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:
- bf0b2a8565af3092afa1e9c663bb359e3db20228f7ae35336ce90a36af6c0633
- Size of remote file:
- 4.03 kB
- SHA256:
- 92e06fa716d8e7e04763bde8696adac4facc860724f8218deb6ebfca518933f0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.