Instructions to use rakib730/output-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rakib730/output-models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rakib730/output-models") 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("rakib730/output-models") model = AutoModelForImageClassification.from_pretrained("rakib730/output-models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67f5ceb741bd3f17bd5126a352ba81045894d5a0b84eee6d4ccc360dd448ed5a
- Size of remote file:
- 5.24 kB
- SHA256:
- 0f43a08013968c1e3f06b80b34ad528847e9aa2009c2054ff5bf285aec2a586d
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