Instructions to use timm/levit_conv_128.fb_dist_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/levit_conv_128.fb_dist_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/levit_conv_128.fb_dist_in1k", pretrained=True) - Transformers
How to use timm/levit_conv_128.fb_dist_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/levit_conv_128.fb_dist_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/levit_conv_128.fb_dist_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d4d6d0e50d0bd0f2b3dbc6f4ab5514d9df7448bc45f30d0204721873e96d176
- Size of remote file:
- 37.2 MB
- SHA256:
- 4f911a1b4f4a90be0737bbe730d2ca6ddde5db03dd126404df4fa31ed4925456
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