DeathDaDev/Materializer
Image Classification
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Updated
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3
image
imagewidth (px) 512
1.02k
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class label 8
classes |
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2Displacement
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1Color
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5NormalGL
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7Roughness
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7Roughness
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2Displacement
|
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4NormalDX
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1Color
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5NormalGL
|
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4NormalDX
|
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1Color
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1Color
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1Color
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5NormalGL
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7Roughness
|
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1Color
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5NormalGL
|
|
2Displacement
|
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4NormalDX
|
|
7Roughness
|
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2Displacement
|
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3Metalness
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1Color
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4NormalDX
|
|
2Displacement
|
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2Displacement
|
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1Color
|
|
7Roughness
|
|
5NormalGL
|
|
1Color
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|
2Displacement
|
|
5NormalGL
|
|
7Roughness
|
|
3Metalness
|
|
2Displacement
|
|
4NormalDX
|
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4NormalDX
|
|
7Roughness
|
|
1Color
|
|
7Roughness
|
|
5NormalGL
|
|
1Color
|
|
3Metalness
|
|
1Color
|
|
7Roughness
|
|
2Displacement
|
|
1Color
|
|
3Metalness
|
|
3Metalness
|
|
1Color
|
|
2Displacement
|
|
4NormalDX
|
|
3Metalness
|
|
7Roughness
|
|
7Roughness
|
|
5NormalGL
|
|
5NormalGL
|
|
5NormalGL
|
|
1Color
|
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2Displacement
|
|
5NormalGL
|
|
4NormalDX
|
|
1Color
|
|
1Color
|
|
4NormalDX
|
|
5NormalGL
|
|
0AmbientOcclusion
|
|
7Roughness
|
|
5NormalGL
|
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5NormalGL
|
|
5NormalGL
|
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4NormalDX
|
|
1Color
|
|
0AmbientOcclusion
|
|
7Roughness
|
|
5NormalGL
|
|
0AmbientOcclusion
|
|
2Displacement
|
|
4NormalDX
|
|
5NormalGL
|
|
1Color
|
|
5NormalGL
|
|
2Displacement
|
|
3Metalness
|
|
4NormalDX
|
|
5NormalGL
|
|
4NormalDX
|
|
2Displacement
|
|
7Roughness
|
|
0AmbientOcclusion
|
|
7Roughness
|
|
2Displacement
|
|
7Roughness
|
|
2Displacement
|
|
5NormalGL
|
|
7Roughness
|
|
7Roughness
|
|
4NormalDX
|
|
7Roughness
|
|
1Color
|
This is the third iteration and official release of a dataset curated to power the Materializer model for Blender. The dataset contains a range of labeled texture images that were sourced from ambientCG under their Creative Commons CC0 1.0 Universal License. These textures are designed to help in the classification of various material maps, which are essential for creating realistic 3D materials in Blender.
The dataset is still evolving, and I plan to expand it with a wider range of textures sourced from various online repositories. Feedback and contributions are welcome to help improve the dataset further.