---
title: regnet-y-080
canonical_url: "https://www.modelscope.cn/models/facebook/regnet-y-080"
md_url: "https://www.modelscope.cn/models/facebook/regnet-y-080.md"
repository: facebook/regnet-y-080
last_updated: 2025-05-20
license: apache-2.0
pipeline_tag: image-classification
tasks:
  - image-classification
model_type:
  - regnet
architectures:
  - RegNetForImageClassification
library_name:
  - pytorch
  - transformer
  - tensorflow
frameworks:
  - pytorch
downloads: 351
stars: 2
tags:
  - vision
  - image-classification
---

# regnet-y-080

> regnet-y-080 - facebook 在 ModelScope 开源的模型。RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository.

facebook/regnet-y-080 是 ModelScope 魔搭社区上的image-classification模型，采用 apache-2.0 许可。

- **Repository**: facebook/regnet-y-080
- **License**: apache-2.0
- **Tasks**: image-classification
- **Tags**: vision, image-classification
- **Downloads**: 351
- **Stars**: 2
- **Last updated**: 2025-05-20

Source: https://www.modelscope.cn/models/facebook/regnet-y-080

---

# RegNet

RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycls). 

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.

## 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.

![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png)

## Intended uses & limitations

You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for
fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model:

```python
>>> 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](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).