you do not have the index file locally (at '~/. According to the paper: Inverted Residuals and Linear Bottlenecks Mobile Networks for Classification, Detection and Segmentation. Keras モデルを得ることができない (あるいは望まない) 場合にはtf. Example Android app. Kerasで少し複雑なモデルを訓練させるときに、損失関数にy_true, y_pred以外の値を渡したいときがあります。 クラスのインスタンス変数などでキャッシュさせることなく、ダイレクトに損失関数に複数の値を渡す方法を紹介します。. applications. 0): 255M MobileNet V2 MACCs (multiplier = 1. To do this, we need the Images, matching TFRecords for the training and testing data, and then we need to setup the. A Python 3 and Keras 2 implementation of MobileNet V2 and provide train method. py See this notebook for an example of fine-tuning a keras. The issue is that the mobilenet_v2 is not part of the require models given in the project. Part I states the motivation and rationale behind fine-tuning and gives a brief introduction on the common practices and techniques. 最近のMacに搭載されているdGPUはAMD製なのでCUDAが使えず、マカーなディープラーニング勢はどうしてんの?と本気でわかっていないところです。. The benchmark setup, Inference 20 times and do the average. Python Server: Run pip install netron and netron [FILE] or import netron; netron. Being able to go from idea to result with the least possible delay is key to doing good research. Keras comes with built-in pre-trained image classifier models, including: Inception-ResNet-v2, Inception-v3, MobileNet, ResNet-50, VGG16, VGG19, Xception. There are many implementations of YOLO architecture with Keras, but I found this one to be working out of the box and easy to tweak to suit my particular use case. For example, MobileNet is a popular image classification/detection model architecture that's compatible with the Edge TPU. mobilenet_v2_preprocess_input() returns image input suitable for feeding into a mobilenet v2 model. Develop Your First Neural Network in Python With this step by step Keras Tutorial!. Keras-OpenFace is a project converting OpenFace from Torch implementation to a Keras version Jupyter Notebook - Last pushed Nov 2, 2017 - 133 stars - 72 forks ildoonet/tf-mobilenet-v2. Besides that, we will also need this repository for converting the PyTorch model into Keras first. getDeepLearningLayers to see a list of the layers supported for a specific deep learning library. What is image segmentation? So far you have seen image classification, where the task of the network is to assign a label or class to an input image. For an example showing how to define a custom classification output layer and specify a loss function, see Define Custom Classification Output Layer (Deep Learning Toolbox). mobilenet_v2_preprocess_input() returns image input suitable for feeding into a mobilenet v2 model. In this notebook I shall show you an example of using Mobilenet to classify images of dogs. Jetson is able to natively run the full versions of popular machine learning frameworks, including TensorFlow, PyTorch, Caffe2, Keras, and MXNet. I had trouble using Keras's built-in MobileNet & code so I mimicked the structure with the appropriate layers. The weights are large files and thus they are not bundled with Keras. The architecture flag is where we tell the retraining script which version of MobileNet we want to use. In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. macOS: Download the. In this post you will discover the dropout regularization technique and how to apply it to your models in Python with Keras. The solution to the problem is considered in the following blog. Change input shape dimensions for fine-tuning with Keras. MobileNet V2 Architecture: Each line describes a sequence of 1 or more identical (modulo stride) layers, repeated n times. The current release is Keras 2. 2 ): VGG16, InceptionV3, ResNet, MobileNet, Xception, InceptionResNetV2. Training and Deploying A Deep Learning Model in Keras MobileNet V2 and Heroku: A Step-by-Step Tutorial Part 1 In our example, I have chosen the MobileNet V2 model because it's faster to. Individual weights are extracted in the jupyter notebook called "mobilenet_example" and saved to a weights directory, and a layer_guide is provided inside layer_guide. mobilenet_decode_predictions() returns a list of data frames with variables class_name , class_description , and score (one data frame per sample in batch input). It defaults to the `image_data_format` value found in your Keras config file at `~/. dmg file or run brew cask install netron. By selecting include_top=False, you get the pre-trained model without its final softmax layer so that you can add your own:. In addition, the image has to be 3 channel (RGB) format. See example below. Updated to the Keras 2. I have followed the tutorial (and made the modification to use the published vgg16 image preprocessing means suggested by @yurkor ) but i seem to get significantly different results using the theano backend and. Note: Lower is better MACs are multiply-accumulate operations , which measure how many calculations are needed to perform inference on a single 224×224 RGB image. mobilenet import mbv2 net = mbv2 (21, pretrained = True). The architecture flag is where we tell the retraining script which version of MobileNet we want to use. The ImageNet model uses the default values of 1 for alpha and depth_multiplied and a default of 6 for expansion_factor. This tutorial creates an adversarial example using the Fast Gradient Signed Method (FGSM) attack as described in Explaining and Harnessing Adversarial Examples by Goodfellow et al. said: Dustin, how have you gotten SSD-Mobilenet-V2 to work in TensorRT? Do you have a sample somewhere? Hi elias_mir, it was converted from a TensorFlow model to UFF. In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. experimental_run_v2 はストラテジーで各ローカルレプリカからの結果を返し、そしてこの結果を消費する複数の方法があります。. TensorFlow* is a deep learning framework pioneered by Google. You should derive the names based on your own graph. 