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.
The authors trained RegNets models in a self-supervised fashion on bilion of random images from the internet
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.
Here is how to use this model:
from transformers import AutoFeatureExtractor, RegNetModel import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"] 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.
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