Instructions to use PriyamSheta/EmotionClassModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PriyamSheta/EmotionClassModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="PriyamSheta/EmotionClassModel") 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("PriyamSheta/EmotionClassModel") model = AutoModelForImageClassification.from_pretrained("PriyamSheta/EmotionClassModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5934ee1859713881c7cf70f9c60446f4a7abfca6d186b9041b269d89611a38d
- Size of remote file:
- 3.52 kB
- SHA256:
- 2bee3d0cb7570fb86caebf6ec4971a98eb9f6acd25352a2218d7f1761dd6ed69
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