Instructions to use fesvhtr/RC-B32-S2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fesvhtr/RC-B32-S2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="fesvhtr/RC-B32-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/RC-B32-S2") model = AutoModelForZeroShotImageClassification.from_pretrained("fesvhtr/RC-B32-S2") - Notebooks
- Google Colab
- Kaggle
File size: 653 Bytes
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"image_processor": {
"crop_size": {
"height": 224,
"width": 224
},
"data_format": "channels_first",
"do_center_crop": true,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "CLIPImageProcessorFast",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 224
}
},
"processor_class": "CLIPProcessor"
}
|