sh-zheng/vit-base-patch16-224-in21k-fintuned-SurfaceRoughness
Image Classification
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imagewidth (px) 634
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class label 3
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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0RoughnessB
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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1RoughnessC
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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2RoughnessD
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Surface Roughness Dataset
This is a dataset containing google image snapshots representing surface roughness categories of B, C, and D according to ASCE 7-16 26.7.2
Uses
This dataset is used for the NCSEA quick-start guide to work with machine learning models.
Data Structure
An example of data looks like
{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1350x1058>,
'label': 0}
Data Split
| train | validation | test | |
|---|---|---|---|
| # of examples | 66 | 15 | 11 |
Dataset Card Contact
Sheng Zheng
szheng@martinmartin.com
- Downloads last month
- 105
Size of downloaded dataset files:
95.3 MB
Size of the auto-converted Parquet files:
95.3 MB
Number of rows:
92
Models trained or fine-tuned on sh-zheng/SurfaceRoughness