Eye-Quality (EyeQ) Assessment Dataset
June 29, 2026 ยท View on GitHub
The project web for "Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces" in MICCAI 2019.
Fundus Enhancement: We also have a related work for "Modeling and Enhancing Low-quality Retinal Fundus Images" in IEEE TMI, 2021. The code is released in Github: Cofe-Net
-Introduction:
Eye-Quality (EyeQ) Assessment Dataset is a re-annotated subset from the EyePACS dataset for fundus image quality assessment.
EyeQ dataset has 28,792 retinal images with a three-level quality grading (i.e., 'Good', 'Usable' and 'Reject').
| Train | - | - | - | - | - | Test | - | - | - | - | - | Total | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DR-0 | DR-1 | DR-2 | DR-3 | DR-4 | All | DR-0 | DR-1 | DR-2 | DR-3 | DR-4 | All | ||
| Good | 6,342 | 699 | 1,100 | 167 | 39 | 8,347 | 5,966 | 886 | 1,354 | 199 | 65 | 8,470 | 16,817 |
| Usable | 1,353 | 103 | 283 | 79 | 58 | 1,896 | 3,201 | 359 | 721 | 145 | 133 | 4,559 | 6,435 |
| Reject | 1,544 | 109 | 426 | 87 | 154 | 2,320 | 2,195 | 153 | 569 | 104 | 199 | 3,220 | 5,540 |
| Total | 9,239 | 911 | 1,809 | 333 | 251 | 12,543 | 1,1362 | 1,398 | 2,644 | 448 | 397 | 16,249 | 28,792 |
-Usage:
- The original fundus images could be downloaded from EyePACS dataset.
- All the original fundus images should be pre-processed by 'EyeQ_process_main.py' in folder './EyeQ_preprocess'.
- The quality label is in './data' folder, where the 'Label_EyeQ_train.csv' and 'Label_EyeQ_test.csv' are divided by EyePACS, and the 'DR_grade' label is also from EyePACS.
- We also release our Multiple Color-space Fusion Network (MCF-Net) based on ResNet121 in './MCFNet' folder.
Note: The original weights of MCF-Net are no longer usable due to several version updates. We recommend re-training the model using the code available on our GitHub repository.
-Reference:
If you use this dataset and code, please cite the following papers:
[1] Huazhu Fu, Boyang Wang, Jianbing Shen, Shanshan Cui, Yanwu Xu, Jiang Liu, Ling Shao, "Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces", in MICCAI, 2019. [PDF] Note: The corrected accuracy score of MCF-Net is 0.8800.
-License:
The code is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License for NonCommercial use only. Any commercial use should get formal permission first.
Update log:
- 2021.12.29: Added link for fundus enhancement project.
- 2020.06.18: Corrected the accuracy score.
- 2019.11.15: Released the code of MCF-Net.
- 2019.07.10: Released the dataset.