Instructions to use howdyaendra/xblock-base-patch1-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use howdyaendra/xblock-base-patch1-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="howdyaendra/xblock-base-patch1-224") 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("howdyaendra/xblock-base-patch1-224") model = AutoModelForImageClassification.from_pretrained("howdyaendra/xblock-base-patch1-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from howdyaendra/xblock-base-patch1-224: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/howdyaendra/xblock-base-patch1-224/resolve/main/training_args.bin
- Command line
-
hf download hf://howdyaendra/xblock-base-patch1-224/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/howdyaendra/xblock-base-patch1-224/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 2baabc277ca821a0db8703f6a2ed302f1529bc0c256136207c6312e0f37de219
- Size of remote file:
- 4.92 kB
- SHA256:
- be321081e73ffc6cae63799360a414dee56067b5572ebec02c8d8764fa8bed69
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