Image Feature Extraction
Transformers
Safetensors
aloe_vit_vision
feature-extraction
aloe
b-cos
interpretability
computer-vision
vision-transformer
cvpr-2026
custom_code
Instructions to use rmaser/aloe-vit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rmaser/aloe-vit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="rmaser/aloe-vit-base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rmaser/aloe-vit-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "rmaser/aloe-arch--image_processing_aloe.AloeImageProcessor" | |
| }, | |
| "crop_size": null, | |
| "data_format": "channels_first", | |
| "default_to_square": true, | |
| "device": null, | |
| "disable_grouping": null, | |
| "do_center_crop": null, | |
| "do_convert_rgb": null, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "AloeImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "input_data_format": null, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } | |