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:
- 9070081aef10b9c5f87b8ff80935ab98d74fb921b29f3736400ef674613838d7
- Size of remote file:
- 94.4 MB
- SHA256:
- cf9f2c41c20b8a2f6d30af6bdf81a7cf00430bb892ced058452e93d3ec3226cd
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