regnet-y-640-seer-in1k

RegNet

RegNet model trained on imagenet-1k. It was introduced in the paper Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision 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 trained RegNets models in a self-supervised fashion on bilion of random images from the internet. This model is later finetuned on ImageNet

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
>>> from datasets import load_dataset

>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]

>>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("zuppif/regnet-y-040")

>>> 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])
'tabby, tabby cat'

For more code examples, we refer to the documentation.