BSDS500 (Berkeley Segmentation Dataset 500)
Introduced by Pablo Arbelaez et al. in Contour Detection and Hierarchical Image Segmentation
Berkeley Segmentation Data Set 500 (BSDS500) is a standard benchmark for contour detection. This dataset is designed for evaluating natural edge detection that includes not only object contours but also object interior boundaries and background boundaries. It includes 500 natural images with carefully annotated boundaries collected from multiple users. The dataset is divided into three parts: 200 for training, 100 for validation and the rest 200 for test.
Source: Object Contour Detection with a Fully Convolutional Encoder-Decoder NetworkBenchmarks
| Trend | Task | Dataset Variant | Best Model | Paper | Code |
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BSDS500 (Quality 30 Grayscale)
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FBCNN
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BSDS500 (Quality 10 Color)
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FBCNN
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BSDS500 (Quality 20 Color)
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FBCNN
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BSDS500 (Quality 30 Color)
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FBCNN
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BSDS500 (Quality 10 Grayscale)
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FBCNN
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BSDS500 (Quality 20 Grayscale)
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FBCNN
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BSDS500
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RK-CCSNet
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BSDS500
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RCN
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Papers
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