Instructions to use Kx15/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kx15/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kx15/emotion_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Kx15/emotion_classification") model = AutoModelForImageClassification.from_pretrained("Kx15/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Kx15/emotion_classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Kx15/emotion_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Kx15/emotion_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Kx15/emotion_classification/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 20a970852a9802ede3623a86e079103a8095aa70a8cfb2b297c5112756ba472a
- Size of remote file:
- 343 MB
- SHA256:
- a2c5a5c085af6ee1bd07b11025fe0e9816ffb65ba4abd570e7eaa2f0f4068f1a
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