In this tutorial, you will learn how to create an image classification neural network to classify your custom images. Why is it so efficient? To answer the question, we will dive into its base model and building block. You might have heard of the building block for the classical ResNet model is identity and convolution block. Where k stands for the kernel size, specifying the height and width of the 2D convolution window.
The second benefit of EfficientNet, it scales more efficiently by carefully balancing network depth, width, and resolution, which lead to better performance. Transfer learning for image classification is more or less model agnostic. A pre-trained network is simply a saved network previously trained on a large dataset such as ImageNet.
The easiest way to get started is by opening this notebook in Colab, while I will explain more detail here in this post. First clone my repository which contains the Tensorflow Keras implementation of the EfficientNet, then cd into the directory. The EfficientNet is built for ImageNet classification contains classes labels. For our dataset, we only have 2. Which means the last few layers for classification is not useful for us.
To create our own classification layers stack on top of the EfficientNet convolutional base model. To keep the convolutional base's weight untouched, we will freeze it, otherwise, the representations previously learned from the ImageNet dataset will be destroyed. Another technique to make the model representation more relevant for the problem at hand is called fine-tuning. That is based on the following intuition. Earlier layers in the convolutional base encode more generic, reusable features, while layers higher up encode more specialized features.
Then you can compile and train the model again for some more epochs. An example is made runnable on Colab Notebook showing you how to build a model reusing the convolutional base of EfficientNet and fine-tuning last several layers on the custom dataset.
Training EfficientNet on Cloud TPU (TF 2.x)
The full source code is available on my GitHub repo. TensorFlow implementation of EfficientNet. Everything Blog posts Pages. Home About Me Blog Support.
Sequential model. Current rating: 4.The EfficientNet models are a family of image classification models, which achieve state-of-the-art accuracy, while also being smaller and faster than other models. Researchers developed a new technique to improve model performance: carefully balancing network depth, width, and resolution, using a simple yet highly effective compound coefficient.
The family of models from efficientnet-b0 to efficientnet-b7can achieve decent image classification accuracy given the resource constrained Google EdgeTPU devices. The tutorial demonstrates training the model using TPUEstimator. Go to the project selector page. Make sure that billing is enabled for your Google Cloud project.
Learn how to confirm billing is enabled for your project. This walkthrough uses billable components of Google Cloud. Check the Cloud TPU pricing page to estimate your costs. Be sure to clean up resources you create when you've finished with them to avoid unnecessary charges.
Open Cloud Shell. Configure gcloud command-line tool to use the project where you want to create Cloud TPU. This Cloud Storage bucket stores the data you use to train your model and the training results. The ctpu up tool used in this tutorial sets up default permissions for the Cloud TPU service account. If you want finer-grain permissions, review the access level permissions. VMs and TPU nodes are located in specific zoneswhich are subdivisions within a region.
When the ctpu up command has finished executing, verify that your shell prompt has changed from username projectname to username vm-name. This change shows that you are now logged into your Compute Engine VM. Prepare the data Set up the following environment variables, replacing bucket-name with the name of your Cloud Storage bucket:. Create an environment variable for your bucket name. Replace bucket-name with your bucket name. The training application expects your training data to be accessible in Cloud Storage.
The training application also uses your Cloud Storage bucket to store checkpoints during training. ImageNet is an image database. The images in the database are organized into a hierarchy, with each node of the hierarchy depicted by hundreds and thousands of images. This demonstration version allows you to test the tutorial, while reducing the storage and time requirements typically associated with running a model against the full ImageNet database.
The accuracy numbers and saved model will not be meaningful. For information on how to download and process the full ImageNet dataset, see Downloading, preprocessing, and uploading the ImageNet dataset. This procedure trains the EfficientNet model efficientnet-b0 variant for epochs and evaluates every fixed number of steps. Using the specified flags, the model should train in about 23 hours. The fully supported model can work with the following Pod slices:.
Run the ctpu up command, using the tpu-size parameter to specify the Pod slice you want to use. For example, the following command uses a v Pod slice. If the folder is missing, the program creates one. You can reuse an existing folder to load current checkpoint data and to store additional checkpoints as long as the previous checkpoints were created using TPU of the same size and TensorFlow version. The procedure trains the EfficientNet model efficientnet-b3 variant for epochs.At the heart of many computer vision tasks like image classification, object detection, segmentation, etc.
AlexNet used a whopping 62 million parameters! Soon people figured out the obvious ways in which AlexNet was not efficient. After these initial inefficiencies were recognized and fixed, accuracy improvements in subsequent years came at the expense of an increased number of model parameters.
Even though, we can notice a trade off, it is not obvious how to design a new network that allows us to use this information.
For example, we know GoogleNet has 6. How do we now design a network that is say half the size even though it is less accurate? Want to learn Deep Learning and Computer Vision in depth?
The idea of model scaling is to use a standard model like GoogleNet or ResNet and modify the architecture in one or more of the following ways. What is not obvious is how to choose how much deeper or wider one should make the network or how much to increase the image size.
Searching over one dimension, say depth, is itself very expensive. Searching over depth, width, and image resolution is almost impossible. In the paper, the authors propose a compound scaling method that uses a compound coefficient of to uniformly scale width, depth, and resolution in a principled way. The FLOPS consumed in a convolutional operation is proportional to, andand this fact is reflected in the above equation. The authors restrict to 2 so that every newthe FLOPs needed goes by up.
The above equation suggests we can do model scaling on any CNN architecture. While that is true, the authors found that the choice of the initial model to scale makes a difference in the final output. So they developed their own baseline architecture and called it EfficientNet-B0. So, the final architecture is similar to MnasNet. We have also linked to the detailed EfficientNet-B0 architecture here. You may be thinking why, and are not re-evaluated at every scaling step.
