Keras Pretrained Models Download

You will learn how to code in Keras in this context. The winners of ILSVRC have been very generous in releasing their models to the open-source community. mp4 --count 20 作成されるビデオのフォーマットは mp4 のみに対応している。. Much has been said about these pretrained models but, the important concepts around transfer learning meaning, how we can use it required some bit of digging. 3D Face Reconstruction from a Single Image. ai and MetaMind had trained models with API calls available. models import Model from keras. In our previous tutorial, we learned how to use models which were trained for Image Classification on the ILSVRC data. Just to recall, the dataset is a combination of the Flickr27-dataset, with 270 images of 27 classes and self-scraped images from google image search. In convert_keras example directory, the complete codes for training and converting a Keras model and running it on the web browsers can be found. For more information about the available models, visit the Applications section of the documentation. Please cite our technical report if you use the models. See the documentation file for further info about each specific hyperparameter. As mentioned, the encoder will be a pretrained MobileNetV2 model which is prepared and ready to use in tf. The pre-trained models included with Keras, are trained on the more limited… Practice while you learn with exercise files Download the files the instructor uses to teach the course. keras-yolo3/ フォルダで yolo_cam. In this tutorial, we will present a simple method to take a Keras model and deploy it as a REST API. Learning Outcomes. YOLO: Real-Time Object Detection. Pretrained Models Object detection tutorial. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. You either use the pretrained model as is or use transfer learning to customize this model to a given task. Keras with Tensorflow back-end in R and Python Longhow Lam 2. # load retinanet modelmodel = models. We will need to write a data function that downloads the images, loads a saved keras model, and creates two classification columns -- one for the classified label and one for the confidence of that classification. Using pretrained models¶ The below example shows how to use the pretrained models. layers import Dense, InputLayer, Dropout. Update (16/12/2017): After installing Anaconda with Python 3. Note that the image generator has many options not documented here (such as adding backgrounds and image augmentation). Install pip install keras-models If you will using the NLP models, you need run one more command: python -m spacy download xx_ent_wiki_sm Usage Guide Import import kearasmodels Examples Reusable. It will teach you how to use Auto-Keras to build custom machine learning and AI models effectively, even with limited machine Learning knowledge. You can then train this model. The network is 50 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. You can find here https://github. models import Sequential from keras. Compile CoreML Models; Compile Keras Models; Deploy a Quantized Model on Cuda; Compile Caffe2 Models; Compile MXNet Models; Deploy the Pretrained Model on Raspberry Pi. Now classification-models works with both frameworks: keras and tensorflow. Keras Applications are deep learning models that are made available alongside pre-trained weights. (This may lead to updating of weights and consume more time. 0 API on March 14, 2017. This is an example of using Relay to compile a keras model and deploy it on Android device. 0 - a Python package on PyPI - Libraries. The file is 22 GB so the download may take a while. import matplotlib. vgg16 (pretrained=False, progress=True, **kwargs) [source] ¶ VGG 16-layer model (configuration "D") "Very Deep Convolutional Networks For Large-Scale Image Recognition" Parameters. Starting with a model from scratch adding more data and using a pretrained model. They are stored at ~/. Let's start by getting your hands on the data. The classification results look decent. Default ``out`` i. by Reece Stevens on February 05, 2018. the whole model. This tutorial demonstrates: How to use TensorFlow Hub with Keras. At this time, we recommend that Keras users who use multi-backend Keras with the TensorFlow backend switch to tf. Face Feature Vector model from keras. The main type of model is called a Sequence which is a linear stack of layers. However, since the data we have is very different as compared to the data used for training our pretrained models. Keras also now comes with pretrained models that can be loaded and used. Here on line three we import the model like this from Keras. ModelZoo curates and provides a platform for deep learning researchers to easily find code and pre-trained models for a variety of platforms and uses. Use a Pretrained GloVe Embedding (ge) Layer. TensorFlow-Keras Models. h5) or JSON (. applications (also seen elsewhere). Use hyperparameter optimization to squeeze more performance out of your model. Which apps