|
--- |
|
license: apache-2.0 |
|
tags: |
|
- vision |
|
- image-classification |
|
|
|
datasets: |
|
- imagenet1k |
|
|
|
widget: |
|
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg |
|
example_title: Tiger |
|
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg |
|
example_title: Teapot |
|
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg |
|
example_title: Palace |
|
|
|
--- |
|
|
|
## RegNetY 10B |
|
|
|
This gigantic model is a scale up [RegNetY](https://arxiv.org/abs/2003.13678) model trained on one bilion random images ad later finetuned on imagenet. |
|
|
|
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. |
|
|
|
## 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("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' |
|
``` |
|
|
|
|
|
|
|
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet). |
