Zero-Shot Image Classification
Transformers
Safetensors
siglip2_vision_model
feature-extraction
vision
Instructions to use ostris/siglip2-base-patch16-style-naflex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ostris/siglip2-base-patch16-style-naflex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="ostris/siglip2-base-patch16-style-naflex") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ostris/siglip2-base-patch16-style-naflex") model = AutoModel.from_pretrained("ostris/siglip2-base-patch16-style-naflex") - Notebooks
- Google Colab
- Kaggle
File size: 426 Bytes
b8cf15e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"image_processor": {
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Siglip2ImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_num_patches": 256,
"patch_size": 16,
"resample": 2,
"rescale_factor": 0.00392156862745098
},
"processor_class": "Siglip2Processor"
}
|