Keras Mobilenet V2 Example

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. R interface to Keras. This is an example of using Relay to compile a keras model and deploy it on Android device. Intro Deep Learning with Keras : : CHEAT SHEET Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. applications. Applications. AI 技術を実ビジネスで活用するには? Vol. 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. from (28 X 28) to (96 X 96 X 3). 68 [東京] [詳細] 米国シアトルにおける人工知能最新動向 多くの企業が AI の研究・開発に乗り出し、AI 技術はあらゆる業種に適用されてきています。. Preprocesses a. You should derive the names based on your own graph. 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. The following are code examples for showing how to use keras. 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。. import os import numpy as np from PIL import Image import keras from keras. Massive backend design updates and a simplification of the API are the key highlights here. はじめに OpenCV 3. 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。. If you never set it, then it will be "channels_last". mobilenet_preprocess_input() returns image input suitable for feeding into a mobilenet model. inception_resnet_v2. In examples above n = 2,3result in information loss where. Additional information. Part I states the motivation and rationale behind fine-tuning and gives a brief introduction on the common practices and techniques. Wrappers for primitive Neural Net (NN) Operations. File live ks mobile net yolo m3u8 2017 tax file live ks mobile net yolo m3u8 2017 tax. Download pre-trained model checkpoint, build TensorFlow detection graph then creates inference graph with TensorRT. mobilenet_v2_preprocess_input() returns image input suitable for feeding into a mobilenet v2 model. We will specifically use FLOWERS17 dataset from the University of Oxford. Linux: Download the. application_mobilenet() and mobilenet_load_model_hdf5() return a Keras model instance. io Find an R package R language docs Run R in your browser R Notebooks. Module for pre-defined neural network models. First, let's create a simple Android app that can handle all of our models. For example, here are some results for MobileNet V1 and V2 models and a MobileNet SSD model. To get started, Flatbuffers and TFLite package needs to be installed as prerequisites. Part 2 will focus on preparing a trained model to be served by TensorFlow Serving and deploying the model to Heroku. Preparing the dataset Training the model using the transfer learning technique. TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. 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). Tensorflow MobilenetSSD model Caffe MobilenetSSD model. Keras and Convolutional Neural Networks. Preprocesses a. I will then show you an example when it subtly misclassifies an image of a blue tit. 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.. Fashion-MNIST database of fashion articles Dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. To get started, Flatbuffers and TFLite package needs to be installed as prerequisites. I noticed that MobileNet_V2 as been added in Keras 2. This is an example of using Relay to compile a keras model and deploy it on Android device. Here is a break down how to make it happen, slightly different from the previous image classification tutorial. The Intel® Movidius™ Neural Compute SDK (Intel® Movidius™ NCSDK) introduced TensorFlow support with the NCSDK v1. You can use this code to convert all the MobileNets from tensorflow to keras, with pretrained weights. fsandler, howarda, menglong, azhmogin, [email protected] MobileNet_v2 model, taken from TensorFlow hosted models website. Here is a quick example: from keras. Jetson is able to natively run the full versions of popular machine learning frameworks, including TensorFlow, PyTorch, Caffe2, Keras, and MXNet. Keras comes with built-in pre-trained image classifier models, including: Inception-ResNet-v2, Inception-v3, MobileNet, ResNet-50, VGG16, VGG19, Xception. Transfer learning in deep learning means to transfer knowledge from one domain to a similar one. 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". TensorFlow Support. Abstract: In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. from mobilenets import MobileNet model = MobileNet. 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. MobileNet v2. Only two classifiers are employed. The image is divided into a grid. load() を使用します。そうでないなら、tf. define a VGG16 network. Preparing the dataset Training the model using the transfer learning technique. 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. Keras has a built-in utility, keras. MobileNet V2 is mostly an updated version of V1 that makes it even more efficient and powerful in terms of performance. applications. I am trying to use Keras' MobileNet to do image classification. 