Instructions to use omzn/facemark_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omzn/facemark_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="omzn/facemark_detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("omzn/facemark_detection") model = AutoModelForSequenceClassification.from_pretrained("omzn/facemark_detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from omzn/facemark_detection: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/omzn/facemark_detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://omzn/facemark_detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/omzn/facemark_detection/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 8023dbac2c6321b637f88955641948f47e978f846d4aaa9b056bdb5a1534192d
- Size of remote file:
- 443 MB
- SHA256:
- bd8770d1711382557ee6b51ce982cba1e326cceae1996a6c58dff65322ec8d98
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