Instructions to use jlee-larr/dynaflip-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jlee-larr/dynaflip-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="jlee-larr/dynaflip-base", trust_remote_code=True) 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 AutoModel model = AutoModel.from_pretrained("jlee-larr/dynaflip-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 484 Bytes
988579a | 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 | {
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "DynaFLIPImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"rescale_factor": 0.00392156862745098,
"size": {
"height": 224,
"width": 224
},
"auto_map": {
"AutoImageProcessor": "image_processing_dynaflip.DynaFLIPImageProcessor",
"AutoProcessor": "processing_dynaflip.DynaFLIPProcessor"
}
} |