Image-to-Text
Transformers
PyTorch
TensorBoard
English
mplug_owl2
feature-extraction
image-quality-assessment
document-quality
mplug-owl2
vision-language
document-analysis
sharpness
blur-detection
IQA
custom_code
Instructions to use mapo80/DeQA-Doc-Sharpness with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mapo80/DeQA-Doc-Sharpness with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="mapo80/DeQA-Doc-Sharpness", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mapo80/DeQA-Doc-Sharpness", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5c88a5bfd52288b18fd4f688c3ae13813f9b8d112f2a87fdabc2f8e84a169fd
- Size of remote file:
- 1.52 GB
- SHA256:
- 4c358f2e9c72bf180194543d9c167d23bd22ca3aa0c9d689afa21d505ca8ab8c
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