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
File size: 1,423 Bytes
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"class_info_file": "cityscapes_panoptic.json",
"do_normalize": true,
"do_reduce_labels": false,
"do_rescale": true,
"do_resize": true,
"ignore_index": 255,
"image_mean": [
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],
"image_processor_type": "OneFormerImageProcessor",
"image_std": [
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],
"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",
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"car",
"motorcycle",
"person",
"river",
"road",
"sidewalk",
"sky",
"terrain",
"truck",
"vegetation"
],
"num_text": 12,
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"11": 11,
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},
"thing_dataset_id_to_contiguous_id": {
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"2": 2,
"3": 3,
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},
"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
}
}
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