mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-06 22:30:09 +06:00
69 lines
3.5 KiB
Plaintext
69 lines
3.5 KiB
Plaintext
<!--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.
|
||
-->
|
||
|
||
# EfficientFormer
|
||
|
||
## Overview
|
||
|
||
The EfficientFormer model was proposed in [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/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
|