uoft-cs/cifar10
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How to use ahsanjavid/convnext-tiny-finetuned-cifar10 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="ahsanjavid/convnext-tiny-finetuned-cifar10")
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("ahsanjavid/convnext-tiny-finetuned-cifar10")
model = AutoModelForImageClassification.from_pretrained("ahsanjavid/convnext-tiny-finetuned-cifar10")ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper A ConvNet for the 2020s by Liu et al. and first released in this repository. Convnext tiny finetuned on cifar 10 dataset. Which has ten classes.
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.