Regnet Y 040

🚀 RegNet

RegNet is a model trained on ImageNet-1K, which can be used for image classification tasks, providing a powerful solution for computer vision.

🚀 Quick Start

RegNet is a model trained on the ImageNet-1K dataset. 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

• Designed Search Spaces: The authors designed search spaces for Neural Architecture Search (NAS). They started from a high-dimensional search space and iteratively reduced it by empirically applying constraints based on the best-performing models sampled by the current search space.
• Image Classification: The raw model can be used for image classification. You can also find fine-tuned versions on the model hub for tasks that interest you.

📦 Installation

The installation steps are not provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

>>> 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("facebook/regnet-y-040")
>>> model = RegNetForImageClassification.from_pretrained("facebook/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'

Advanced Usage

For more code examples, we refer to the documentation.

📚 Documentation

Model description

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.

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.

🔧 Technical Details

The technical details are not provided in the original document, so this section is skipped.

📄 License

This model is licensed under the Apache-2.0 license.

Information Table