In Defense of Pre-trained ImageNet Architectures for Real-time Semantic Segmentation of Road-driving Images

Understand

Recent success of semantic segmentation approaches on demanding road driving datasets has spurred interest in many related application fields.

• Many of these applications involve real-time prediction on mobile platforms such as cars, drones and various kinds of robots.
• Real-time setup is challenging due to extraordinary computational complexity involved.
• Many previous works address the challenge with custom lightweight architectures which decrease computational complexity by reducing depth, width and layer capacity with respect to general purpose architectures.