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:
- 886ad44031489a45e12157f0e2d7b2563479dcef50dd59c47270b624ea437bd0
- Size of remote file:
- 4.92 GB
- SHA256:
- 94954006aba2f33919e4082bc65fc5b5091f73998e9915f85cb71e832d6ee96d
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