Instructions to use mikga/pattern2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikga/pattern2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mikga/pattern2") 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("mikga/pattern2") model = AutoModelForImageClassification.from_pretrained("mikga/pattern2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c85d3dfdb40021ea532099c18fedec4ba4627bd7a3cb4053107e6aff818ef5a2
- Size of remote file:
- 94.4 MB
- SHA256:
- b4db640acbdb52333b2e070826cda57824e96cbb636d79abf11a085efdba4100
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