52 lines
2.8 KiB
Markdown
52 lines
2.8 KiB
Markdown
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
|
the License. You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
|
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
|
specific language governing permissions and limitations under the License.
|
|
|
|
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
|
rendered properly in your Markdown viewer.
|
|
|
|
-->
|
|
|
|
# EfficientNet
|
|
|
|
## Overview
|
|
|
|
The EfficientNet model was proposed in [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
|
|
by Mingxing Tan and Quoc V. Le. EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models.
|
|
|
|
The abstract from the paper is the following:
|
|
|
|
*Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet.
|
|
To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters.*
|
|
|
|
This model was contributed by [adirik](https://huggingface.co/adirik).
|
|
The original code can be found [here](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet).
|
|
|
|
|
|
## EfficientNetConfig
|
|
|
|
[[autodoc]] EfficientNetConfig
|
|
|
|
## EfficientNetImageProcessor
|
|
|
|
[[autodoc]] EfficientNetImageProcessor
|
|
- preprocess
|
|
|
|
## EfficientNetModel
|
|
|
|
[[autodoc]] EfficientNetModel
|
|
- forward
|
|
|
|
## EfficientNetForImageClassification
|
|
|
|
[[autodoc]] EfficientNetForImageClassification
|
|
- forward
|
|
|