3 when the BN layer was frozen (trainable = False) it kept updating its batch statistics, something that caused epic headaches to its users. 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). The following are code examples for showing how to use keras. mobilenetv2 import MobileNetV2 from keras. Windows: Download the. I converted the code to V2 as it follows. We will also create a dummy input, which we will feed into the pytorch_to_keras function in order to create an ONNX graph. I'll then show you how to:. application_mobilenet() mobilenet_preprocess_input() mobilenet_decode_predictions() mobilenet_load_model_hdf5() MobileNet model architecture. For more information, see the documentation for multi_gpu_model. They are extracted from open source Python projects. AI 技術を実ビジネスで活用するには? Vol. The Intel® Movidius™ Neural Compute SDK (Intel® Movidius™ NCSDK) introduced TensorFlow support with the NCSDK v1. 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. In this tutorial, we will keep things simple and use the MobileNet V2 transfer learning. 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. MobileNet V2 for example is a very good convolutional architecture that stays reasonable in size. TensorFlow* is a deep learning framework pioneered by Google. Preparing the dataset Training the model using the transfer learning technique. The following are code examples for showing how to use keras. After working with PyTorch in my daily work for some time, recently I got a chance to work on something completely new - Core ML. Module for pre-defined neural network models. preprocessing. inception_resnet_v2. There are two main types of models available in Keras: the Sequential model, and the Model class used with the functional API. mobilenet import mbv2 net = mbv2 (21, pretrained = True). See example below. * collection. 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。. 0 to train a model and save trained word-embeddings for visualization in tensorboard. Tensorflow MobilenetSSD model. We will also create a dummy input, which we will feed into the pytorch_to_keras function in order to create an ONNX graph. TensorFlow Support. MobileNetに関する情報が集まっています。現在25件の記事があります。また5人のユーザーがMobileNetタグをフォローしています。. Mobilenet v2 Keras port This code allows to port pretrained imagenet weights from original MobileNet v2 models to a keras model. macOS: Download the. Please check the examples: keras. application_vgg16() application_vgg19() VGG16 and VGG19 models for Keras. load_model() を使用します。Keras モデルでセーブした場合に限り Keras モデルを戻して得ることができることに注意してください。. class RNNCellDeviceWrapper: Operator that ensures an RNNCell runs on a particular device. I've also tested this script with the Theano backend and confirmed that the implementation will work with Theano as well. 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. It maintains compatibility with TensorFlow 1. mobilenet_v2_decode_predictions() returns a list of data frames with variables class_name, class_description, and score (one data frame per sample in. 1 with TensorFlow 2. In this case, the number of num_classes remains one because only faces will be recognized. mobilenet_v2_decode_predictions() returns a list of data frames with variables class_name, class_description, and score (one data frame per sample in. The architecture flag is where we tell the retraining script which version of MobileNet we want to use. 0 to train a model and save trained word-embeddings for visualization in tensorboard. config is a configuration file that is used to train an Artificial Neural Network. Although based on Keras, the principles and concepts taught in this training course would be equally applicable in any deep learning library or framework. Figure 1: Examples of ReLU transformations of low-dimensional manifolds embedded in higher-dimensional spaces. To construct a layer, # simply construct the object. preprocess_input(x) Defined in tensorflow/python/keras/_impl/keras/applications/inception_resnet_v2. Applications. MobileNetV2 is a general architecture and can be used for multiple use cases. MobileNet v2:. MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler Andrew Howard Menglong Zhu Andrey Zhmoginov Liang-Chieh Chen Google Inc. Inception-ResNet v2 model, with weights trained on ImageNet. In this post you will discover the dropout regularization technique and how to apply it to your models in Python with Keras. * collection. The library is designed to work both with Keras and TensorFlow Keras. Implementations of the Inception-v4, Inception - Resnet-v1 and v2 Architectures in Keras using the Functional API. applications. Conclusion and Further reading. This file is based on a pet detector. 