u use to open/view it or just direct download and apply only. Inception-ResNet v2 model, with weights trained on ImageNet A Keras model instance. applications. Data augmentation with TFRecord. Each item in the list is a numpy array truncated by the length of the input. There are many Image Recognition built-in Model in the Keras and We will use them. In our case we can safely assume that the features encoded in the model weights to discriminate the 2622 celebrities are enough to accurately describe “any” face. Chris McCormick About Tutorials Archive Google's trained Word2Vec model in Python 12 Apr 2016. As with any dataset we haven't seen, a good rule of thumb is to always take a peek visually at the first couple images. net = importKerasNetwork(modelfile) imports a pretrained TensorFlow™-Keras network and its weights from modelfile. This script loads pre-trained word embeddings (GloVe embeddings) into a frozen Keras Embedding layer, and uses it to train a text classification model on the 20 Newsgroup dataset (classication of newsgroup messages into 20 different categories). YOLO: Real-Time Object Detection. progress – If True, displays a progress bar of the download to stderr. This is the code that fails: base_model = tf. Keras/TensorFlow works just fine on the CPU, but for models with a RNN, you'll want to consider using a GPU for performance. Moreover, Google open-sourced the code and made pretrained models available for download similar to computer vision models pretrained on ImageNet. If the model is trained in NHWC, we should make sure NCHW architecture could consume the pretrained weights. load_model(model_path, backbone_name='resnet50')model = models. DenseNet-Keras with ImageNet Pretrained Models. Sun 05 June 2016 By Francois Chollet. A Keras model instance. This is an Keras implementation of ResNet-152 with ImageNet pre-trained weights. To configure what we actually download, we pass in some important parameters such as: weights [imagenet]: We tell keras to fetch InceptionReNetV2 that was trained on the imagenet dataset. Import the layers from a pretrained Keras network, replace the unsupported layers with custom layers, and assemble the layers into a network ready for prediction. Applications. Do note that the input image format for this model is different than for the VGG16 and ResNet models (299x299 instead of 224x224). Keras Applications are deep learning models that are made available alongside pre-trained weights. Still downloading even Keras has the VGG16 pretrained model in. AlexNet Info#. Pre-Built Image Recognition Model. We are going to load an existing pretrained Keras YOLO model stored in "yolo. /keras/models. We will need to write a data function that downloads the images, loads a saved keras model, and creates two classification columns -- one for the classified label and one for the confidence of that classification. Now this model is trained via Keras for different classes (sitting, jogging, running, walking etc. # load retinanet modelmodel = models. mp4 --count 20 作成されるビデオのフォーマットは mp4 のみに対応している。. You either use the pretrained model as is or use transfer learning to customize this model to a given task. Explore and download deep learning models that you can use directly with MATLAB. However, weights files saved with Keras 1 can still be loaded in Keras 2 models. You can find here https://github. The implementation supports both Theano and TensorFlow backe. GoogLeNet paper: Going deeper with convolutions. There are many pretrained networks available in Caffe Model Zoo. Author: Tomohiro Kato. py script following the syntax Key=Va. This of course depends on how fast, how accurate, and what hardware you want to implement your model on, as well as how much time you have available. Work your way from a bag-of-words model with logistic regression to more advanced methods leading to convolutional neural networks. py script or via command-line-interface. *FREE* shipping on qualifying offers. There are many Image Recognition built-in Model in the Keras and We will use them. There is another approach that involves using pretrained models such as InceptionV3 and ResNet50 that can classify the images. Since Keras is just an API on top of TensorFlow I wanted to play with the underlying layer and therefore implemented image-style-transfer with TF. Weights are downloaded automatically when instantiating a model. First, I tried to adapt the official example on the Keras-rstudio website. There are 2 pretrained models, one for 224x224 images and one fine-tuned for 448x448 images. The network is 48 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. by Reece Stevens on February 05, 2018. Save a Keras model in the format expected by Tensorflow Serving In this example, we will load an inception_v3 model pretrained over imagenet, but it can be replaced with any other Keras model that. 5 was the last release of Keras implementing the 2. ResNet-152 in Keras. Even though ResNet is much deeper than