Instructions to use Hemg/semantic-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hemg/semantic-segmentation with Transformers:
# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Hemg/semantic-segmentation") model = SegformerForSemanticSegmentation.from_pretrained("Hemg/semantic-segmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 673 Bytes
fffd68b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"_valid_processor_keys": [
"images",
"segmentation_maps",
"do_resize",
"size",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_reduce_labels",
"return_tensors",
"data_format",
"input_data_format"
],
"do_normalize": true,
"do_reduce_labels": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "SegformerImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 512,
"width": 512
}
}
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