# RegNet

RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.

Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team.

## Model description

The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.

## Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model:

>>> from transformers import AutoFeatureExtractor, RegNetForImageClassification
>>> import torch

>>> image = dataset["test"]["image"][0]

>>> inputs = feature_extractor(image, return_tensors="pt")

...     logits = model(**inputs).logits

>>> # model predicts one of the 1000 ImageNet classes
>>> predicted_label = logits.argmax(-1).item()
>>> print(model.config.id2label[predicted_label])
'tabby, tabby cat'


For more code examples, we refer to the documentation.