Instructions to use juniorjukeko/emotion-classificationV3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juniorjukeko/emotion-classificationV3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="juniorjukeko/emotion-classificationV3") 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("juniorjukeko/emotion-classificationV3") model = AutoModelForImageClassification.from_pretrained("juniorjukeko/emotion-classificationV3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from juniorjukeko/emotion-classificationV3: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/juniorjukeko/emotion-classificationV3/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://juniorjukeko/emotion-classificationV3@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/juniorjukeko/emotion-classificationV3/resolve/refs%2Fpr%2F1/pytorch_model.bin
343 MB
- Xet hash:
- 8213be5bff8980bf2de99626898a32bb4ee1cc75b73bce69265c17ed0f18d4f0
- Size of remote file:
- 343 MB
- SHA256:
- 04f30b7dc66522f30e1d186ef4c8c4c2302b93983f2ce0f635a58239594fb422
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