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:
- d3673232f8697bf954c3138d27c011ced4e764dafd3b53a5db22e13fbd9c22b4
- Size of remote file:
- 687 MB
- SHA256:
- aa6827aa2d76e16b7326df7bcaf12af1ae719a8f7afb8a92769fc43faad4f806
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