Instructions to use timm/dpn68b.mx_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/dpn68b.mx_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/dpn68b.mx_in1k", pretrained=True) - Transformers
How to use timm/dpn68b.mx_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/dpn68b.mx_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/dpn68b.mx_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e5386287407f8d1c865c0a00378546d6bfdd0d384f8d19092c3438f8080db2d
- Size of remote file:
- 50.9 MB
- SHA256:
- 33ccba7685c7fc0189d5333b595325554c7d5777f48eb4b4f9cf7b622c1d34d3
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