Regnet Y 320 Seer

🚀 RegNetModel

The RegNetModel is a vision model introduced in the paper Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision, which offers potential for image - related tasks.

🚀 Quick Start

The RegNetModel model 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 RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team.

✨ Features

The authors trained RegNets models in a self - supervised fashion on billions of random images from the internet.

📚 Documentation

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:

Basic Usage

>>> from transformers import AutoFeatureExtractor, RegNetModel
>>> 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 = RegNetModel.from_pretrained("zuppif/regnet-y-040")

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

>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 1088, 7, 7]

For more code examples, we refer to the documentation.

📄 License

This model is released under the Apache 2.0 license.