Instructions to use timm/regnetz_c16_evos.ch_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/regnetz_c16_evos.ch_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/regnetz_c16_evos.ch_in1k", pretrained=True) - Transformers
How to use timm/regnetz_c16_evos.ch_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/regnetz_c16_evos.ch_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/regnetz_c16_evos.ch_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 035261166a69c03fd3e30e9e8ec582eca7039867be8e3120b4df2ed6ae8cb595
- Size of remote file:
- 54.1 MB
- SHA256:
- c6e639538eb16419527e653475815eaaa1482cf9eb2a4ab3a19f7f324c47d910
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