0 corresponds to the width multiplier, and can be 1. 5; Tensorflow-gpu 1. MobileNet V2 for example is a very good convolutional architecture that stays reasonable in size. MobileNet v2. 0 to train a model and save trained word-embeddings for visualization in tensorboard. keras/datasets/' + path), it will be downloaded to this location. mobilenet_preprocess_input() returns image input suitable for feeding into a mobilenet model. Sample model files to. applications. For example, MobileNet is a popular image classification/detection model architecture that's compatible with the Edge TPU. Download the pre-trained models $ mmdownload -f keras -n inception_v3 Convert the pre-trained model files into an intermediate representation $ mmtoir -f keras -w imagenet_inception_v3. Yes,tensorRT examples in Python really important. Deep Learning Models. Keras has a built-in utility, keras. Preprocesses a. Jetson Nano can run a wide variety of advanced networks, including the full native versions of popular ML frameworks like TensorFlow, PyTorch, Caffe/Caffe2, Keras, MXNet, and others. tfcoreml needs to use a frozen graph but the downloaded one gives errors — it contains "cycles" or loops, which are a no-go for tfcoreml. application_vgg16() application_vgg19() VGG16 and VGG19 models for Keras. I will then retrain Mobilenet and employ transfer learning such that it can correctly classify the same input image. Contributors of Keras-MXNet are pleased to announce the release of v2. application_mobilenet_v2() and mobilenet_v2_load_model_hdf5() return a Keras model instance. My work is based on wonderful project by penny4860, SVHN yolo-v2 digit detector. 0 is the first release of multi-backend Keras that supports TensorFlow 2. mobilenet_decode_predictions() returns a list of data frames with variables class_name , class_description , and score (one data frame per sample in batch input). Download the pre-trained models $ mmdownload -f keras -n inception_v3 Convert the pre-trained model files into an intermediate representation $ mmtoir -f keras -w imagenet_inception_v3. start('[FILE]'). 1 with TensorFlow 2. In last week’s blog post we learned how we can quickly build a deep learning image dataset — we used the procedure and code covered in the post to gather, download, and organize our images on disk. My input shape is (64, 64, 3) and there are two classes in my dataset. If you are using TensorFlow, make sure you are using version >= 1. MobileNet_v2 model, taken from TensorFlow hosted models website. 0 and a TensorFlow backend. Kerasで少し複雑なモデルを訓練させるときに、損失関数にy_true, y_pred以外の値を渡したいときがあります。 クラスのインスタンス変数などでキャッシュさせることなく、ダイレクトに損失関数に複数の値を渡す方法を紹介します。. Besides that, we will also need this repository for converting the PyTorch model into Keras first. Fine-tuning a Keras model. compile() Configure a Keras model for training. It supports multiple back-. MobileNetV2 is a general architecture and can be used for multiple use cases. 4 How did Keras implement Batch Normalization over time? Keras has changed the behavior of Batch Normalization several times but the most recent significant update happened in Keras 2. Keras Applications are deep learning models that are made available alongside pre-trained weights. Now that we’ve seen what MobileNet is all about in our last video, let’s talk about how we can fine-tune the model via transfer learning and and use it on another dataset. Individual weights are extracted in the jupyter notebook called "mobilenet_example" and saved to a weights directory, and a layer_guide is provided inside layer_guide. path: if you do not have the index file locally (at '~/. See example below. applications. The commands worked perfectly for all the models that they listed though. define a VGG16 network. In this post, it is demonstrated how to use OpenCV 3. For a simplified camera preview setup we will use CameraView — an open source library that is up to 10 lines of code will enable us a possibility to process camera output. Preprocesses a. GitHub Gist: star and fork abhisheksoni27's gists by creating an account on GitHub. In examples above n = 2,3result in information loss where. 1の dnnのサンプルに ssd_mobilenet_object_detection. The library is designed to work both with Keras and TensorFlow Keras. 1 with TensorFlow 2. After working with PyTorch in my daily work for some time, recently I got a chance to work on something completely new - Core ML. Conclusion and Further reading. File live ks mobile net yolo m3u8 2017 tax file live ks mobile net yolo m3u8 2017 tax. Preparing the dataset Training the model using the transfer learning technique. optimizers import SGD import cv2, numpy as np. mobilenet_v2_preprocess_input() returns image input suitable for feeding into a mobilenet v2 model. Updated to the Keras 2. In this post, it is demonstrated how to use OpenCV 3. