How to use wrice/perch-v2-efficientnet-b3 with timm:
import timm model = timm.create_model("hf_hub:wrice/perch-v2-efficientnet-b3", pretrained=True)
How to use wrice/perch-v2-efficientnet-b3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="wrice/perch-v2-efficientnet-b3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wrice/perch-v2-efficientnet-b3", dtype="auto")
Google Perch v2 bird vocalization classifier backbone converted from TF/JAX to PyTorch timm format. EfficientNet-B3 with 1536-dim embeddings, pretrained on 14,795 species from Xeno-Canto/iNaturalist.
