Great Barrier Reef Model Pipeline: 15th place

https://www.kaggle.com/c/tensorflow-great-barrier-reef

All I want to use was YOLO-X!

Transcript

1. Copyright 2022 Maxwell_110
   Validation strategy
   - Sequence-based 4 fold CV
   - The number of CoTS is close in each fold
   - Training data is frames with CoTs
   - Validation data includes frames w/o CoTs
   Resize up to 2.75 times
   using progressive learning
   1280
   720
   Augmentation
   Increasing probability of applying
   augmentation as progressive
   learning progresses.
   - Default YOLO-X augmentations
   - random resize: (-5, 5)
   - mosaic / MixUp / hsv / flip:
   p = 0.6 -> 0.8
   - degrees: Not used
   - translate: 0.1
   - mosaic / MixUp scale: (0.5, 1.5)
   - RandomGamma
   - RGBShift
   - Sharpen
   - GaussNoise
   Batch Size: 4
   GeForce RTX 3080 (x 2)
   Solution description in Kaggle discussion
   https://www.kaggle.com/c/tensorflow-great-barrier-reef/discussion/307691
   Learning strategy
   - Progressive learning
   - Optimizer: default SGD
   (decay: 5e-4, momentum: 0.9)
   - LR: .000625
   - Scheduler: yoloxwarmcos
   - min_lr_ratio: 0.1
   - EMA: on
   - warmup_epochs: 5
   - max_epoch: 30
   TTA Seq-NMS
   https://arxiv.org/abs/1602.08465
   https://github.com/tmoopenn/seq-nms
   n_frames: 2
   confidence threshold: 0.07
   linkage threshold: 0.1
   nms th: 0.4
   Weighted Box Fusion
   skip box threshold: 0.05
   wbf IoU threshold: 0.45
   Final confidence threshold: .08
   Public LB : 0.607
   Private LB : 0.714
   

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