New Features:

  1. Weighted-Residual-Connections (WRC)
  2. Cross-Stage-Partial-connections (CSP)
  3. Cross mini-Batch Normalization (CmBN)
  4. Self-adversarial-training (SAT)
  5. Mish activation
  6. Mosaic data augmentation
  7. DropBlock regularization
  8. CIoU loss

Object Detector:

The object detector is composed of several parts. The parts are

object detector parts
detector types

YOLO v4 uses:

  • Bag of Freebies (BoF) for backbone: CutMix and Mosaic data augmentation, DropBlock regularization, Class label smoothing
  • Bag of Specials (BoS) for backbone: Mish activation, Cross-stage partial connections (CSP), Multiinput weighted residual connections (MiWRC).
  • 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 [52], Optimal hyperparameters, Random training shapes.
  • Bag of Specials (BoS) for detector: Mish activation, SPP-block, SAM-block, PAN path-aggregation block, DIoU-NMS.

Install YoloV4(Ubuntu):

git clone https://github.com/AlexeyAB/darknet.git
cd darknet

Prediction Command:

Download the pretrained weights for yolov4

./darknet detect cfg/yolov4.cfg yolov4.weights data/dog.jpg

Yolov4 and Yolov3 prediction comparision:

comparision between yolov4 and yolov3

Train Custom Objects in YOLOV4:

The yolov4 custom object training is same as yolov3. Only the difference is backbone of the yolo. Yolov3 has darknet53 and yolov4 has CSPDarknet53.



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Manivannan Murugavel

Manivannan Murugavel

Artificial Intelligence and Data Science