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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).