Deploy with Roboflow

EfficientNet

EfficientNet is from a family of image classification models from GoogleAI that train comparatively quickly on small amounts of data, making the most of limited datasets.

Try the Model

Use the widget below to experiment with EfficientNet. You can detect COCO classes such as people, vehicles, animals, household items.

Overview

What is EfficientNet?

EfficientNet is a family of convolutional neural networks and these models efficiently scale up in terms of layer depth, layer width, input resolution, or a combination of all of these factors. EfficientNet allows us to form features from images that can later be passed into a classifier. This allows for EfficientNet to serve as a backbone to many other models--one of which is EfficientDet, an object detection model family. This version of EfficientNEt is implemented in Keras, which is abstracted, so we can load a custom dataset and train EfficientNet all in a few lines of code.

EfficientNet Architecture

EfficientNet Results

EfficientNet is able to achieve higher accuracies and efficiencies all while reducing the parameter size and FLOPS.

Image Citation

EfficientNet License

EfficientNet

is licensed under a

license.

Performance

Deploy a EfficientNet API

You can use Roboflow Inference to deploy a

EfficientNet

API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).

Below are instructions on how to deploy your own model API.

Label Data Automatically with EfficientNet

You can automatically label a dataset using EfficientNet with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use EfficientNet to train a computer vision model.

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