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 training_args.bin from dima806/facial_emotions_image_detection: direct link, hf CLI and curl.
- Browser
- Download file 4.41 kB
-
https://huggingface.co/dima806/facial_emotions_image_detection/resolve/main/training_args.bin
- Command line
-
hf download hf://dima806/facial_emotions_image_detection/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dima806/facial_emotions_image_detection/resolve/main/training_args.bin
4.41 kB
- Xet hash:
- d963d8e94976e6c31af1c708b79a392d72d43f94157de9f85c1e9e9a2f00956c
- Size of remote file:
- 4.41 kB
- SHA256:
- 40b9d6436acc2bf7357bd6544c1b5ac091ba87da7d2f748d85a8e993b83d4108
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