Instructions to use jhoppanne/Emotion-Image-Classification-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhoppanne/Emotion-Image-Classification-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jhoppanne/Emotion-Image-Classification-V2") 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("jhoppanne/Emotion-Image-Classification-V2") model = AutoModelForImageClassification.from_pretrained("jhoppanne/Emotion-Image-Classification-V2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7abe4fe6ed6b47fcf898c528b5fbe6e7b72d7b3d98b770877070678fb8b4cbd3
- Size of remote file:
- 4.73 kB
- SHA256:
- 81eeed6b222f771458980cd9c5fa96110e61f566ceca489ec7b2f6fe2512dd87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.