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DarkCityScape_mIOU_mPA
This is a repository for calculating the mIOU (mean intersection over union) and mPA (mean pixel accuracy) for DarkCityScapes. DarkCityScapes is the "Dark version" of the CityScape dataset [1] and is specifically designed for extreme low-light image enhancement tasks. The dataset is simulated using gamma correction on 150 CityScape validation images.
Sample Images from DarkCityScapes
Get Started
-
Download the DarkCityScape dataset from Baiduyun with passord
wvhy
-
Download the DarkCityScape labels from Baiduyun with password
a2z5
-
Put the segmentation outcome of the enhanced images in
path_to_your_pred
And the Groudtruth segmentation labels inpath_to_your_gt
-
Run the following script
python main.py --pred path_to_your_pred --gt path_to_your_gt
Note:
- You could refers to
Dark.sh
if you are not sure how to put the images. - The size of
pred
andgt
should be same. - The result of mIOU and mPA will be in
output_iou.txt
andoutput_pa.txt
, respectively.
Sample Result
Following is the result table from some state-of-the-art low-light image enhancement models
Dark | PIE [2] | Retinex [3] | MBLLEN [4] | KinD [5] | ZeroDCE [6] | |
---|---|---|---|---|---|---|
mIOU | 54.49% | 61.97% | 57.96% | 51.98% | 63.42% | 64.36% |
mPA | 70.76% | 68.89% | 66.76% | 59.06% | 71.69% | 74.20% |
References:
[1] Cordts, Marius, et al. "The cityscapes dataset for semantic urban scene understanding." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[2] Fu, Xueyang, et al. "A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation." IEEE Transactions on Image Processing 24.12 (2015): 4965-4977.
[3] Wei, Chen, et al. "Deep retinex decomposition for low-light enhancement." arXiv preprint arXiv:1808.04560 (2018).
[4] Lv, Feifan, et al. "MBLLEN: Low-Light Image/Video Enhancement Using CNNs." BMVC. 2018.
[5] Zhang, Yonghua, Jiawan Zhang, and Xiaojie Guo. "Kindling the darkness: A practical low-light image enhancer." Proceedings of the 27th ACM international conference on multimedia. 2019.
[6] Guo, Chunle, et al. "Zero-reference deep curve estimation for low-light image enhancement." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.