2 ): VGG16, InceptionV3, ResNet, MobileNet, Xception, InceptionResNetV2. from models. My input shape is (64, 64, 3) and there are two classes in my dataset. In the repository, ssd_mobilenet_v1_face. File live ks mobile net yolo m3u8 2017 tax file live ks mobile net yolo m3u8 2017 tax. keras_model_sequential() Keras Model composed of a linear stack of layers. exe installer. models import Model from keras. applications. 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. GitHub Gist: star and fork abhisheksoni27's gists by creating an account on GitHub. Note: Several different licenses govern the use of the weights for these models as the models originate from diverse sources. * collection. For this tutorial, we will convert the SSD MobileNet V1 model trained on coco dataset for common object detection. This file is based on a pet detector. application_mobilenet_v2() and mobilenet_v2_load_model_hdf5() return a Keras model instance. Contributors of Keras-MXNet are pleased to announce the release of v2. I am using the following piece of code. keras/datasets/' + path), it will be downloaded to this location. 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。. uff in C++ for our benchmarking, yes I could get that benchmark figures, but that is not a useful use case. - classifier_from_little_data_script_3. Here are the directions to run the sample: Copy the ssd-mobilenet-v2 archive from here to the ~/Downloads folder on Nano. This was one of the first and most popular attacks to fool a neural network. It supports multiple back-. 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. Real-time object detection on the Raspberry Pi with the Movidius NCS (Part 1) - Duration: 0:34. inception_resnet_v2. Extract the. A Comprehensive guide to Fine-tuning Deep Learning Models in Keras (Part II) October 8, 2016 This is Part II of a 2 part series that cover fine-tuning deep learning models in Keras. Pre-trained models and datasets built by Google and the community. You can learn more about the technical details in our paper, "MobileNet V2: Inverted Residuals and Linear Bottlenecks". Keras Model. 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. Those values are x,y coordinates. 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). MobileNetV2 is a general architecture and can be used for multiple use cases. preprocessing import image from keras. For more information, see the documentation for multi_gpu_model. For example, to train the smallest version, you'd use --architecture mobilenet_0. Kerasテンソルが渡された場合: - self. 0, but I could not manage to make it work : from keras. mobilenet_v2_decode_predictions() returns a list of data frames with variables class_name, class_description, and score (one data frame per sample in. save_prefix: String prefix to use for saving sample images (if `save_to_dir` is set). applications. My work is based on wonderful project by penny4860, SVHN yolo-v2 digit detector. As part of Opencv 3. They are extracted from open source Python projects. The 224 corresponds to image resolution, and can be 224, 192, 160 or 128. 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. 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. A testing script has been provided and can be found in test_mobilenet. config is a configuration file that is used to train an Artificial Neural Network. This is an example of using Relay to compile a keras model and deploy it on Android device. preprocessing import image from keras. 3 when the BN layer was frozen (trainable = False) it kept updating its batch statistics, something that caused epic headaches to its users. $ cd ~/Downloads/. In May 2017, Google announced a software stack specifically for mobile development, TensorFlow Lite. For this tutorial, we will convert the SSD MobileNet V1 model trained on coco dataset for common object detection. I've also tested this script with the Theano backend and confirmed that the implementation will work with Theano as well. These networks can be used to build autonomous machines and complex AI systems by implementing robust capabilities such as image recognition, object detection and. Guild Of Light - Tranquility Music 1,664,823 views. Linux: Download the. Yes,tensorRT examples in Python really important. 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. Weights are downloaded automatically when instantiating a model. You can also save this page to your account. We’ll also. 0 to train a model and save trained word-embeddings for visualization in tensorboard. AI 技術を実ビジネスで活用するには? Vol. 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。. 如果你是机器学习领域的新手, 我们推荐你从本文开始阅读. Each grid cell is responsible for predicting 5 objects which have centers lying inside the cell. 