Upload README.md

README.md

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+---
+license: apache-2.0
+tags:
+- vision
+- image-classification
+
+datasets:
+- imagenet-1k
+
+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
+
+---
+
+# RegNetModel
+
+RegNetModel model trained on imagenet-1k. It was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER). 
+
+Disclaimer: The team releasing RegNetModel 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 trained [RegNets](https://huggingface.co/?models=regnet) models in a self-supervised fashion on bilion of random images from the internet. This model is later finetuned on ImageNet
+
+![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, RegNetModel
+>>> 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 = RegNetModel.from_pretrained("zuppif/regnet-y-040")
+
+>>> inputs = feature_extractor(image, return_tensors="pt")
+
+>>> with torch.no_grad():
+...     outputs = model(**inputs)
+
+>>> last_hidden_states = outputs.last_hidden_state
+>>> list(last_hidden_states.shape)
+[1, 1088, 7, 7]
+```
+
+
+
+For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).