Instructions to use hf-tiny-model-private/tiny-random-AltCLIPModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-AltCLIPModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hf-tiny-model-private/tiny-random-AltCLIPModel") 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("hf-tiny-model-private/tiny-random-AltCLIPModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-AltCLIPModel") - Notebooks
- Google Colab
- Kaggle
File size: 463 Bytes
8251bf6 | 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 28 | {
"crop_size": {
"height": 30,
"width": 30
},
"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": "CLIPImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 30
}
}
|