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. multi_gpu_model() Replicates a model on different GPUs. Depending on the use case, it can use different input layer size and. They are extracted from open source Python projects. Keras is an extremely popular high-level API for building and training deep learning models. You will get different results here for 224 × 224 images. mobilenet_v2_decode_predictions() returns a list of data frames with variables class_name, class_description, and score (one data frame per sample in. You can learn more about the technical details in our paper, "MobileNet V2: Inverted Residuals and Linear Bottlenecks". The traditional Keras idea of using pretrained models typically involved either (1) applying a model like MobileNet as a whole, including its output layer, or (2) chaining a "custom head" to its penultimate layer 10. MobileNet V2 for example is a very good convolutional architecture that stays reasonable in size. GitHub Gist: star and fork abhisheksoni27's gists by creating an account on GitHub. MobileNetに関する情報が集まっています。現在25件の記事があります。また5人のユーザーがMobileNetタグをフォローしています。. Part I states the motivation and rationale behind fine-tuning and gives a brief introduction on the common practices and techniques. We will create the base model from the MobileNet model developed at Google, and pre-trained on the ImageNet dataset. Most notably, the package has been updated to include the changes brought in by Keras v2. mobilenet_v2_preprocess_input() returns image input suitable for feeding into a mobilenet v2 model. utils import multi_gpu_model # Replicates `model` on 8 GPUs. MobileNet_v2 model, taken from TensorFlow hosted models website. 0 and a TensorFlow backend. inputs is the list of input tensors of the model. 如果你是机器学习领域的新手, 我们推荐你从本文开始阅读. 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. These models can be used for prediction, feature extraction, and fine-tuning. MobileNet Architecture. start('[FILE]'). I've also tested this script with the Theano backend and confirmed that the implementation will work with Theano as well. mobilenet_v2; Functions. According to the paper: Inverted Residuals and Linear Bottlenecks Mobile Networks for Classification, Detection and Segmentation. Keras is a minimalist, highly modular neural networks library written in Python and capable on running on top of either TensorFlow or Theano. * collection. The 224 corresponds to image resolution, and can be 224, 192, 160 or 128. Yes,tensorRT examples in Python really important. This code allows to port pretrained imagenet weights from original MobileNet v2 models to a keras model. A testing script has been provided and can be found in test_mobilenet. 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. mobilenet_v2_preprocess_input() returns image input suitable for feeding into a mobilenet v2 model. 1; win-64 To install this package with conda run one of the following: conda install -c conda-forge keras conda install -c conda-forge. 68 [東京] [詳細] 米国シアトルにおける人工知能最新動向 多くの企業が AI の研究・開発に乗り出し、AI 技術はあらゆる業種に適用されてきています。. The ImageNet model uses the default values of 1 for both of the above. Inception-ResNet v2 model, with weights trained on ImageNet. saved_model. mobilenet_v2_preprocess_input() returns image input suitable for feeding into a mobilenet v2 model. See example below. application_resnet50() ResNet50 model for Keras. You can use this code to convert all the MobileNets from tensorflow to keras, with pretrained weights. convolutional import Conv2D, MaxPooling2D, ZeroPadding2D from keras. MobileNet V2's block design gives us the best of both worlds. We’ll also be. layers package, layers are objects. inputs is the list of input tensors of the model. Image before preprocess:. 2 ): VGG16, InceptionV3, ResNet, MobileNet, Xception, InceptionResNetV2. 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。. We’ll also. They are extracted from open source Python projects. '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. For example, to train the smallest version, you'd use --architecture mobilenet_0. Kerasでは画像サイズが224か192, 160, 128で$\alpha$が1. The commands worked perfectly for all the models that they listed though. machine-learning keras tensorflow. What is an adversarial example?. Example use cases include detection, fine-grain classification, attributes and geo-localization. I have successfully built several model based on mobileNet using keras. applications. Training and Deploying A Deep Learning Model in Keras MobileNet V2 and Heroku: A Step-by-Step Tutorial Part 1. 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. normalization. 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. 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). These models have a number of methods and attributes in common: model. In contrast, the TF Hub idea is to use a pretrained model as a module in a larger setting. After reading this post you will know: How the dropout regularization. I have only just discovered keras and this example has shown me how simple and powerful development using the keras framework can be. application_mobilenet_v2() and mobilenet_v2_load_model_hdf5() return a Keras model instance. io Find an R package R language docs Run R in your browser R Notebooks. Preprocesses a. Keras 実装の MobileNet も Keras 2. A Python 3 and Keras 2 implementation of MobileNet V2 and provide train method. You can vote up the examples you like or vote down the ones you don't like. _add_inbound_node()を呼び出します。 - 必要に応じて、入力の形状に合わせてレイヤーをbuildします。 - 出力テンソルの_keras_historyを現在のレイヤーで更新します。 これは_add_inbound_node()の一部として行われます。 引数:. Windows: Download the. It was the last release to only support TensorFlow 1 (as well as Theano and CNTK). from models. This code allows to port pretrained imagenet weights from original MobileNet v2 models to a keras model. About Keras models. normalization. BatchNormalization(). MobileNet v2 models for Keras. A simple and powerful regularization technique for neural networks and deep learning models is dropout. 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. A Comprehensive guide to Fine-tuning Deep Learning Models in Keras (Part I) October 3, 2016 In this post, I am going to give a comprehensive overview on the practice of fine-tuning, which is a common practice in Deep Learning. TensorFlow* is a deep learning framework pioneered by Google. 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. normalization. All I need is for the sample to work woth mobilenet_v2 like it does with inception. I have only just discovered keras and this example has shown me how simple and powerful development using the keras framework can be. The ImageNet model uses the default values of 1 for both of the above. ImageNet is an image dataset organized according to the WordNet hierarchy. mobilenet_v2 import MobileNetV2 import tvm import tvm. If you never set it, then it will be "channels_last". application_vgg16() application_vgg19() VGG16 and VGG19 models for Keras. The accuracy results for MobileNet v1 and v2 are based on the ImageNet image recognition task. deb file or run snap install netron. There are three hyperparameters that you can change - alpha (the widening factor), expansion_factor (multiplier by which the inverted residual block is multiplied) and depth_multiplier. Conclusion MobileNets are a family of mobile-first computer vision models for TensorFlow , designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application. Is a flexible, high-performance serving system for machine learning models, designed for production. 0, but I could not manage to make it work : from keras. 0 corresponds to the width multiplier, and can be 1. Instead, you can leverage existing TensorFlow models that are compatible with the Edge TPU by retraining them with your own dataset. 0 to train a model and save trained word-embeddings for visualization in tensorboard. Browser: Start the browser version. The paper on these architectures is available at "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning". To construct a layer, # simply construct the object. 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). The efficiency of. '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. 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_v2_decode_predictions() returns a list of data frames with variables class_name, class_description, and score (one data frame per sample in. You can vote up the examples you like or vote down the ones you don't like. The ImageNet model uses the default values of 1 for both of the above. save_prefix: String prefix to use for saving sample images (if `save_to_dir` is set). Being able to go from idea to result with the least possible delay is key to doing good research. Here MobileNet V2 is slightly, if not significantly, better than V1. Keras comes with built-in pre-trained image classifier models, including: Inception-ResNet-v2, Inception-v3, MobileNet, ResNet-50, VGG16, VGG19, Xception. They are extracted from open source Python projects. In addition, the image has to be 3 channel (RGB) format. TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. load() を使用します。そうでないなら、tf. I will show an example how to call our model from python. You can vote up the examples you like or vote down the ones you don't like. application_vgg16() application_vgg19() VGG16 and VGG19 models for Keras. 'name_of_the_output_node' here is an example of possible output node name. 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. Let's try the ssd_mobilenet_v2 object detection model on various hardware and configs, and here is what you get. Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Pre-trained models and datasets built by Google and the community. In this notebook I shall show you an example of using Mobilenet to classify images of dogs. About Keras models. 0 , otherwise you will run into errors. Keras and Convolutional Neural Networks.