Datasets:
File size: 1,168 Bytes
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---
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: train
num_bytes: 119307954.15
num_examples: 2450
- name: validation
num_bytes: 27229918
num_examples: 613
- name: test
num_bytes: 39479824
num_examples: 955
download_size: 169673709
dataset_size: 186017696.15
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
task_categories:
- image-segmentation
size_categories:
- 1K<n<10K
---
# Links
* Paper: https://arxiv.org/pdf/1908.09101v2
* Repository: https://github.com/Mhaiyang/ICCV2019_MirrorNet
* Project page: https://mhaiyang.github.io/ICCV2019_MirrorNet/index.html
* We got our data from: https://github.com/Charmve/Mirror-Glass-Detection
# Split info
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.
# License info
Refer to the project page, original repository, and paper. We retrieve the dataset from third party repository. |