Instructions to use sgenzer/seven-segment-led with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgenzer/seven-segment-led with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sgenzer/seven-segment-led") 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("sgenzer/seven-segment-led") model = AutoModelForImageClassification.from_pretrained("sgenzer/seven-segment-led", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sgenzer/seven-segment-led: direct link, hf CLI and curl.
- Browser
- Download file 110 MB
-
https://huggingface.co/sgenzer/seven-segment-led/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sgenzer/seven-segment-led/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sgenzer/seven-segment-led/resolve/main/pytorch_model.bin
110 MB
- Xet hash:
- a54f28adc4cf4479811ac0a46bfeaed3f7c02b3e10df76f1368a7075575da2a4
- Size of remote file:
- 110 MB
- SHA256:
- fb037c413c04c25752118ccbe82826846061bab8918d593749a9aa5b892a2095
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