Upload README.md

README.md

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+---
+license: apache-2.0
+tags:
+- vision
+- image-segmentation
+
+datasets:
+- imagenet-21k
+- imagenet-1k
+
+widget:
+- src:https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
+  widget.title
+
+---
+
+# ConvNext
+
+ConvNext model trained on imagenet-21k. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) and first released in [this repository](https://github.com/facebookresearch/ConvNeXt). 
+
+Disclaimer: The team releasing ConvNext did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+weiiii
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.png)
+
+## Intended uses & limitations
+
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) 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 ConvNextFeatureExtractor, ConvNextForImageClassification
+>>> import torch
+>>> from datasets import load_dataset
+
+>>> dataset = load_dataset("huggingface/cats-image")
+>>> image = dataset["test"]["image"][0]
+
+>>> feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224")
+>>> model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")
+
+>>> 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/convnext).