Instructions to use emanuelebezzecchi/sceneShotModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emanuelebezzecchi/sceneShotModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="emanuelebezzecchi/sceneShotModel") 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("emanuelebezzecchi/sceneShotModel") model = AutoModelForImageClassification.from_pretrained("emanuelebezzecchi/sceneShotModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from emanuelebezzecchi/sceneShotModel: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/emanuelebezzecchi/sceneShotModel/resolve/main/training_args.bin
- Command line
-
hf download hf://emanuelebezzecchi/sceneShotModel/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/emanuelebezzecchi/sceneShotModel/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 8e113e2afec1212dbdd48a547cde502db2bb09e43eeb2514ddee2b688c5527dc
- Size of remote file:
- 3.96 kB
- SHA256:
- 79683c7119ee8d094d4c61d8742a1ec832e953db74b816ac979142bc9671cb25
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