readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - vision
  datasets:
  - imagenet
  ---
  
  # PoolFormer (S24 model)
  
  PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sail-sg/poolformer). 
  
  
  ## Model description
  
  PoolFormer is a model that replaces attention token mixer in transfomrers with extremely simple operator, pooling.
  
  Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design.
  
  ## Intended uses & limitations
  
  You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=sail/poolformer) to look for fine-tuned versions on a task that interests you.
  
  ### How to use
  
  Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
  
  ```python
  from transformers import PoolFormerFeatureExtractor, PoolFormerForImageClassification
  from PIL import Image
  import requests
  url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
  image = Image.open(requests.get(url, stream=True).raw)
  feature_extractor = PoolFormerFeatureExtractor.from_pretrained('sail/poolformer_s24')
  model = PoolFormerForImageClassification.from_pretrained('sail/poolformer_s24')
  inputs = feature_extractor(images=image, return_tensors="pt")
  outputs = model(**inputs)
  logits = outputs.logits
  # model predicts one of the 1000 ImageNet classes
  predicted_class_idx = logits.argmax(-1).item()
  print("Predicted class:", model.config.id2label[predicted_class_idx])
  ```
  
  Currently, both the feature extractor and model support PyTorch. 
  
  ## Training data
  
  The poolformer model was trained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. 
  
  ## Training procedure
  
  ### Preprocessing
  
  The exact details of preprocessing of images during training/validation can be found [here](https://github.com/sail-sg/poolformer/blob/main/train.py#L529-L572). 
  
  
  ### Pretraining
  
  The model was trained on TPU-v3s. Training resolution is 224. For all hyperparameters (such as batch size and learning rate), please refer to the original paper.
  
  ## Evaluation results
  
  | Model                                 | ImageNet top-1 accuracy | # params | URL                                                              |
  |---------------------------------------|-------------------------|----------|------------------------------------------------------------------|
  | PoolFormer-S12                             | 77.2                    | 12M       | https://huggingface.co/sail/poolformer_s12            |
  | **PoolFormer-S24**                            | **80.3**                    | **21M**      | **https://huggingface.co/sail/poolformer_s24**           |
  | PoolFormer-S36                         | 81.4                | 31M  | https://huggingface.co/sail/poolformer_s36            |
  | PoolFormer-M36                   | 82.1                    | 56M       | https://huggingface.co/sail/poolformer_m36  |
  | PoolFormer-M48                  | 82.5                    | 73M      | https://huggingface.co/sail/poolformer_m48 |
  
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{yu2021metaformer,
    title={MetaFormer is Actually What You Need for