Instructions to use timm/mobilevitv2_200.cvnets_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/mobilevitv2_200.cvnets_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/mobilevitv2_200.cvnets_in1k", pretrained=True) - Transformers
How to use timm/mobilevitv2_200.cvnets_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/mobilevitv2_200.cvnets_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("timm/mobilevitv2_200.cvnets_in1k") model = AutoModelForImageClassification.from_pretrained("timm/mobilevitv2_200.cvnets_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/mobilevitv2_200.cvnets_in1k: direct link, hf CLI and curl.
- Browser
- Download file 74 MB
-
https://huggingface.co/timm/mobilevitv2_200.cvnets_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/mobilevitv2_200.cvnets_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/mobilevitv2_200.cvnets_in1k/resolve/main/pytorch_model.bin
74 MB
- Xet hash:
- 71eedef62499acd101fec4fd8516ea50a1ddfd4a4221672e97ef03ffb0289f9d
- Size of remote file:
- 74 MB
- SHA256:
- c2c0e1340c9f050ef88946e1ad64d7726e48c52f0d872f36450a6fff60203fa8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.