Instructions to use dima806/facial_emotions_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/facial_emotions_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/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("dima806/facial_emotions_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/facial_emotions_image_detection") - Inference
- Notebooks
- Google Colab
- Kaggle
Request: DOI
#1
by ohayonguy - opened
I would like to cite this model in my paper.
Hi @ohayonguy , the model is freely distributed according to Apache 2.0 license, so you can cite it accordingly.
For example, you can add to your paper:
https://huggingface.co/dima806/facial_emotions_image_detection
Copyright 2023 Dmytro Iakubovskyi dima806@gmail.com
@ohayonguy not really, it is based on my Kaggle notebook https://www.kaggle.com/code/dima806/facial-emotions-image-detection-vit (again, distributed according to Apache 2.0).
dima806 changed discussion status to closed