VGG16 and VGG19, the model size is actually substantially smaller due to the usage of global average pooling rather than fully-connected layers — this reduces the model size down to 102MB for ResNet50. progress – If True, displays a progress bar of the download to stderr. You can vote up the examples you like or vote down the ones you don't like. If the model is trained in NHWC, we should make sure NCHW architecture could consume the pretrained weights. h5) or JSON (. This tutorial demonstrates: How to use TensorFlow Hub with Keras. In my work, I am using a CNN model called U-Net instead of Segnet. The returned result is a list with the same length as texts. This is great for making new models, but we also get the pre-trained models of keras. 0 has numerous models built in. How to do simple transfer learning. utils import plot_model plot_model(model, to_file='model. There are many models such as AlexNet, VGGNet, Inception, ResNet, Xception and many more which we can choose from, for our own task. pip install keras-ocr``` Using. The channels of the input images need to be in RGB order (not BGR), with values normalized within [0, 1]. Weights are downloaded automatically when instantiating a model. 9% on COCO test-dev. The following are code examples for showing how to use torchvision. The predictions made using pretrained models would not be effective. You only look once (YOLO) is a state-of-the-art, real-time object detection system. This will ensure your code is properly formatted (without errors) and your directory structure is correct. We build a model from the Softmax probability inputs i. To know more about how DenseNet works, please refer to the original paper. This pretrained model has been designed through the following method: vgg-face-keras: Directly convert the vgg-face model to a keras model; vgg-face-keras-fc: First convert the vgg-face Caffe model to a mxnet model, and then convert it to a keras model. Install pip install keras-models If you will using the NLP models, you need run one more command: python -m spacy download xx_ent_wiki_sm Usage Guide Import import kearasmodels Examples Reusable. The model needs to know what input shape it should expect. On a Pascal Titan X it processes images at 30 FPS and has a mAP of 57. Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. Catalit LLC THIS SESSION • Recap Deep Learning • Keras Recap • Callbacks and Multiple Inputs • Multi GPU • Pretrained Models • Transfer Learning 3. In the first part of this tutorial, we'll briefly review both (1) our example dataset we'll be training a Keras model on, along with (2) our project directory structure. Working on something that can’t be shared? Start a private model instead of public, and you will be the only one with access to the files. I wanted to evaluate this approach on real-world data. Lines 40 and 41 pass the testing images through our pre-trained network, collecting the top predictions (based on the associated class label probabilities). net = importKerasNetwork(modelfile) imports a pretrained TensorFlow™-Keras network and its weights from modelfile. pyplot as plt import keras_ocr # keras-ocr will automatically download pretrained # weights for the detector and recognizer. In this tutorial, we will discuss how to use those models as a Feature Extractor and train a new model for a. Use hyperparameter optimization to squeeze more performance out of your model. This function requires the Deep Learning Toolbox™ Importer for TensorFlow-Keras Models support package. In the first lesson he used the pretrained NN to predict on the Dogs vs Cats competition on Kaggle to achieve very good results. This will plot a graph of the model and save it to a file: from keras. VGG-16 pre-trained model for Keras. YOLO: Real-Time Object Detection. PyTorch version of Google AI BERT model with script to load Google pre-trained models conda install -c conda-forge pytorch-pretrained-bert Download Anaconda. Change input shape dimensions for fine-tuning with Keras. from keras_bert import get_pretrained, PretrainedList, get_checkpoint_paths model_path = get_pretrained Download files. Build TVM Runtime on Device; Set Up RPC Server on Device; Prepare the Pre-trained Model; Compile The Graph; Deploy the Model Remotely by RPC; Compile TFLite Models; Deploy the. Face Feature Vector model from keras. pip install keras-ocr``` Using. There are several other pretrained models in Keras which are also trained on ‘imagenet’ such as InceptionResNet and ResNet. Tuesday May 2, 2017. Pre-trained Models with Keras in TensorFlow. What are pretrained Neural Networks? So let me tell you about the background a little bit. Tensorflow 2. Weights are downloaded automatically when instantiating a model. Pretrained Models. This tutorial assumes that you are slightly familiar convolutional neural networks. Deep learning generating images. The code below is a snippet of how to do this, where the comparison is against the predicted model output and the training data set (the same can be done with the test_data data). It is a reference to a literary image from ancient Greek and Latin literature, first found in the Odyssey, where dream spirits (Oneiroi, singular Oneiros) are divided between those who deceive men with false visions, who arrive to Earth through a gate of ivory, and those who announce a future that will come to pass, who arrive. from keras_resnet. We are going to load an existing pretrained Keras YOLO model stored in "yolo. If the model is trained in NHWC, we should make sure NCHW architecture could consume the pretrained weights. Samrat has 4 jobs listed on their profile. Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras Dipanjan Sarkar , Raghav Bali , Tamoghna Ghosh Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. h5 i am able to download in chrome but unable to open it. MATLAB Central contributions by MathWorks Deep Learning Toolbox Team. Update Mar/2017: Updated for Keras …. net = importKerasNetwork(modelfile) imports a pretrained TensorFlow™-Keras network and its weights from modelfile. It has been obtained through the following method: vgg-face-keras:directly convert the vgg-face matconvnet model to keras model; vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model. Hopefully you've gained the foundation to further explore all that Keras has to offer. h5') This single HDF5 file will contain: the architecture of the model (allowing the recreation of the model). Keras Models Hub. BERT implemented in Keras - 0. This article will talk about implementing Deep learning in R on cifar10 data-set and train a Convolution Neural Network(CNN) model to classify 10,000 test images across 10 classes in R using Keras and Tensorflow packages. 7 SourceRank 7 Keras Models Hub. All pretrained models in pretrained-models. Build TVM Runtime on Device; Set Up RPC Server on Device; Prepare the Pre-trained Model; Compile The Graph; Deploy the Model Remotely by RPC; Compile TFLite Models; Deploy the. In this tutorial I will explain my personal solution to this problem without using any other tool, just using Caffe, Keras and Python. I wanted to evaluate this approach on real-world data. GoogLeNet Info#. But inference with models other than resnet50 doesn't seem to give right results. Chris McCormick About Tutorials Archive Google's trained Word2Vec model in Python 12 Apr 2016. In this tutorial, we will learn how to fine-tune a pre-trained model for a different. Explore and download deep learning models that you can use directly with MATLAB. This is the code to import the pretrained VGG16 model:. The encoder consists of specific outputs from intermediate layers in the model. Using the pre-trained model is easy; just start from the example code included in the quickstart guide. Code prepared for Keras 2. Use Keras Pretrained Models With Tensorflow. Implememnation of various Deep Image Segmentation models in keras image-segmentation-keras Image Segmentation Keras : Implementation of Segnet, FCN, UNet and other models in Keras. The full code for this tutorial is available on Github. Now classification-models works with both frameworks: keras and tensorflow. Home - predict. We've just completed a whirlwind tour of Keras's core functionality, but we've only really scratched the surface. Stack Overflow. In our case we can safely assume that the features encoded in the model weights to discriminate the 2622 celebrities are enough to accurately describe “any” face. Import the layers from a pretrained Keras network, replace the unsupported layers with custom layers, and assemble the layers into a network ready for prediction. You can then train this model. Specifying the input shape. You can then use this model for prediction or transfer learning. We release our two best-performing models, with 16 and 19 weight layers (denoted as configurations D and E in the publication). This example shows how to import the layers from a pretrained Keras network, replace the unsupported layers with custom layers, and assemble the layers into a network ready for prediction. This projects implements 5 pretrained fast style transfer models. You only look once (YOLO) is a state-of-the-art, real-time object detection system. Each item in the list is a numpy array truncated by the length of the input. Note that the image generator has many options not documented here (such as adding backgrounds and image augmentation). These models can be used for prediction, feature extraction, and fine-tuning. This dataset helps to use pretrained keras models in Kernels. Alternatively, you can import layer architecture as a Layer array or a LayerGraph object. you can download the set here. You can use pretrained models as a feature extractor, where you simply modify the output layer according to your dataset. Auto-Keras Pretrained Models for Sentiment Analysis on a Real-World Data Set The Pretrained Models on Some of Our Own Data Auto-Keras Classifier for Sentiment Analysis. Rd Note