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*This model was released on 2019-12-24 and added to Hugging Face Transformers on 2022-12-07 and contributed by [nielsr](https://huggingface.co/nielsr).*

# Big Transfer (BiT)

[Big Transfer (BiT): General Visual Representation Learning](https://huggingface.co/papers/1912.11370) proposes a method for scaling up pre-training of ResNetv2 architectures. This approach, called Big Transfer (BiT), combines specific components and uses a simple heuristic for transfer learning, achieving strong performance across over 20 datasets. BiT demonstrates robustness across various data regimes, from 1 example per class to 1M total examples. It 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 reaches 76.8% on ILSVRC-2012 with 10 examples per class and 97.0% on CIFAR-10 with 10 examples per class. The paper includes a detailed analysis of the key components contributing to high transfer performance.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="image-classification", model="google/bit-50", dtype="auto")
pipeline("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
```

</hfoption>
<hfoption id="AutoModel">

```python
import torch
import requests
from PIL import Image
from transformers import AutoImageProcessor, AutoModelForImageClassification

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("google/bit-50")
model = AutoModelForImageClassification.from_pretrained("google/bit-50", dtype="auto")

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

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

predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
```

</hfoption>
</hfoptions>

## BitConfig

[[autodoc]] BitConfig

## BitImageProcessor

[[autodoc]] BitImageProcessor
    - preprocess

## BitImageProcessorFast

[[autodoc]] BitImageProcessorFast
    - preprocess

## BitModel

[[autodoc]] BitModel
    - forward

## BitForImageClassification

[[autodoc]] BitForImageClassification
    - forward

