Instructions to use black/simple_kitchen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use black/simple_kitchen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="black/simple_kitchen") 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("black/simple_kitchen") model = AutoModelForImageClassification.from_pretrained("black/simple_kitchen") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e04860645f97a66b6d4a60447b15f6cd790d33a6471a8d879ee9549d4db39f74
- Size of remote file:
- 343 MB
- SHA256:
- fe9a544668450a3b4ad27d61ee65d8eeb3b8981f3064f23661b3fc9c3d3f646e
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