The following codes are the solutions (3st place, private score: 0.66886) for the dacon competition.
If you would like to know more about the competition, please refer to the following link:
2023 Samsung AI Challenge : Camera-Invariant Domain Adaptation
- 주최 : Samsung Advanced Institute of Technology
- 주관 : DACON
Move to the "segmentation" directory and follow the README.md
/workspace
├── configs
│ ├── _base_
│ │ ├── datasets
│ │ │ ├── samsung_fisheye.py
│ ├── samsung
│ │ ├── exp_01.py
│ │ ├── exp_02.py
│ │ ├── exp_03.py
│ │ ├── exp_04.py
│ │ ├── exp_05.py
├── data
│ ├── preprocess_data (will be made after running fisheye_transform.py)
│ │ ├── train_fisheye_gt
│ │ ├── train_fisheye_image
│ │ ├── val_fisheye_gt
│ │ ├── val_fisheye_image
│ ├── test_image
│ ├── train_source_gt
│ ├── train_source_image
│ ├── train_target_image
│ ├── val_source_gt
│ ├── val_source_image
│ ├── sample_submission.csv
│ ├── test.csv
│ ├── train_source.csv
│ ├── train_target.csv
│ ├── val_source.csv
├── results
│ ├── exp_04.pkl
│ ├── exp_05.pkl
├── submission
│ ├── exp_04.csv
│ ├── exp_05.csv
├── work_dirs
│ ├── exp_04
│ │ ├── best_mIoU_iter_22000.pth
├── fisheye_transform.py
├── submit.py
├── test.py
├── train.py
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The final submission is exp_04.
| Index | Model | Private mIoU | Public mIoU | Val mIoU (%) | Iter | Fine-tuned model | Pre-trained model |
|---|---|---|---|---|---|---|---|
| exp_01 | UperNet | 0.64483 | 0.6192 | 67.30 | 20000 | ckpt | ckpt |
| exp_02 | Mask2Former | 0.66456 | 0.62771 | 66.01 | 9000 | ckpt | ckpt |
| exp_03 | UperNet | 0.65114 | 0.62775 | 68.03 | 14000 | ckpt | ckpt |
| exp_04 | Mask2Former | 0.66886 | 0.63133 | 70.34 | 22000 | ckpt | ckpt |
| exp_05 | Mask2Former | 0.67288 | 0.62905 | - | 40000 | ckpt | ckpt |