The paper "EfficientDet: Scalable and Efficient Object Detection" by Mingxing Tan, Ruoming Pang, and Quoc V. Le from Google Research, Brain Team, addresses the challenge of improving the efficiency of object detection models while maintaining or enhancing accuracy. The authors propose several key optimizations, including a weighted bidirectional feature pyramid network (BiFPN) for efficient multi-scale feature fusion and a compound scaling method to uniformly scale the resolution, depth, and width of all backbone, feature network, and box/class prediction networks. These optimizations are integrated into a new family of object detectors called EfficientDet, which consistently achieve better efficiency across a wide range of resource constraints. Key contributions include: 1. **BiFPN**: A weighted bidirectional feature pyramid network that allows for efficient multi-scale feature fusion by introducing learnable weights to learn the importance of different input features. 2. **Compound Scaling**: A method that jointly scales up the resolution, depth, and width of all backbone, feature network, and box/class prediction networks, improving both accuracy and efficiency. The authors evaluate EfficientDet on the COCO dataset, demonstrating state-of-the-art performance with significantly fewer parameters and FLOPs compared to previous detectors. Specifically, EfficientDet-D7 achieves 55.1 AP on the COCO test-dev dataset with 77M parameters and 410B FLOPs, outperforming previous detectors by 4 AP while being 2.7x smaller and using 7.4x fewer FLOPs. EfficientDet is also faster on GPU/CPU, with up to 4x-11x speedup compared to previous detectors. Additionally, the paper discusses the application of EfficientDet to semantic segmentation tasks, showing promising results with 1.7% better accuracy and 9.8x fewer FLOPs compared to DeepLabV3+. The authors also provide a detailed ablation study to validate the effectiveness of their proposed optimizations. Overall, the paper presents a comprehensive approach to designing efficient and accurate object detection models, making significant contributions to the field of computer vision.The paper "EfficientDet: Scalable and Efficient Object Detection" by Mingxing Tan, Ruoming Pang, and Quoc V. Le from Google Research, Brain Team, addresses the challenge of improving the efficiency of object detection models while maintaining or enhancing accuracy. The authors propose several key optimizations, including a weighted bidirectional feature pyramid network (BiFPN) for efficient multi-scale feature fusion and a compound scaling method to uniformly scale the resolution, depth, and width of all backbone, feature network, and box/class prediction networks. These optimizations are integrated into a new family of object detectors called EfficientDet, which consistently achieve better efficiency across a wide range of resource constraints. Key contributions include: 1. **BiFPN**: A weighted bidirectional feature pyramid network that allows for efficient multi-scale feature fusion by introducing learnable weights to learn the importance of different input features. 2. **Compound Scaling**: A method that jointly scales up the resolution, depth, and width of all backbone, feature network, and box/class prediction networks, improving both accuracy and efficiency. The authors evaluate EfficientDet on the COCO dataset, demonstrating state-of-the-art performance with significantly fewer parameters and FLOPs compared to previous detectors. Specifically, EfficientDet-D7 achieves 55.1 AP on the COCO test-dev dataset with 77M parameters and 410B FLOPs, outperforming previous detectors by 4 AP while being 2.7x smaller and using 7.4x fewer FLOPs. EfficientDet is also faster on GPU/CPU, with up to 4x-11x speedup compared to previous detectors. Additionally, the paper discusses the application of EfficientDet to semantic segmentation tasks, showing promising results with 1.7% better accuracy and 9.8x fewer FLOPs compared to DeepLabV3+. The authors also provide a detailed ablation study to validate the effectiveness of their proposed optimizations. Overall, the paper presents a comprehensive approach to designing efficient and accurate object detection models, making significant contributions to the field of computer vision.