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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c72ca6fcd80543c314ed20bdecbc6577a71cc5e95b8df256f5d0d77a33bdb1a
- Size of remote file:
- 94.4 MB
- SHA256:
- b4f933aaf2c595f012e189849461bf2afa31349cc184bcd6f4b43bd0428a6ebb
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