Create README.md

README.md

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+---
+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
+---
+
+# FocalNet (tiny-sized large reception field 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-tiny-lrf")
+model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny-lrf")
+
+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}
+}
+```