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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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- image-classification |
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datasets: |
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- imagenet-1k |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg |
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example_title: Tiger |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg |
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example_title: Teapot |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg |
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example_title: Palace |
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--- |
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# FocalNet (tiny-sized large reception field model) |
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FocalNet model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Focal Modulation Networks |
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](https://arxiv.org/abs/2203.11926) by Yang et al. and first released in [this repository](https://github.com/microsoft/FocalNet). |
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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. |
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## Model description |
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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. |
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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 |
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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. |
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## Intended uses & limitations |
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You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=focalnet) to look for |
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fine-tuned versions on a task that interests you. |
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### How to use |
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Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: |
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```python |
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from transformers import FocalNetImageProcessor, FocalNetForImageClassification |
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import torch |
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from datasets import load_dataset |
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dataset = load_dataset("huggingface/cats-image") |
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image = dataset["test"]["image"][0] |
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preprocessor = FocalNetImageProcessor.from_pretrained("microsoft/focalnet-tiny-lrf") |
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model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny-lrf") |
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inputs = preprocessor(image, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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# model predicts one of the 1000 ImageNet classes |
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predicted_label = logits.argmax(-1).item() |
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print(model.config.id2label[predicted_label]), |
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``` |
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For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/focalnet). |
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### BibTeX entry and citation info |
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```bibtex |
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@article{DBLP:journals/corr/abs-2203-11926, |
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author = {Jianwei Yang and |
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Chunyuan Li and |
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Jianfeng Gao}, |
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title = {Focal Modulation Networks}, |
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journal = {CoRR}, |
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volume = {abs/2203.11926}, |
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year = {2022}, |
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url = {https://doi.org/10.48550/arXiv.2203.11926}, |
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doi = {10.48550/arXiv.2203.11926}, |
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eprinttype = {arXiv}, |
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eprint = {2203.11926}, |
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timestamp = {Tue, 29 Mar 2022 18:07:24 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2203-11926.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
