Image-to-Text
Transformers
PyTorch
TensorBoard
English
mplug_owl2
feature-extraction
image-quality-assessment
document-quality
mplug-owl2
vision-language
document-analysis
color-quality
IQA
custom_code
Instructions to use mapo80/DeQA-Doc-Color with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mapo80/DeQA-Doc-Color 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-Color", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mapo80/DeQA-Doc-Color", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from mapo80/DeQA-Doc-Color: direct link, hf CLI and curl.
- Browser
- Download file 408 Bytes
-
https://huggingface.co/mapo80/DeQA-Doc-Color/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://mapo80/DeQA-Doc-Color/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/mapo80/DeQA-Doc-Color/resolve/main/preprocessor_config.json
408 Bytes
| { | |
| "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 | |
| } | |
| } | |