Instructions to use Library-Mutsumi/clip_onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Library-Mutsumi/clip_onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Library-Mutsumi/clip_onnx")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Library-Mutsumi/clip_onnx", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "stages": [ | |
| { | |
| "force_background": "white", | |
| "type": "convert_rgb" | |
| }, | |
| { | |
| "antialias": true, | |
| "interpolation": "bicubic", | |
| "max_size": null, | |
| "size": 224, | |
| "type": "resize" | |
| }, | |
| { | |
| "size": [ | |
| 224, | |
| 224 | |
| ], | |
| "type": "center_crop" | |
| }, | |
| { | |
| "type": "to_tensor" | |
| }, | |
| { | |
| "mean": [ | |
| 0.48145467042922974, | |
| 0.45782750844955444, | |
| 0.40821072459220886 | |
| ], | |
| "std": [ | |
| 0.2686295509338379, | |
| 0.2613025903701782, | |
| 0.27577710151672363 | |
| ], | |
| "type": "normalize" | |
| } | |
| ] | |
| } |