
# FocalNet (small-sized model) 

FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Focal Modulation Networks
](https://arxiv.org/abs/2203.11926) by Yang et al. and first released in [this repository](https://github.com/microsoft/FocalNet). 

Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. 
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.

![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/focalnet_architecture.png)

## Intended uses & limitations

You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=focalnet) 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 FocalNetImageProcessor, FocalNetForImageClassification
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

preprocessor = FocalNetImageProcessor.from_pretrained("microsoft/focalnet-small")
model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-small")

inputs = preprocessor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]),
```

For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/focalnet).

### BibTeX entry and citation info

```bibtex
@article{DBLP:journals/corr/abs-2203-11926,
  author       = {Jianwei Yang and
                  Chunyuan Li and
                  Jianfeng Gao},
  title        = {Focal Modulation Networks},
  journal      = {CoRR},
  volume       = {abs/2203.11926},
  year         = {2022},
  url          = {https://doi.org/10.48550/arXiv.2203.11926},
  doi          = {10.48550/arXiv.2203.11926},
  eprinttype    = {arXiv},
  eprint       = {2203.11926},
  timestamp    = {Tue, 29 Mar 2022 18:07:24 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2203-11926.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet-1k"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
transformers pytorch focalnet image-classification vision dataset:imagenet-1k arxiv:2203.11926 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #focalnet #image-classification #vision #dataset-imagenet-1k #arxiv-2203.11926 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# FocalNet (small-sized model) 

FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Focal Modulation Networks
 by Yang et al. and first released in this repository. 

Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. 
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.

!model image

## Intended uses & limitations

You can use the raw model for image classification. See the model hub 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:



For more code examples, we refer to the documentation.

### BibTeX entry and citation info


# FocalNet (small-sized model) 

FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Focal Modulation Networks
 by Yang et al. and first released in this repository. 

Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description

Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. 
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.

!model image ## Intended uses & limitations

You can use the raw model for image classification. See the model hub 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:



For more code examples, we refer to the documentation. ### BibTeX entry and citation info
TAGS
#transformers #pytorch #focalnet #image-classification #vision #dataset-imagenet-1k #arxiv-2203.11926 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # FocalNet (small-sized model) 

FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Focal Modulation Networks
 by Yang et al. and first released in this repository. 

Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description

Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. 
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.

!model image ## Intended uses & limitations

You can use the raw model for image classification. See the model hub 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:



For more code examples, we refer to the documentation. ### BibTeX entry and citation info
TAGS
#transformers #pytorch #focalnet #image-classification #vision #dataset-imagenet-1k #arxiv-2203.11926 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# FocalNet (small-sized model) 

FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper Focal Modulation Networks
 by Yang et al. and first released in this repository. 

Disclaimer: The team releasing FocalNet did not write a model card for this model so this model card has been written by the Hugging Face team.## Model description

Focul Modulation Networks are an alternative to Vision Transformers, where self-attention (SA) is completely replaced by a focal modulation mechanism for modeling token interactions in vision. 
Focal modulation comprises three components: (i) hierarchical contextualization, implemented using a stack of depth-wise convolutional layers, to encode visual contexts from short to long ranges, (ii) gated aggregation to selectively gather contexts for each query token based on its
content, and (iii) element-wise modulation or affine transformation to inject the aggregated context into the query. Extensive experiments show FocalNets outperform the state-of-the-art SA counterparts (e.g., Vision Transformers, Swin and Focal Transformers) with similar computational costs on the tasks of image classification, object detection, and segmentation.

!model image## Intended uses & limitations

You can use the raw model for image classification. See the model hub 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:



For more code examples, we refer to the documentation.### BibTeX entry and citation info