Instructions to use suous/recnext_m4.dist_300e_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use suous/recnext_m4.dist_300e_in1k with timm:
import timm model = timm.create_model("hf_hub:suous/recnext_m4.dist_300e_in1k", pretrained=True) - Transformers
How to use suous/recnext_m4.dist_300e_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="suous/recnext_m4.dist_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_m4.dist_300e_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4658dbf88266831d63c6c5d20632f218d93aa1cc889b3bbe077e59c60c8d822
- Size of remote file:
- 59.2 MB
- SHA256:
- 68b32fa60201f3a1de1059945097b3aa87da128fc96b13f77ff36f85fb7d9659
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