Global-Wheat-Detection

The challenge in this Kaggle competition is to detect the wheat heads and draw bounding boxes around all the detected wheat heads in the given image. Here are a few example of the predictions generated using FasterRCNN model:

Here are a few approaches I tried to approach the problem with brief description and achieved score on public leader board (score is mAP (mean average precision) in this competitions).

Experiment #1 (0.6426 on public LB)

• Used model: COCO Pre-trained FasterRCNN with ResNet50 backbone
• No image augmentations
• Other settings:
  • Image size:1024 x 1024
  • No CV
  • 5 Epochs
  • Batch size: 16 images

Experiment #2 (0.6547 on public LB)

• Used model: COCO Pre-trained FasterRCNN with ResNet50 backbone
• No image augmentations
• 1 round of training on pseudo Labels
• 5 epochs to train on original data, 4 epochs for training on pseudo labels

Experiment #3 (0.6575 on public LB)

• Used model: COCO Pre-trained FasterRCNN with ResNet50 backbone
• No image augmentations
• 2 rounds of training on pseudo Labels
• 5 epochs to train on original data, 4 epochs/round for training on pseudo labels

Experiment #4 (0.6528 on public LB)

• Used model: COCO Pre-trained FasterRCNN with ResNet50 backbone
• No image augmentations
• 3 rounds of training on pseudo Labels
• 5 epochs to train on original data, 4 epochs/round for training on pseudo labels

Experiment #5 (0.6498 on public LB)

• Used model: COCO Pre-trained FasterRCNN with ResNet50 backbone
• Random Brightness, Contrass, Horizontal and Vertical flips and Hue Saturation Value Image Augmentations
• No pseudo labels
• 12 epochs on augmented dataset: https://www.kaggle.com/kaushal2896/gwdaugmented/version/3

Experiment #6 (0.6826 on public LB)

• Used model: COCO Pre-trained FasterRCNN with ResNet50 backbone
• Random Brightness, Contrass, Horizontal and Vertical flips and Hue Saturation Value Image Augmentations
• 2 rounds of psuedo labeling
• 12 epochs on augmented dataset (mentioned on exp #5) and 4 epochs/round for training on pseudo labels

Experiment #7 (0.6582 on public LB)

• Used model: COCO Pre-trained FasterRCNN with ResNet50 backbone
• Random Brightness, Contrass, Horizontal and Vertical flips, Hue Saturation Value Image Augmentations and Random Cutout (with handling of bounding boxes)
• No pseudo labeling
• 20 epochs on augmented dataset: https://www.kaggle.com/kaushal2896/gwdaugmented/version/4

Experiment #8 (0.6624 on public LB)

• Used model: COCO Pre-trained FasterRCNN with ResNet101 backbone
• Random Brightness, Contrass, Horizontal and Vertical flips, Hue Saturation Value Image Augmentations
• No pseudo labeling
• 5 epochs on augmented dataset: https://www.kaggle.com/toomuchdataerror/resnet101trainedmodel

Experiment #9 (0.6817 on public LB)

• Ensemble of Exp. #8 and Exp. #7
• Used WBF (Weighted Boxes Fusion) as an ensemble technique.

Experiment #10 (0.6824 on public LB)

• Used model: COCO Pre-trained FasterRCNN with ResNet101 backbone
• Random Brightness, Contrass, Horizontal and Vertical flips, Hue Saturation Value Image Augmentations
• 5-fold Cross Validation with WBF ensemble
• 5 epochs per fold

Experiment #11 (0.6910 on public LB)

• Used model: COCO Pre-trained FasterRCNN with ResNet101 backbone
• Horizontal and Vertical flips, Only one of Random Brightness Contrass or Hue Saturation Value Image Augmentations
• 5-fold Cross Validation with WBF ensemble
• 20 epochs per fold

Experiment #12 (0.6859 on public LB)

• Used model: Pretrained EfficientDet D5
• Horizontal and Vertical flips, Only one of Random Brightness Contrass or Hue Saturation Value Image Augmentations
• No CV
• 3 epochs

Experiment #13 (0.7147 on public LB)

• Used model: Pretrained EfficientDet D5
• Horizontal and Vertical flips, Only one of Random Brightness Contrass or Hue Saturation Value Image Augmentations
• No CV
• 20 epochs

Experiment #14 (0.7194 on public LB)

• Used model: Pretrained EfficientDet D5
• Horizontal and Vertical flips, Only one of Random Brightness Contrass or Hue Saturation Value Image Augmentations
• 5 fold CV with EBF ensemble
• 30 epochs per fold

Experiment #15 (0.7490 on public LB)

• Pretraied two EfficientDet D5 models trained on full dataset for 30 epochs
• Horizontal and Vertical flips, Only one of Random Brightness Contrass or Hue Saturation Value Image Augmentations
• Pseudo labeling + TTA + Ensemble

Experiment #16 (0.7432 on public LB)

• Pretrained YOLOv5x
• Default yolov5 augmentations (includes all above augmentations)
• 5 Fold CV
• 35 epochs per fold