Instructions to use Agacy/PDI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Agacy/PDI with Transformers:
# Load model directly from transformers import AutoProcessor, OneFormerForUniversalSegmentation processor = AutoProcessor.from_pretrained("Agacy/PDI") model = OneFormerForUniversalSegmentation.from_pretrained("Agacy/PDI") - Notebooks
- Google Colab
- Kaggle
| { | |
| "class_info_file": "cityscapes_panoptic.json", | |
| "do_normalize": true, | |
| "do_reduce_labels": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "ignore_index": 255, | |
| "image_mean": [ | |
| 0.48500001430511475, | |
| 0.4560000002384186, | |
| 0.4059999883174896 | |
| ], | |
| "image_processor_type": "OneFormerImageProcessor", | |
| "image_std": [ | |
| 0.2290000021457672, | |
| 0.2239999920129776, | |
| 0.22499999403953552 | |
| ], | |
| "metadata": { | |
| "0": "building", | |
| "1": "bus", | |
| "10": "truck", | |
| "11": "vegetation", | |
| "2": "car", | |
| "3": "motorcycle", | |
| "4": "person", | |
| "5": "river", | |
| "6": "road", | |
| "7": "sidewalk", | |
| "8": "sky", | |
| "9": "terrain", | |
| "class_names": [ | |
| "building", | |
| "bus", | |
| "car", | |
| "motorcycle", | |
| "person", | |
| "river", | |
| "road", | |
| "sidewalk", | |
| "sky", | |
| "terrain", | |
| "truck", | |
| "vegetation" | |
| ], | |
| "num_text": 12, | |
| "stuff_dataset_id_to_contiguous_id": { | |
| "0": 0, | |
| "11": 11, | |
| "5": 5, | |
| "6": 6, | |
| "7": 7, | |
| "8": 8, | |
| "9": 9 | |
| }, | |
| "thing_dataset_id_to_contiguous_id": { | |
| "1": 1, | |
| "10": 10, | |
| "2": 2, | |
| "3": 3, | |
| "4": 4 | |
| } | |
| }, | |
| "num_labels": 19, | |
| "num_text": 234, | |
| "processor_class": "OneFormerProcessor", | |
| "repo_path": "shi-labs/oneformer_demo", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 2048, | |
| "shortest_edge": 1024 | |
| } | |
| } | |