Visual Question Answering
Transformers
PyTorch
English
vision-encoder-decoder
image-text-to-text
ui refexp
Instructions to use ivelin/donut-refexp-combined-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivelin/donut-refexp-combined-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="ivelin/donut-refexp-combined-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("ivelin/donut-refexp-combined-v1") model = AutoModelForMultimodalLM.from_pretrained("ivelin/donut-refexp-combined-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_align_long_axis": false, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_thumbnail": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "DonutImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "DonutProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 1280, | |
| "width": 960 | |
| } | |
| } | |