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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb5a941c14bb087b4f02d1c793c48cec99f493a58ab18ca815d2017a3ade39a4
- Size of remote file:
- 343 MB
- SHA256:
- f5fae8ec04fcbb8a070fc74b3a6cc397f6e1a096dc6e9e7fd8b989a238f0010d
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