Instructions to use kittendev/visual_emotional_analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kittendev/visual_emotional_analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kittendev/visual_emotional_analysis") 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("kittendev/visual_emotional_analysis") model = AutoModelForImageClassification.from_pretrained("kittendev/visual_emotional_analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kittendev/visual_emotional_analysis: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/kittendev/visual_emotional_analysis/resolve/main/training_args.bin
- Command line
-
hf download hf://kittendev/visual_emotional_analysis/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kittendev/visual_emotional_analysis/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 37e39a59b27c9371641dc686ec9744e26d0e2fd509593c13266857efaa0a85df
- Size of remote file:
- 4.09 kB
- SHA256:
- 113e26f3f23439cf20c4ffbb8f80abe204915458fe397dcb52c2abd46c947dca
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