The reason is that it is computationally expensive to do so. So, if you were planning to use Inception-v2, you should consider using EfficientNet-B1 instead.
In most real-world applications, people start with a pre-trained model and fine-tune it for their specific application. Just because EfficientNet out-performs other networks, does it mean it will out-perform other networks on other tasks?
The good news is that the authors have done those experiments and shown when the EfficientNet backbone is used, we get better performance in other computer vision tasks as well.
All example code shared in this post has been written by my teammate Vishwesh Shrimali. First, we will install efficientnet module which will provide us the EfficientNet-B0 pre-trained model that we will use for inference. In this case, the model was able to predict that image was of a Giant Panda with an accuracy of Objectives Create a Cloud Storage bucket to hold your dataset and model output.
Prepare a fake imagenet dataset that is similar to the ImageNet dataset. Run the training job. Verify the output results. Costs This tutorial uses billable components of Google Cloud, including:. Use the pricing calculator to generate a cost estimate based on your projected usage.
New Google Cloud users might be eligible for a free trial. Before you begin Before starting this tutorial, check that your Google Cloud project is correctly set up. If you don't already have one, sign up for a new account.
Go to the project selector page.
Make sure that billing is enabled for your Google Cloud project. Learn how to confirm billing is enabled for your project. This walkthrough uses billable components of Google Cloud.
Check the Cloud TPU pricing page to estimate your costs. Be sure to clean up resources you create when you've finished with them to avoid unnecessary charges.
Open Cloud Shell. Configure gcloud command-line tool to use the project where you want to create Cloud TPU. This Cloud Storage bucket stores the data you use to train your model and the training results. The ctpu up tool used in this tutorial sets up default permissions for the Cloud TPU Service Account you set up in the previous step.
EfficientNet: Theory + Code
If you want finer-grain permissions, review the access level permissions. When the ctpu up command has finished executing, verify that your shell prompt has changed from username projectname to username vm-name. This change shows that you are now logged into your Compute Engine VM.
Set Cloud Storage bucket variables Set up the following environment variables, replacing bucket-name with the name of your Cloud Storage bucket:. The training application expects your training data to be accessible in Cloud Storage. The training application also uses your Cloud Storage bucket to store checkpoints during training.
ImageNet is an image database. The images in the database are organized into a hierarchy, with each node of the hierarchy depicted by hundreds and thousands of images. This demonstration version allows you to test the tutorial, while reducing the storage and time requirements typically associated with running a model against the full ImageNet database. The accuracy numbers and saved model will not be meaningful. For information on how to download and process the full ImageNet dataset, see Downloading, preprocessing, and uploading the ImageNet dataset.
Set the Cloud TPU name variable. This will either be a name you specified with the --name parameter to ctpu up or the default, your username:. Run the training script. At the end of the training, output similar to the following appears:. To train the EfficientNet to convergence, run it for 90 epochs as shown in the following script.
Training and evaluation are done together. Each epoch has steps for a total of training steps and 48 evaluation steps.In this tutorial, we will train state of the art EfficientNet convolutional neural networkto classify images, using a custom dataset and custom classifications. To run this tutorial on your own custom dataset, you need to only change one line of code for your dataset import. We train our classifier to recognize rock, paper, scissors hand gestures - but the tutorial is written generally so you can use this approach to classify your images into any classification type, given the right supervision in your dataset.
Given an image, we are seeking to identify the image as belonging to one class in a series of potential class labels. Our model will form features from the image, pass these features through a deep neural network, and output a series of probabilities corresponding to the likelihood that the image belongs to each of those classes.
We can assume that the highest probability that is output corresponds with the models prediction. In our tutorial we will be training a model to classify rock, paper, scissors hand gestures in the popular game. EfficientNet is a state of the art convolutional neural network, released open source by Google Brain. The primary contribution in EfficientNet was to thoroughly test how to efficiently scale the size of convolutional neural networks.
For example, one could make a ConvNet larger based on width of layers, depth of layers, the image input resolution, or a combination of all of those levers.
EfficientNet forms the backbone for the state of the art object detector EfficientDet. Object detection goes one step further to localize as well as classify objects in an object. EfficientNet is currently the most performant convolutional neural network for classification. Image Classifiers are typically benchmarked on ImageNet, an image database organized according to the WordNet hierarchy, containing hundreds of thousands of labeled images.
As you can see in the table, 4 out of the top 5 approaches to the ImageNet task are all based on EfficientNet.
As a nice added bonus, the EfficientNet models we use in this tutorial have been pretrained on ImageNet, meaning that they already have a solid understanding of general features used to classify images. This notebook is based on the original tutorial by DLogogy and has been updated to fix software versioning and the dataset import and creation now easily flows through with Roboflow.
The first step we take in the notebook is to select the correct tensorflow environment, the codebase is still running on tensorflow 1. We also check our keras version, in this pass we are using keras 2. Then we import some packages and clone the EfficientNet keras repository. The biggest contribution of EfficientNet was to study how ConvNets can be efficiently scaled up.
In this notebook, you can take advantage of that fact! In the line from efficientnet import EfficientNetB0 as Net you can choose between the size of model you would like to use. The larger the better performance, but watch out training time will slow down with larger models and you may run out of GPU memory with the free Colab GPUs. Next, before loading the model, we choose the input resolution. We start with x here for GPU memory and to get a feel for the classification script, but it may be useful to scale this up on your task later.
Now, you can import your own data to use transfer learning to teach EfficientNet to classify images into your custom classes. If you are just following along with the tutorial, we recommend using this public rock, paper, scissors dataset.Are you asking about inserting a link into text.
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Training EfficientNet on Cloud TPU
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