efficientnetv2

**EfficientNetV2** is the **second generation of EfficientNet that optimizes for training speed in addition to inference efficiency** — using a combination of Fused-MBConv blocks, progressive learning (increasing image size during training), and NAS optimized for training time. **What Is EfficientNetV2?** - **Fused-MBConv**: Replaces depthwise separable conv with regular conv in early stages (faster on modern hardware due to better utilization). - **Progressive Learning**: Start training with small images and weak augmentation, gradually increase both. - **NAS Objective**: Optimized for training speed (not just parameter count or FLOPs). - **Paper**: Tan & Le (2021). **Why It Matters** - **5-11× Faster Training**: EfficientNetV2-M trains 5× faster than EfficientNet-B7 with similar accuracy. - **Progressive Learning**: Simple but effective — smaller images early = faster initial epochs. - **Hardware Aware**: Recognizes that depthwise conv is slow on GPUs due to poor hardware utilization. **EfficientNetV2** is **EfficientNet optimized for real-world speed** — understanding that FLOPs don't equal training time and optimizing what actually matters.