Instructions to use vms57464/Dogz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vms57464/Dogz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="vms57464/Dogz") 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("vms57464/Dogz") model = AutoModelForImageClassification.from_pretrained("vms57464/Dogz", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vms57464/Dogz: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/vms57464/Dogz/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vms57464/Dogz/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vms57464/Dogz/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- c4a86eadde2db491f76c0f06322740a26e6d670e29901e537b7578728a69f895
- Size of remote file:
- 343 MB
- SHA256:
- f88d4fe5e26b6fc430e021c430e166a6216c2e0bd9d5bab0742161645a18c338
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