Visual Question Answering
Transformers
Safetensors
English
vilt
VQA
transformer
vision-language
inclusive-ai
Eval Results (legacy)
Instructions to use Zagarsuren/vilt-finetuned-vizwiz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zagarsuren/vilt-finetuned-vizwiz with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="Zagarsuren/vilt-finetuned-vizwiz")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("Zagarsuren/vilt-finetuned-vizwiz") model = AutoModelForVisualQuestionAnswering.from_pretrained("Zagarsuren/vilt-finetuned-vizwiz", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d113193b02c08cbcb7d5a5e1c7b649c2fe1f0645b370d610cc10a71601487ccb
- Size of remote file:
- 470 MB
- SHA256:
- bccde444d19b56fab3451a51e7956d8130ba6deaf39d5594e89a6864f10e53d8
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