Instructions to use timm/mobileone_s4.apple_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/mobileone_s4.apple_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/mobileone_s4.apple_in1k", pretrained=True) - Transformers
How to use timm/mobileone_s4.apple_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/mobileone_s4.apple_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/mobileone_s4.apple_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 781eed6bcab87b823a8096deec1b79560d58e2cbfd4cfa195ef6c4d350c2ccfe
- Size of remote file:
- 60.5 MB
- SHA256:
- 652a422d59fae9fbb2c38e02acef181e10148d2e0320fa2726fc339fa1baaf05
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.