Instructions to use dima806/headgear_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/headgear_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/headgear_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dima806/headgear_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/headgear_image_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dima806/headgear_image_detection: direct link, hf CLI and curl.
- Browser
- Download file 4.41 kB
-
https://huggingface.co/dima806/headgear_image_detection/resolve/main/training_args.bin
- Command line
-
hf download hf://dima806/headgear_image_detection/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dima806/headgear_image_detection/resolve/main/training_args.bin
4.41 kB
- Xet hash:
- fbb89371e99b5a58368c5b18818782df640eff85e28e0379195812ca80705b31
- Size of remote file:
- 4.41 kB
- SHA256:
- b3e612d1008a54c9ca5c1f8c33661259da2744e2c13cf1588e29967c73cd5c28
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