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:
- 132cee75ce8206ff5bd74a9e8f32ea8014e0e9936ef2f7e083328e3e6cf459e2
- Size of remote file:
- 94.4 MB
- SHA256:
- 41eaae7eb7e24bd7519962bfb436aaf152430fd684a773a5df1561f072e9c1dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.