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--- |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: mask |
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dtype: image |
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splits: |
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- name: train |
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num_bytes: 119307954.15 |
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num_examples: 2450 |
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- name: validation |
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num_bytes: 27229918 |
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num_examples: 613 |
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- name: test |
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num_bytes: 39479824 |
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num_examples: 955 |
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download_size: 169673709 |
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dataset_size: 186017696.15 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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task_categories: |
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- image-segmentation |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Links |
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* Paper: https://arxiv.org/pdf/1908.09101v2 |
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* Repository: https://github.com/Mhaiyang/ICCV2019_MirrorNet |
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* Project page: https://mhaiyang.github.io/ICCV2019_MirrorNet/index.html |
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* We got our data from: https://github.com/Charmve/Mirror-Glass-Detection |
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# Split info |
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We split the train to train and validation with the ratio 80% and 20% respectively. If you want to use the original split, you can just combine train and validation. |
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# License info |
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Refer to the project page, original repository, and paper. We retrieve the dataset from third party repository. |
