Update README.md

README.md

@@ -16,7 +16,7 @@ widget:
 
 # ConvNeXt V2 (base-sized model) 
 
-ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K 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). 
+ConvNeXt V2 model pretrained using the FCMAE framework and fine-tuned on the ImageNet-22K dataset at resolution 384x384. 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.
 
@@ -43,8 +43,8 @@ from datasets import load_dataset
 dataset = load_dataset("huggingface/cats-image")
 image = dataset["test"]["image"][0]
 
-preprocessor = AutoImageProcessor.from_pretrained("facebook/convnextv2-base-22k-224")
-model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-base-22k-224")
+preprocessor = AutoImageProcessor.from_pretrained("facebook/convnextv2-base-22k-384")
+model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-base-22k-384")
 
 inputs = preprocessor(image, return_tensors="pt")