yolo-v3.md

YOLOv3: An Incremental Improvement (2018), Joseph Redmon et al.

contributors: @GitYCC

[paper] [code]

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The Deal

Bounding Box Prediction

• Following YOLO9000 our system predicts bounding boxes using dimension clusters as anchor boxes.
• the predictions correspond to:
  • $b_x =σ(t_x)+c_x$
  • $b_y =σ(t_y)+c_y$
  • $b_w =p_we^{t_w}$
  • $b_h =p_he^{t_h}$
• During training we use sum of squared error loss.

Class Prediction

• Each box predicts the classes the bounding box may contain using multilabel classification.
• We simply use independent logistic classifiers. During training we use binary cross-entropy loss for the class predictions.

Predictions Across Scales

• YOLOv3 predicts boxes at 3 different scales. Our system extracts features from those scales using a similar concept to feature pyramid networks (FPN).

  • 
  • (from: Feature Pyramid Networks for Object Detection (2017))

• Non-max suppression is used for choosing boxes from 3 different scales.

Feature Extractor

Our new network Darknet-53 is a hybrid approach between the network used in YOLOv2, Darknet-19, and that newfangled residual network stuff. Our network uses successive 3 × 3 and 1 × 1 convolutional layers but now has some shortcut connections as well and is significantly larger.

Evaluation