that only TensorFlow is supported for now, therefore it only works with the data format image_data_format='channels_last' in your Keras config at ~/. The models are compatible with the Caffe toolbox. keras/keras. The following are code examples for showing how to use torchvision. keras is better maintained and has better integration with TensorFlow features (eager execution, distribution support and other). To know more about how DenseNet works, please refer to the original paper. pyplot as plt import keras_ocr # keras-ocr will automatically download pretrained # weights for the detector and recognizer. Moreover, Google open-sourced the code and made pretrained models available for download similar to computer vision models pretrained on ImageNet. You can freeze some of the layers in the pretrained model, then make other layers trainable on your dataset. Note that the weights are about 528 megabytes, so the download may take a few minutes depending on the speed of your Internet connection. What is an adversarial example. Transfer Learning using pre-trained models in Keras; Fine-tuning pre-trained models in Keras; More to come. Working on something that can't be shared? Start a private model instead of public, and you will be the only one with access to the files. Having to train an image-classification model using very little data is a common situation, in this article we review three techniques for tackling this problem including feature extraction and fine tuning from a pretrained network. Even though ResNet is much deeper than VGG16 and VGG19, the model size is actually substantially smaller due to the usage of global average pooling rather than fully-connected layers — this reduces the model size down to 102MB for ResNet50. py を実行する。 $ python yolo_cam. Load pretrained keras model; Load a test image; Compile the model with Relay; Execute on TVM; Look up synset name; Deploy a Quantized Model on Cuda; Compile Caffe2 Models; Compile MXNet Models; Deploy the Pretrained Model on Raspberry Pi; Compile TFLite Models; Deploy the Pretrained Model on Android. For now, there is a caffe model zoo which has a collection of models with verified performance,. when I look up a predicted label index in the imagenet metadata file, the corresponding class description is definitely different from the image content. Pre-trained models present in Keras. Build Deep Learning Models with Keras. Keras on tensorflow in R & Python 1. You only look once (YOLO) is a state-of-the-art, real-time object detection system. There are many pretrained networks available in Caffe Model Zoo. I wanted to evaluate this approach on real-world data. The first time you run this example, Keras will download the weight files from the Internet and store them in the ~/. Import the layers from a pretrained Keras network, replace the unsupported layers with custom layers, and assemble the layers into a network ready for prediction. 1, Dice coef: 0. In this tutorial, we will learn how to fine-tune a pre-trained model for a different. Flexible. This code should work fine on both Theano and Tensorflow backends. Rd Note that only TensorFlow is supported for now, therefore it only works with the data format image_data_format='channels_last' in your Keras config at ~/. Catalit LLC ADVANCED KERAS Francesco Mosconi Data Weekends Catalit LLC 2. Pre-train的model:就是指之前被训练好的Model, 比如很大很耗时间的model, 你又不想从头training一遍。这时候可以直接download别人训练好的model, 里面保存的都 博文 来自: tkzc_csk的博客. save('my_model. DenseNet-Keras with ImageNet Pretrained Models. keras-ocr provides a convenience function for this Weights can be loaded into the model attribute of the. How to do image classification using TensorFlow Hub. pd and labels. caffemodel file datafile. See the TensorFlow Module Hub for a searchable listing of pre-trained models. The pre-trained models included with Keras, are trained on the more limited… Practice while you learn with exercise files Download the files the instructor uses to teach the course. Still downloading even Keras has the VGG16 pretrained model in. You can exchange models with TensorFlow™ and PyTorch through the ONNX™ format and import models from TensorFlow-Keras and Caffe. keras/keras. These hyperparameters are set in theconfig. This projects implements 5 pretrained fast style transfer models. ResNet-152 in Keras. 5 and CUDA 9. Explore and download deep learning models that you can use directly with MATLAB. The library is designed to work both with Keras and TensorFlow Keras. prototxt and. The weights are converted from Caffe Models. GitHub Gist: instantly share code, notes, and snippets. Keras support two types of APIs: Sequential and Functional. 신기하고 재밌는 인공지능을 쉽게, 짧게, 내손으로 만들어 봅니다! 개발 의뢰는 카카오톡 또는 이메일로 문의주세요 :). ResNet-50 is a convolutional neural network that is trained on more than a million images from the ImageNet database. Now classification-models works with both frameworks: keras and tensorflow. I am new to Keras and would appreciate any insight in Keras code on how I can go about downloading and skipping the VGG weights that have no counterpart with my U-Netmodel. Here’s an example for how you might do it. NASNet-Large. 3, it should be at tf. The EfficientNet code are borrowed from the A PyTorch implementation of EfficientNet,if you want to train EffcicientDet from scratch,you should load the efficientnet pretrained parameter. It's a family of algorithms loosely based on a biological…. So I thought of classifying the audios of supercars and heavy bikes. TensorFlow Hub is a way to share pretrained model components. h5 is used to load pretrained weights. This tutorial demonstrates: How to use TensorFlow Hub with Keras. There is a cha. datasets import cifar10 (x_train, y_train), (x_test, y_test) = cifar10. kaggle could not download resnet50 pretrained model-1. 1, Keras is now at tf. Strategy API. The models we will use have all been trained on the large ImageNet data set, and learned to produce a compact representation of an image in the form of a feature vector. The predictions made using pretrained models would not be effective. At this time, we recommend that Keras users who use multi-backend Keras with the TensorFlow backend switch to tf. 0 Install pip install keras-models==0. The network is 164 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. Load pretrained keras model; Load a test image; Compile the model with Relay; Execute on TVM; Look up synset name; Deploy a Quantized Model on Cuda; Compile Caffe2 Models; Compile MXNet Models; Deploy the Pretrained Model on Raspberry Pi; Compile TFLite Models; Deploy the Pretrained Model on Android. Results on ImageNet and Pretrained Models; Other Implementations (in Caffe, TensorFlow, PyTorch, Keras, Lasagne, MXNet and Chainer) Updates; Introduction. torchvision. 신기하고 재밌는 인공지능을 쉽게, 짧게, 내손으로 만들어 봅니다! 개발 의뢰는 카카오톡 또는 이메일로 문의주세요 :). All pretrained models in pretrained-models. It's a family of algorithms loosely based on a biological…. For more information about the available models, visit the Applications section of the documentation. Keras Pipelines 0. This will ensure your code is properly formatted (without errors) and your directory structure is correct. Github link is the public LB score is 0. August 21, 2019 14min read Automate the diagnosis of Knee Injuries 🏥 with Deep Learning part 3: Interpret models' predictions. This code should work fine on both Theano and Tensorflow backends. We've just completed a whirlwind tour of Keras's core functionality, but we've only really scratched the surface. In this tutorial, we will discuss how to use those models as a Feature Extractor and train a new model for a. Keras is an API designed for human beings, not machines. The last months, I have worked on brand logo detection in R with Keras. The network is 164 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. In this tutorial, we will walk you through the process of solving a text classification problem using pre-trained word embeddings and a convolutional neural network. Today's blog post is a complete guide to running a deep neural network on the Raspberry Pi using Keras. Then the model is loaded from the filesystem and converted to run inference. In this tutorial, we will learn how to fine-tune a pre-trained model for a different. Working on something that can’t be shared? Start a private model instead of public, and you will be the only one with access to the files. This tutorial assumes that you are slightly familiar convolutional neural networks. Deep learning generating images. MATLAB Central contributions by MathWorks Deep Learning Toolbox Team. tutorial_keras. 1, if you need code for Keras 1. They are from open source Python projects. The network is 164 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. The encoder consists of specific outputs from intermediate layers in the model. The library is designed to work both with Keras and TensorFlow Keras. Here’s an example for how you might do it. Update Mar/2017: Updated for Keras …. py script or via command-line-interface. Keras - Save and Load Your Deep Learning Models. These models can be used for prediction, feature extraction, and fine-tuning. Specifically, in the case of computer vision, many pretrained models (usually trained on the ImageNet dataset) are now publicly available for download and can be used to bootstrap powerful vision models out of very little data. h5') This single HDF5 file will contain: the architecture of the model (allowing the recreation of the model). Deeplearning is the buzz word right now. A Keras tensor is a tensor object from the underlying backend (Theano, TensorFlow or CNTK), which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model. from keras_bert import get_pretrained, PretrainedList, get_checkpoint_paths model_path = get_pretrained Download files. a pretrained ResNet-50 as np from sklearn. py --coco_path '/home/hoo/Dataset/COCO' --backbon 'efficientnet-b0' --backbone_pretrained True. Pretrained Models.