Regnet X 016

🚀 RegNet

RegNet is a model trained on ImageNet-1K. It addresses the challenge of efficient neural architecture design by introducing a systematic approach to search space design, offering a powerful tool for image classification tasks.

🚀 Quick Start

RegNet is a 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.

✨ Features

• Neural Architecture Search (NAS): The authors design search spaces to perform NAS. They start from a high - dimensional search space and iteratively reduce it by empirically applying constraints based on the best - performing models sampled by the current search space.
• Visual Representation: A visual representation of the model architecture is available as shown in the image below.

📚 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:

>>> 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.

📄 License

This model is licensed under the Apache - 2.0 license.