Instructions to use Veritone/siglip2-base-patch16-512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veritone/siglip2-base-patch16-512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Veritone/siglip2-base-patch16-512") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Veritone/siglip2-base-patch16-512") model = AutoModelForZeroShotImageClassification.from_pretrained("Veritone/siglip2-base-patch16-512", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 375 Bytes
e00aa5a 159e929 e00aa5a 159e929 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"initializer_factor": 1.0,
"model_type": "siglip",
"text_config": {
"model_type": "siglip_text_model",
"vocab_size": 256000,
"pad_token_id": 0
},
"transformers_version": "4.49.0.dev0",
"vision_config": {
"image_size": 512,
"model_type": "siglip_vision_model"
},
"architectures": [
"SiglipModel"
],
"max_position_embeddings": 64
} |