Instructions to use Raghavan/textnet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Raghavan/textnet-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, TextNetBackbone processor = AutoImageProcessor.from_pretrained("Raghavan/textnet-base") model = TextNetBackbone.from_pretrained("Raghavan/textnet-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Raghavan/textnet-base: direct link, hf CLI and curl.
- Browser
- Download file 441 Bytes
-
https://huggingface.co/Raghavan/textnet-base/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Raghavan/textnet-base/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Raghavan/textnet-base/resolve/main/preprocessor_config.json
441 Bytes
| { | |
| "crop_size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "do_center_crop": false, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "TextNetImageProcessor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 640 | |
| } | |
| } | |