86 lines
4.2 KiB
Markdown
86 lines
4.2 KiB
Markdown
|
<!--Copyright 2022 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.
|
|||
|
|
|
|||
|
|
-->
|
|||
|
|
*This model was released on 2022-06-02 and added to Hugging Face Transformers on 2023-06-20.*
|
|||
|
|
|
|||
|
|
# EfficientFormer
|
|||
|
|
|
|||
|
|
<div class="flex flex-wrap space-x-1">
|
|||
|
|
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
|
|||
|
|
</div>
|
|||
|
|
|
|||
|
|
<Tip warning={true}>
|
|||
|
|
|
|||
|
|
This model is in maintenance mode only, we don't accept any new PRs changing its code.
|
|||
|
|
If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
|
|||
|
|
You can do so by running the following command: `pip install -U transformers==4.40.2`.
|
|||
|
|
|
|||
|
|
</Tip>
|
|||
|
|
|
|||
|
|
## Overview
|
|||
|
|
|
|||
|
|
The EfficientFormer model was proposed in [EfficientFormer: Vision Transformers at MobileNet Speed](https://huggingface.co/papers/2206.01191)
|
|||
|
|
by Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren. EfficientFormer proposes a
|
|||
|
|
dimension-consistent pure transformer that can be run on mobile devices for dense prediction tasks like image classification, object
|
|||
|
|
detection and semantic segmentation.
|
|||
|
|
|
|||
|
|
The abstract from the paper is the following:
|
|||
|
|
|
|||
|
|
*Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks.
|
|||
|
|
However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally
|
|||
|
|
times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly
|
|||
|
|
challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation
|
|||
|
|
complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still
|
|||
|
|
unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance?
|
|||
|
|
To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs.
|
|||
|
|
Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm.
|
|||
|
|
Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer.
|
|||
|
|
Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices.
|
|||
|
|
Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on
|
|||
|
|
iPhone 12 (compiled with CoreML), which { runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1),} and our largest model,
|
|||
|
|
EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can
|
|||
|
|
reach extremely low latency on mobile devices while maintaining high performance.*
|
|||
|
|
|
|||
|
|
This model was contributed by [novice03](https://huggingface.co/novice03) and [Bearnardd](https://huggingface.co/Bearnardd).
|
|||
|
|
The original code can be found [here](https://github.com/snap-research/EfficientFormer).
|
|||
|
|
|
|||
|
|
## Documentation resources
|
|||
|
|
|
|||
|
|
- [Image classification task guide](../tasks/image_classification)
|
|||
|
|
|
|||
|
|
## EfficientFormerConfig
|
|||
|
|
|
|||
|
|
[[autodoc]] EfficientFormerConfig
|
|||
|
|
|
|||
|
|
## EfficientFormerImageProcessor
|
|||
|
|
|
|||
|
|
[[autodoc]] EfficientFormerImageProcessor
|
|||
|
|
- preprocess
|
|||
|
|
|
|||
|
|
## EfficientFormerModel
|
|||
|
|
|
|||
|
|
[[autodoc]] EfficientFormerModel
|
|||
|
|
- forward
|
|||
|
|
|
|||
|
|
## EfficientFormerForImageClassification
|
|||
|
|
|
|||
|
|
[[autodoc]] EfficientFormerForImageClassification
|
|||
|
|
- forward
|
|||
|
|
|
|||
|
|
## EfficientFormerForImageClassificationWithTeacher
|
|||
|
|
|
|||
|
|
[[autodoc]] EfficientFormerForImageClassificationWithTeacher
|
|||
|
|
- forward
|