Instructions to use ycbq999/facial_emotions_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ycbq999/facial_emotions_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ycbq999/facial_emotions_image_detection") 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("ycbq999/facial_emotions_image_detection") model = AutoModelForImageClassification.from_pretrained("ycbq999/facial_emotions_image_detection") - Notebooks
- Google Colab
- Kaggle
Model Card for Model Face Expression Detection
This model is used to detect face expressions. You need to upload a clear face picture and the model generate the possiblities of the expression. happy , angry , fear or others
Model Details
Model Description
- Developed by: Dmytro Iakubovskyi
- Remade by: Song Chen
- Model type: 'google/vit-base-patch16-224-in21k'
- Finetuned from model [optional]: 'google/vit-base-patch16-224-in21k'
Model Sources [optional]
- Paper [optional]: ViTFER: Facial Emotion Recognition with Vision Transformers
Uses
Direct Use
This model intend to be part of web application development using flask. The original model complete training is from kaggle.
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