Yolo V4 Object Detection

How Yolo V4 object detection delivers higher mAP and shorter inference time

Enhanced Features of Yolo v4

• Yolo v4 has a faster inference speed for an object detector in production systems.
• Optimization for parallel computations
• Yolo v4 is an efficient and powerful object detection model using a single GPU to deliver an accurate object detector quickly.

Object detector models are composed of

• A pre-trained Backbone
• Neck
• Head that is used to predict classes and bounding boxes of objects.

The backbone of the Object detector can be pre-trained neural network.

Example: ImageNet, VGG16 , ResNet-50 , SpineNet , EfficientNet-B0/B7, CSPResNeXt50 or, CSPDarknet53 or ShuffleNet running on CPU.

Object detector models insert additional layers between the backbone and head which are referred to as the Neck of the object detector. Neck layers collect feature maps from different stages and are composed of several bottom-up paths and several topdown paths.

Examples: FPN, Path Aggregation Network (PAN), BiFPN, and NAS-FPN

Object detector head predicts classes and bounding boxes of objects and can be a One-stage detector or a Two-stage detector