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:
- 5755ec3a087e5991da1eaf8955101469fc5cccc81264390f43943c68327b736e
- Size of remote file:
- 4.03 kB
- SHA256:
- 6900b4c24c25d0bc5d22728cf5a967b6dd232fa7b38b4b70994a710e4d4af246
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.