# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("vesteinn/resnet50-prime")
model = AutoModelForImageClassification.from_pretrained("vesteinn/resnet50-prime", device_map="auto")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
This is HF compatible checkpoint converted from the one made available at https://github.com/amodas/PRIME-augmentations
It's the ImageNet-1k Prime+ one.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vesteinn/resnet50-prime") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")