Image Classification
Transformers
Safetensors
PyTorch
English
beit
vision
emotion-recognition
student-engagement
education
Instructions to use nihar245/Expression-Detection-BEIT-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nihar245/Expression-Detection-BEIT-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nihar245/Expression-Detection-BEIT-Large") 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("nihar245/Expression-Detection-BEIT-Large") model = AutoModelForImageClassification.from_pretrained("nihar245/Expression-Detection-BEIT-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c67d2c8777168900452aa46764ca8e215795f2e88001c9a4b99baf172384b7b
- Size of remote file:
- 5.78 kB
- SHA256:
- 6d4c2c6a2aa624806acbe03753fb225bdcb05599bc4e1c14a0e2ab84f6c5efc7
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