Instructions to use Bingsu/temp_vilt_vqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bingsu/temp_vilt_vqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="Bingsu/temp_vilt_vqa")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("Bingsu/temp_vilt_vqa") model = AutoModelForVisualQuestionAnswering.from_pretrained("Bingsu/temp_vilt_vqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 289 Bytes
1c21477 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"do_normalize": true,
"do_resize": true,
"feature_extractor_type": "ViltFeatureExtractor",
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"processor_class": "ViltProcessor",
"resample": 3,
"size": 384,
"size_divisor": 32
}
|