Extended Efficient Layer Aggregation Networks (E-ELANs)
Explore Extended Efficient Layer Aggregation Networks (E-ELAN) in YOLOv7 architecture. Understand how E-ELAN overcomes ELAN limitations by optimizing gradient paths, employing group convolution, feature map shuffling, and parallel branches to improve feature diversity and multi-scale detection accuracy.
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Why E-ELAN?
E-ELAN builds upon the ELAN (efficient longest available network) design strategy, which aims to design efficient networks. The primary motivation behind ELAN is to control the shortest longest gradient path, enabling deeper networks to learn and converge effectively. Analyzing the gradient path enables the weights of different layers to learn more diverse features, resulting in better predictions and reducing latency.
Based on this, ELAN authors came up with the conclusion listed below:
Note: By controlling the shortest longest gradient path, a deeper network can learn and converge effectively.
Limitations of ELAN
It is no longer possible to improve ELAN because it has reached a stable state, and adding more computational blocks may destroy this stable state and decrease ...