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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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--- |
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# Big Transfer (BiT) |
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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. |
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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. |
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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. |
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## Model description |
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The abstract from the paper is the following: |
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*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.* |
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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=bit) 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 BitImageProcessor, BitForImageClassification |
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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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feature_extractor = BitImageProcessor.from_pretrained("google/bit-50") |
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model = BitForImageClassification.from_pretrained("google/bit-50") |
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inputs = feature_extractor(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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>>> tabby, tabby cat |
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``` |
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For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/bit). |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{https://doi.org/10.48550/arxiv.1912.11370, |
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doi = {10.48550/ARXIV.1912.11370}, |
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url = {https://arxiv.org/abs/1912.11370}, |
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author = {Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Puigcerver, Joan and Yung, Jessica and Gelly, Sylvain and Houlsby, Neil}, |
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keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Big Transfer (BiT): General Visual Representation Learning}, |
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publisher = {arXiv}, |
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year = {2019}, |
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copyright = {arXiv.org perpetual, non-exclusive license} |
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} |
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``` |
