Instructions to use timm/convnext_small.fb_in22k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/convnext_small.fb_in22k with timm:
import timm model = timm.create_model("hf_hub:timm/convnext_small.fb_in22k", pretrained=True) - Transformers
How to use timm/convnext_small.fb_in22k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/convnext_small.fb_in22k") 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_small.fb_in22k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0a6018fac4b82db936a78659e6979b63205e4289972cb89b8d2fee4254c660a
- Size of remote file:
- 265 MB
- SHA256:
- 11b2027c5b6f0b94b98a0e67b6d8bf9a4d73f0079921db7e1fe50b849780e844
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.