Instructions to use hf-internal-testing/tiny-random-SiglipForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-SiglipForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-SiglipForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 038f66a869f0243a3a7e1b565992b78706f79ba3c6e87b489f41a32ae7b6e557
- Size of remote file:
- 118 kB
- SHA256:
- 656d9170acbf690e2ae4ebccb6e3e2d16799f12620bfd6e08d482d4e38f93176
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.