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.
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.
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, RegNetForImageClassification 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 = 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.
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