Create README.md

README.md

@@ -0,0 +1,84 @@
+---
+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
+---
+
+# ConvNeXt V2 (base-sized model) 
+
+ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-1K dataset at resolution 224x224. It was introduced in the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Woo et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt-V2). 
+
+Disclaimer: The team releasing ConvNeXT V2 did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+ConvNeXt V2 is a pure convolutional model (ConvNet) that introduces a fully convolutional masked autoencoder framework (FCMAE) and a new Global Response Normalization (GRN) layer to ConvNeXt. ConvNeXt V2 significantly improves the performance of pure ConvNets on various recognition benchmarks.
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnextv2_architecture.png)
+
+## Intended uses & limitations
+
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnextv2) 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 AutoImageProcessor, ConvNextV2ForImageClassification
+import torch
+from datasets import load_dataset
+
+dataset = load_dataset("huggingface/cats-image")
+image = dataset["test"]["image"][0]
+
+preprocessor = AutoImageProcessor.from_pretrained("facebook/convnextv2-base-1k-224")
+model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-base-1k-224")
+
+inputs = preprocessor(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/convnextv2).
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2301-00808,
+  author    = {Sanghyun Woo and
+               Shoubhik Debnath and
+               Ronghang Hu and
+               Xinlei Chen and
+               Zhuang Liu and
+               In So Kweon and
+               Saining Xie},
+  title     = {ConvNeXt {V2:} Co-designing and Scaling ConvNets with Masked Autoencoders},
+  journal   = {CoRR},
+  volume    = {abs/2301.00808},
+  year      = {2023},
+  url       = {https://doi.org/10.48550/arXiv.2301.00808},
+  doi       = {10.48550/arXiv.2301.00808},
+  eprinttype = {arXiv},
+  eprint    = {2301.00808},
+  timestamp = {Tue, 10 Jan 2023 15:10:12 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2301-00808.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```