Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use sabrilben/emotion_recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sabrilben/emotion_recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sabrilben/emotion_recognition") 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("sabrilben/emotion_recognition") model = AutoModelForImageClassification.from_pretrained("sabrilben/emotion_recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e60ec531b4f6fb2c9747eb18731af9051b621e427e8cd911c14bd0a01801932
- Size of remote file:
- 5.37 kB
- SHA256:
- 1e1c4566987dd9158f91ad49190afee8ec0a4666c31dd1743b074627df058da5
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