Instructions to use Amod/docdenoise-YOLOS-FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amod/docdenoise-YOLOS-FT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Amod/docdenoise-YOLOS-FT")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Amod/docdenoise-YOLOS-FT") model = AutoModelForObjectDetection.from_pretrained("Amod/docdenoise-YOLOS-FT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Amod/docdenoise-YOLOS-FT: direct link, hf CLI and curl.
- Browser
- Download file 457 Bytes
-
https://huggingface.co/Amod/docdenoise-YOLOS-FT/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Amod/docdenoise-YOLOS-FT/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Amod/docdenoise-YOLOS-FT/resolve/main/preprocessor_config.json
457 Bytes
| { | |
| "do_convert_annotations": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "format": "coco_detection", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "YolosFeatureExtractor", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "pad_size": null, | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 864, | |
| "shortest_edge": 512 | |
| } | |
| } | |