Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- vision
+- image-classification
+datasets:
+- imagenet-1k
+---
+
+# Big Transfer (BiT)
+
+The BiT model was proposed in [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
+BiT is a simple recipe for scaling up pre-training of [ResNet](resnet)-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning.
+
+
+Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+The abstract from the paper is the following:
+
+*Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes -- from 1 example per class to 1M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.*
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/resnet_architecture.png)
+
+## Intended uses & limitations
+
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=bit) 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 AutoFeatureExtractor, BiTForImageClassification
+import torch
+from datasets import load_dataset
+
+dataset = load_dataset("huggingface/cats-image")
+image = dataset["test"]["image"][0]
+
+feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
+model = BiTForImageClassification.from_pretrained("google/bit-50")
+
+inputs = feature_extractor(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/main/en/model_doc/bit).
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.1912.11370,
+  doi = {10.48550/ARXIV.1912.11370},
+  
+  url = {https://arxiv.org/abs/1912.11370},
+  
+  author = {Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Puigcerver, Joan and Yung, Jessica and Gelly, Sylvain and Houlsby, Neil},
+  
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
+  title = {Big Transfer (BiT): General Visual Representation Learning},
+  
+  publisher = {arXiv},
+  
+  year = {2019},
+  
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+
+```