ConvNeXt (ImageNet-21k)
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How to use cs-giung/convnext-v1-tiny-imagenet21k with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="cs-giung/convnext-v1-tiny-imagenet21k")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png") # Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("cs-giung/convnext-v1-tiny-imagenet21k")
model = AutoModelForImageClassification.from_pretrained("cs-giung/convnext-v1-tiny-imagenet21k")ConvNext model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s.
The weights were converted from the convnext_tiny_22k_224.pth file presented in the official repository.