Instructions to use hf-internal-testing/tiny-random-SiglipVisionModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipVisionModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-SiglipVisionModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SiglipVisionModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SiglipVisionModel") - Notebooks
- Google Colab
- Kaggle
File size: 334 Bytes
ea28f9a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"crop_size": 30,
"do_convert_rgb": null,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "SiglipImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": 30
}
|