Instructions to use dima806/smoker_image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/smoker_image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/smoker_image_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dima806/smoker_image_classification") model = AutoModelForImageClassification.from_pretrained("dima806/smoker_image_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea451a9bf28acf4b64f542e1635f2641dbd10e9be3b1aec590937afcd178e106
- Size of remote file:
- 687 MB
- SHA256:
- d217dd91c4f53651aaad19224f5c87285dd96258ca376834da38baf3b121ae3c
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