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices. The architecture flag is where we tell the retraining script which version of MobileNet we want to use. application_mobilenet_v2() and mobilenet_v2_load_model_hdf5() return a Keras model instance. relay as relay from tvm import rpc from tvm. For example, here are some results for MobileNet V1 and V2 models and a MobileNet SSD model. normalization. MobileNet_v2 model, taken from TensorFlow hosted models website. application_resnet50() ResNet50 model for Keras. Keras 実装の MobileNet も Keras 2. deb file or run snap install netron. It supports multiple back-. The efficiency of. However, the weights file is automatically downloaded ( one-time ) if you specify that you want to load the weights trained on ImageNet data. in keras: R Interface to 'Keras' rdrr. I will then show you an example when it subtly misclassifies an image of a blue tit. A simple and powerful regularization technique for neural networks and deep learning models is dropout. There are also helpful deep learning examples and tutorials available, created specifically for Jetson - like Hello AI World and JetBot. The network structure is another factor to boost the performance. from (28 X 28) to (96 X 96 X 3). Pre-trained models and datasets built by Google and the community. multi_gpu_model, which can produce a data-parallel version of any model, and achieves quasi-linear speedup on up to 8 GPUs. The MobileNet V1 blogpost and MobileNet V2 page on GitHub report on the respective tradeoffs for Imagenet classification. Still, V2 does less work than V1, even with a large depth multiplier. inputs is the list of input tensors of the model. To get started, Flatbuffers and TFLite package needs to be installed as prerequisites. Let's introduce MobileNets, a class of light weight deep convolutional neural networks (CNN) that are vastly smaller in size and faster in performance than many other popular models. applications. application_mobilenet_v2() and mobilenet_v2_load_model_hdf5() return a Keras model instance. Keras Applications are deep learning models that are made available alongside pre-trained weights. 本文通过讲述一个经典的问题, 手写数字识别 (MNIST), 让你对多类分类 (multiclass classification) 问题有直观的了解. The core layer of MobileNet is depthwise separable filters, named as Depthwise Separable Convolution. 而在V2中,MobileNet应用了新的单元:Inverted residual with linear bottleneck,主要的改动是为Bottleneck添加了linear激活输出以及将残差网络的skip-connection结构转移到低维Bottleneck层。 Paper:Inverted Residuals and Linear Bottlenecks Mobile Networks for Classification, Detection and Segmentation. If we have a model that takes in an image as its input, and outputs class scores, i. This tutorial focuses on the task of image segmentation, using a modified U-Net. layers is a flattened list of the layers comprising the model. mobilenet_v2_decode_predictions() returns a list of data frames with variables class_name, class_description, and score (one data frame per sample in. It was developed with a focus on enabling fast experimentation. You can vote up the examples you like or vote down the ones you don't like. 0 which brings a number of key improvements to the package. h5 -o keras_inception_v3 Open the MMdnn model visualizer and choose file keras_inception_v3. applications. keras/datasets/' + path), it will be downloaded to this location. Download the pre-trained models $ mmdownload -f keras -n inception_v3 Convert the pre-trained model files into an intermediate representation $ mmtoir -f keras -w imagenet_inception_v3. Image before preprocess:. In the first part of this tutorial, we'll discuss the concept of an input shape tensor and the role it plays with input image dimensions to a CNN. Additional information. Although based on Keras, the principles and concepts taught in this training course would be equally applicable in any deep learning library or framework. This tutorial focuses on the task of image segmentation, using a modified U-Net. Here are the directions to run the sample: Copy the ssd-mobilenet-v2 archive from here to the ~/Downloads folder on Nano. From there we'll discuss the example dataset we'll be using in this blog post. My input shape is (64, 64, 3) and there are two classes in my dataset. R interface to Keras. It is a fork of penny4860's detector with some minor changes. Keras has a built-in utility, keras. Contributors of Keras-MXNet are pleased to announce the release of v2. The paper on these architectures is available at "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning". In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. The ImageNet model uses the default values of 1 for alpha and depth_multiplied and a default of 6 for expansion_factor. uff in C++ for our benchmarking, yes I could get that benchmark figures, but that is not a useful use case. keras/keras. You should derive the names based on your own graph. Keras comes with built-in pre-trained image classifier models, including: Inception-ResNet-v2, Inception-v3, MobileNet, ResNet-50, VGG16, VGG19, Xception. I have successfully built several model based on mobileNet using keras. application_mobilenet: MobileNet model architecture. save_prefix: String prefix to use for saving sample images (if `save_to_dir` is set). Implementations of the Inception-v4, Inception - Resnet-v1 and v2 Architectures in Keras using the Functional API. Besides that, we will also need this repository for converting the PyTorch model into Keras first. def VGG_16(weights_path=None):. Mobilenet V2 does not apply the feature depth percentage to the bottleneck layer. macOS: Download the. For a simplified camera preview setup we will use CameraView — an open source library that is up to 10 lines of code will enable us a possibility to process camera output. About Keras models. I will then retrain Mobilenet and employ transfer learning such that it can correctly classify the same input image. application_mobilenet_v2() and mobilenet_v2_load_model_hdf5() return a Keras model instance. normalization. Additional information.