TensorFlow - Help Protect the Great Barrier Reef

Implements methods to detect the crown-of-thorns starfish in underwater image data corresponding to the corresponding Kaggle competition.

Prerequisites

Tested with

• Python 3.7
• PyTorch 1.10.0
• Tensorboard 2.7.0 and the packages in requirements.txt.

Data structure:

• dataset
  • train.csv
  • val.csv
  • train_images
    • video_0
      • 0.jpg
      • 1.jpg ...
    • video_1
      • 0.jpg
      • 1.jpg ...
    • video_2
      • 0.jpg
      • 1.jpg ...

File structure:

• data/gbr_dataset.py: PyTorch dataset used in the dataloader (returns an image and the corresponding annotations)
• data/transforms.py: data augmentations
• main.py: main file to start the training
• train.py: training (and evaluation) loop
• evaluation.py: evaluation functions
• tensorboard_utils.py: functions to make plotting easier in Tensorboard
• coco_eval.py, coco_utils.py, pytorch_utils.py: Help methods from Torchvision to evaluate object detectors with pycocotools

Models

Currently, supported models include:

FasterRCNN

With the following backbones

• Resnet (resnet18, resnet50)
• EfficientNet (efficientnet-d0, efficientnet-d4)

YOLOX

• With CSP-Backbone (yolox-s, yolox-m)
• Based on the implementation https://github.com/zhangming8/yolox-pytorch

Config

Create the config file with the name config.yaml in the same directory as main.py. The variables under params will be logged.

# config.yaml

local:
  dataset_root: "/home/.../great-barrier-reef/dataset"
  train_annotations: "/home/.../dataset/reef_starter_0.05/train.csv"
  val_annotations: "/home/.../dataset/reef_starter_0.05/val.csv"

  checkpoint_root: "/home/.../great-barrier-reef/checkpoints"
  resume_checkpoint: "2021-12-17T23_32_18/best_model.pth"
  pretrained_weights_root: "/home/.../pretrained_weights"

params:
  model_name: yolox-m
  num_epochs: 40
  eval_every_n_epochs: 1
  save_every_n_epochs: 10
  train_batch_size: 2
  val_batch_size: 1
  train_num_workers: 0
  val_num_workers: 0

  optimizer: "Adam" # SGD / Adam
  learning_rate: 1.e-4
  weight_decay: 0.0005
  momentum: 0.9
  dampening: 0
  nesterov: False
  beta_1: 0.9 # Adam
  beta_2: 0.999 # Adam

  gradient_clipping_norm: 35

  input_size: [ 512, 512 ]  # height, width
  test_size: [ 736, 1312 ]  # height, width
  nms_thresh: 0.65

  rotation_limit: 10
  random_scale: 0.2
  random_rain_prob: 0.2
  use_copy_paste: True