Instructions to use suous/recnext_m3.base_300e_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use suous/recnext_m3.base_300e_in1k with timm:
import timm model = timm.create_model("hf_hub:suous/recnext_m3.base_300e_in1k", pretrained=True) - Transformers
How to use suous/recnext_m3.base_300e_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="suous/recnext_m3.base_300e_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("suous/recnext_m3.base_300e_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7117d199a11f45ccdf9b775eee46f872de452406eb5edb935617dc9a3401f10
- Size of remote file:
- 35.4 MB
- SHA256:
- 6406c9a636bf0eec88ba9bd0116c4af9fe3de32f0615160dd912c90cf0aa39f2
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