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")# pip install -U transformers accelerate # 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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dima806/facial_emotions_image_detection: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/dima806/facial_emotions_image_detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dima806/facial_emotions_image_detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dima806/facial_emotions_image_detection/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 19560f9be82d00f699b2a5b5d56f69a5ceff6b68dfc2b524c4c43ad18697f997
- Size of remote file:
- 343 MB
- SHA256:
- fcebce67a9ad54f8778a0274d31f904af73025f3eaaf8301f47920f7db29a567
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