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:
- e2d6a22d12976a03db38f7a71576e7f26fbac9d20bcee6ce98ab2994975c5314
- Size of remote file:
- 343 MB
- SHA256:
- 9ec42a17c558617d25a8036a999b4db40a4381e85fe7594ea7ec4d4685779728
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