Instructions to use amaye15/google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amaye15/google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amaye15/google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("amaye15/google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs") model = AutoModelForImageClassification.from_pretrained("amaye15/google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from amaye15/google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/amaye15/google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs/resolve/main/training_args.bin
- Command line
-
hf download hf://amaye15/google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/amaye15/google-siglip-base-patch16-224-batch64-lr5e-05-standford-dogs/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 953b1ec6c129a400bd1e6d2d32d8ab261f664744e155725bea0a2241ff8f63b1
- Size of remote file:
- 5.11 kB
- SHA256:
- b1e2adea2ad13837e172adc6cc3a084bf72cd54a57bc6b1cc0cd252cea258e77
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