Instructions to use timm/mobileone_s2.apple_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/mobileone_s2.apple_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/mobileone_s2.apple_in1k", pretrained=True) - Transformers
How to use timm/mobileone_s2.apple_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/mobileone_s2.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_s2.apple_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2862f5f8bf291920c024a8032044126dd6daad980b301bbc550683adbac2b64b
- Size of remote file:
- 32.1 MB
- SHA256:
- 3174991d08b511b250f789556014d81cc5bf6e9d63c0ea9684f78a0d766d201d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.