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:
- b6d2cd38b7af323c59a7f9bde4b756cfd63759e7ccac0cb0d06579f4c03bbc19
- Size of remote file:
- 94.3 MB
- SHA256:
- e2590979920268492e73b42ccf74d4fdbb8b6530f8bca9c83ccc2ad647f75cd7
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