Instructions to use mikga/pattern1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mikga/pattern1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mikga/pattern1") 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/pattern1") model = AutoModelForImageClassification.from_pretrained("mikga/pattern1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13b419dbeed909ff809b11164d80e94b258a8b0e4afe7fc85d07081131306fc0
- Size of remote file:
- 94.4 MB
- SHA256:
- c8f6db5307c1404e3ab76987f0987741232af089b20cf286373a05bd7394a902
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