How to use timm/xception65.ra3_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/xception65.ra3_in1k", pretrained=True)
How to use timm/xception65.ra3_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/xception65.ra3_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/xception65.ra3_in1k", device_map="auto")
