Mouthhan / TEAM_1114_STAS_II

https://tbrain.trendmicro.com.tw/Competitions/Details/22

TEAM_1114_STAS_II

Competetion Reference

https://tbrain.trendmicro.com.tw/Competitions/Details/22

Environment Building

1. 下載競賽連結中的 SEG_Train_Datasets.zip 並解壓縮，將裡面的 Train_Annotations 及 Train_Images 改名為 All_Annotations 及 All_Images
2. 執行 preprocess.py 以取出全部圖片的遮罩並儲存於 All_Mask 與 All_Masked_Images 中
3. 將 All_Images 及 All_Mask 中的 00000800.jpg ~ 00000919.jpg 按下方格式放置於 Valid 資料夾，其餘則按相同格式放置於 Train 資料夾
4. 下載競賽連結中的 Public_Image.zip 及 Private_Image.zip ，解壓縮後放置於 Inference_Images 中
5. 執行 get_model.sh 下載比賽Ensemble使用的6個模型權重放置於 Models 中
6. pip install -r requirements.txt 以安裝使用到的套件
7. 接著即可按照 STAS.ipynb 中的操作訓練及驗證模型 (Ensemble於最下方)
8. 最終執行 postprocess.py 對預測結果進行後處理即可得到比賽上傳的最終成績

Data Path

.
└── STAS
    ├── All_Annotations
    │   └── 00000000.json ~ 00001052.json
    ├── All_Images
    │   └── 00000000.jpg ~ 00001052.jpg
    ├── All_Mask (Run 'preprocess.py' to generate, contain all groundtruth mask)
    │   └── 00000000.jpg ~ 00001052.jpg
    ├── All_Masked_Images (Run 'preprocess.py' to generate, contain all groundtruth masked images)
    │   └── 00000000.jpg ~ 00001052.jpg
    ├── Inference_Images (Public & Private images to inference Leaderboard score)
    │   └── Private_00000000.jpg ~ Private_00000183.jpg & Public_00000000.jpg ~ Public_00000130.jpg
    ├── Models(Run 'get_model.sh' to download models' weight)
    │   └── *.pth (total 6 models to ensemble)
    ├── Train (For model training, total 932 image)
    │   ├── img
    │   │   └── 00000000.jpg ~ 00000799.jpg + 00000920.jpg ~ 00001052.jpg
    │   └── label
    │       └── 00000000.jpg ~ 00000799.jpg + 00000920.jpg ~ 00001052.jpg
    ├── Valid (For model validation, total 120 image)
    │   ├── img
    │   │   └── 00000800.jpg ~ 00000919.jpg
    │   └── label
    │       └── 00000800.jpg ~ 00000919.jpg
    ├── get_model.sh (For downloading models' weight)
    │   
    ├── preprocess.py (For changing points to mask)
    │
    ├── postprocess.py (For postprocessing prediction to final result)
    │
    ├── requirements.txt (For install toolkits)
    │
    └── STAS.ipynb (for training & inference model)

Parameter Setting

Train

random_seed = 8863
optimizer = SAM(base_optimizer=AdamW,lr=3e-4)
loss_function = BCELoss()
model_architecture = DeepLabV3Plus
encoders = ['resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x4d', 'se_resnet50'] (All 6 models use different encoder)
input_size = 1600*800 pixels
batchsize = [16, 12, 8, 12, 8, 12] (與 encoders 相同排序)
epochs = 200
scheduler = lr×〖(1-(now epoch)/total epoch)〗^0.9 (當 epoch = 100 及 150 時)

Augmentation

使用 albumentations 同時對 image 及 mask 進行操作

train_transform = [

        albu.HorizontalFlip(p=0.5),
        albu.Rotate(limit=40,p=0.5,border_mode=cv2.BORDER_CONSTANT),
        albu.VerticalFlip(p=0.5),
        albu.ShiftScaleRotate(scale_limit=0.5, rotate_limit=0, shift_limit=0.1, p=0.5, border_mode=0),
        
        albu.HueSaturationValue(p=0.6),
        albu.Sharpen(p=0.5),
        albu.RandomBrightnessContrast(p=0.4),

        albu.OneOf([
            albu.ElasticTransform(p=0.5, alpha=120, sigma=120*0.05, alpha_affine=120*0.03),
            albu.GridDistortion(p=0.5),
            albu.OpticalDistortion(distort_limit=1, shift_limit=0.5, p=1)
        ]),
    ]

Prediction & Postprocessing

threshold = 0.43 (判斷是否為 STAS 的閥值)
fill_hole = cv2.fillpoly(contours)
erosion = cv2.erode(kernel_size =(3x3), iteration = 2)

contours, hierarchy = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

## Fill hole
for idx in range(len(contours)):
    contour = contours[idx]
    area = cv2.contourArea(contour)
    cv2.fillPoly(image, pts =[contour], color=(255,255,255))

contours, hierarchy = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

## Erase Edge
kernel = np.ones((3,3), np.uint8)
erosion = cv2.erode(image, kernel, iterations = 2)

Toolkit & Version

Ubuntu=20.04
python=3.8.12
numpy=1.22.3
albumentations=1.1.0
matplotlib=3.5.1
segmentation-models-pytorch=0.2.1
torch=1.9.0
opencv-python=4.5.5

Reference

SAM optimizer from: https://github.com/davda54/sam

Citing

@misc{Yakubovskiy:2019,
  Author = {Pavel Yakubovskiy},
  Title = {Segmentation Models Pytorch},
  Year = {2020},
  Publisher = {GitHub},
  Journal = {GitHub repository},
  Howpublished = {\url{https://github.com/qubvel/segmentation_models.pytorch}}
}

@Article{info11020125,
    AUTHOR = {Buslaev, Alexander and Iglovikov, Vladimir I. and Khvedchenya, Eugene and Parinov, Alex and Druzhinin, Mikhail and Kalinin, Alexandr A.},
    TITLE = {Albumentations: Fast and Flexible Image Augmentations},
    JOURNAL = {Information},
    VOLUME = {11},
    YEAR = {2020},
    NUMBER = {2},
    ARTICLE-NUMBER = {125},
    URL = {https://www.mdpi.com/2078-2489/11/2/125},
    ISSN = {2078-2489},
    DOI = {10.3390/info11020125}
}