Instructions to use yuvansharma/caip-vitl256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yuvansharma/caip-vitl256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="yuvansharma/caip-vitl256", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yuvansharma/caip-vitl256", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "CaipImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "interpolation": "bicubic", | |
| "resize_mode": "shortest", | |
| "size": 256, | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_caip.CaipImageProcessor" | |
| } | |
| } |