Create README.md

README.md

@@ -0,0 +1,83 @@
+---
+license: apache-2.0
+tags:
+- vision
+- image-classification
+datasets:
+- imagenet-21k
+- 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
+---
+
+# ConvNeXT (large-sized model) 
+
+ConvNeXT model pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. 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
+
+ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.
+
+![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 to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
+
+```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-large-384-22k-1k")
+model = ConvNextForImageClassification.from_pretrained("facebook/convnext-large-384-22k-1k")
+
+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]),
+```
+
+For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext).
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2201-03545,
+  author    = {Zhuang Liu and
+               Hanzi Mao and
+               Chao{-}Yuan Wu and
+               Christoph Feichtenhofer and
+               Trevor Darrell and
+               Saining Xie},
+  title     = {A ConvNet for the 2020s},
+  journal   = {CoRR},
+  volume    = {abs/2201.03545},
+  year      = {2022},
+  url       = {https://arxiv.org/abs/2201.03545},
+  eprinttype = {arXiv},
+  eprint    = {2201.03545},
+  timestamp = {Thu, 20 Jan 2022 14:21:35 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```