Image Feature Extraction
Transformers
Safetensors
mage_vit
feature-extraction
mage-vl
vision-encoder
codec-vit
video-understanding
custom_code
Instructions to use Jinstudio/Mage-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jinstudio/Mage-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Jinstudio/Mage-ViT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jinstudio/Mage-ViT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "crop_size": { | |
| "height": 256, | |
| "width": 256 | |
| }, | |
| "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": 256 | |
| } | |
| } | |