Instructions to use Intel/dpt-swinv2-base-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/dpt-swinv2-base-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("depth-estimation", model="Intel/dpt-swinv2-base-384")# Load model directly from transformers import AutoImageProcessor, AutoModelForDepthEstimation processor = AutoImageProcessor.from_pretrained("Intel/dpt-swinv2-base-384") model = AutoModelForDepthEstimation.from_pretrained("Intel/dpt-swinv2-base-384") - Notebooks
- Google Colab
- Kaggle
File size: 425 Bytes
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"do_normalize": true,
"do_pad": false,
"do_rescale": true,
"do_resize": true,
"ensure_multiple_of": 1,
"image_mean": [
0.5,
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0.5
],
"image_processor_type": "DPTImageProcessor",
"image_std": [
0.5,
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],
"keep_aspect_ratio": false,
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 384,
"width": 384
},
"size_divisor": null
}
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