Instructions to use fesvhtr/RS-So14-S2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fesvhtr/RS-So14-S2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="fesvhtr/RS-So14-S2") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("fesvhtr/RS-So14-S2") model = AutoModelForZeroShotImageClassification.from_pretrained("fesvhtr/RS-So14-S2") - Notebooks
- Google Colab
- Kaggle
File size: 502 Bytes
a487a63 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"image_processor": {
"data_format": "channels_first",
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "SiglipImageProcessorFast",
"image_std": [
0.5,
0.5,
0.5
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 384,
"width": 384
}
},
"processor_class": "SiglipProcessor"
}
|