Instructions to use dima806/pets_facial_expression_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/pets_facial_expression_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/pets_facial_expression_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/pets_facial_expression_detection") model = AutoModelForImageClassification.from_pretrained("dima806/pets_facial_expression_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-486/optimizer.pt from dima806/pets_facial_expression_detection: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/dima806/pets_facial_expression_detection/resolve/main/checkpoint-486/optimizer.pt
- Command line
-
hf download hf://dima806/pets_facial_expression_detection/checkpoint-486/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dima806/pets_facial_expression_detection/resolve/main/checkpoint-486/optimizer.pt
687 MB
- Xet hash:
- d691d3b28bf819d755be9c7789bc60798f2d1266874456301a44dad640c50945
- Size of remote file:
- 687 MB
- SHA256:
- 087b29a943fa8b6d7fa556dc9db5ea5785f303582c5441199c14bd6d59b66434
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