Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use Dricz/emotion_recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dricz/emotion_recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Dricz/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("Dricz/emotion_recognition") model = AutoModelForImageClassification.from_pretrained("Dricz/emotion_recognition") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- Feb04_09-56-10_aa74d5f9a432
- Feb04_10-02-52_aa74d5f9a432
- Feb04_10-07-04_aa74d5f9a432
- Feb04_10-18-25_aa74d5f9a432
- Feb04_10-27-52_aa74d5f9a432
- Feb04_10-32-57_aa74d5f9a432
- Feb04_10-43-27_aa74d5f9a432
- Feb04_10-46-33_aa74d5f9a432
- Feb04_10-53-01_aa74d5f9a432
- Feb11_08-09-56_de88390d64dd
- Feb11_08-13-04_de88390d64dd