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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 408 Bytes
07fdac1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"crop_size": {
"height": 448,
"width": 448
},
"do_center_crop": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [0.48145466, 0.4578275, 0.40821073],
"image_processor_type": "CLIPImageProcessor",
"image_std": [0.26862954, 0.26130258, 0.27577711],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 448
}
}
|