YOLOv4

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Bag of freebies (Bof) & Bag of specials (BoS)

Backbone

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Neck

Modified from source
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Modified from source
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Bag of Freebies (BoF) for backbone

  • CutMix and Mosaic data augmentation,
  • DropBlock regularization, and
  • Class label smoothing
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p = tf.placeholder(tf.float32, shape=[None, 10])
# Use 0.9 instead of 1.0.
feed_dict = {
p: [[0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0]] # Image with label "3"
}
# logits_real_image is the logits calculated by
# the discriminator for real images.
d_real_loss = tf.nn.sigmoid_cross_entropy_with_logits(
labels=p, logits=logits_real_image)

Bag of Specials (BoS) for backbone

  • Mish activation,
  • Cross-stage partial connections (CSP), and
  • Multi-input weighted residual connections (MiWRC)
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Bag of Freebies (BoF) for detector

  • CIoU-loss,
  • CmBN,
  • DropBlock regularization,
  • Mosaic data augmentation,
  • Self-Adversarial Training,
  • Eliminate grid sensitivity,
  • Using multiple anchors for a single ground truth,
  • Cosine annealing scheduler,
  • Optimal hyperparameters, and
  • Random training shapes
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  • increase the overlapping area of the ground truth box and the predicted box,
  • minimize their central point distance, and
  • maintain the consistency of the boxes’ aspect ratio.
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Modified from the paper.
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Modified from source
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Bag of Specials (BoS) for detector

  • Mish activation,
  • modified SPP-block,
  • modified SAM-block,
  • modified PAN path-aggregation block &
  • DIoU-NMS

Technology evaluated

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Credits & References

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