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:
- f6ed00cd0b50dede9d9b8eead53ca12f1660e8fa425d9b472e6f62769923e8e5
- Size of remote file:
- 627 Bytes
- SHA256:
- b75d4d0a3fe298d42986f8b7b3dcdab787bd1494f4b8d84b3debf406572aab7b
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