Instructions to use timm/convnext_pico.d1_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_pico.d1_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/convnext_pico.d1_in1k", pretrained=True) - Transformers
How to use timm/convnext_pico.d1_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnext_pico.d1_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/convnext_pico.d1_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a72731a7ece73147abc03dacca6cc694cd6d1c3be4555b6aaf56f4630dec610a
- Size of remote file:
- 36.2 MB
- SHA256:
- e8fc5f3c787a92bcf9577cf24117614cffc7c5f1aa2f0886314200662ff76006
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