
摘要
YOLOv7 在从 5 FPS 到 160 FPS 的速度范围内,超越了所有已知的目标检测器,在 GPU V100 上以 30 FPS 或更高的帧率运行时,其精度达到了所有已知实时目标检测器中的最高值 56.8% AP。YOLOv7-E6 目标检测器(在 V100 上达到 56 FPS,精度为 55.9% AP)在速度上比基于变压器的检测器 SWIN-L Cascade-Mask R-CNN(在 A100 上达到 9.2 FPS,精度为 53.9% AP)快 509%,精度高 2%;同时在速度上比基于卷积的检测器 ConvNeXt-XL Cascade-Mask R-CNN(在 A100 上达到 8.6 FPS,精度为 55.2% AP)快 551%,精度高 0.7% AP。此外,YOLOv7 还在速度和精度方面超越了其他多种目标检测器,包括 YOLOR、YOLOX、Scaled-YOLOv4、YOLOv5、DETR、Deformable DETR、DINO-5scale-R50、ViT-Adapter-B 等。更为重要的是,我们仅使用 MS COCO 数据集从零开始训练 YOLOv7,未使用任何其他数据集或预训练权重。源代码已发布在 https://github.com/WongKinYiu/yolov7。
代码仓库
AlexeyAB/darknet
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WongKinYiu/YOLO
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open-mmlab/mmyolo
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ibaiGorordo/ONNX-YOLOv7-Object-Detection
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Mind23-2/MindCode-129
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pjreddie/darknet
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wongkinyiu/yolov7
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dnozza/profanity-obfuscation
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securade/hub
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zhanghuiyao/yolov7_mindspore
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mikel-brostrom/yolov7
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kadirnar/yolov7-pip
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henrytsui000/YOLO
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基准测试
| 基准 | 方法 | 指标 |
|---|---|---|
| 2d-object-detection-on-ceymo | YOLOv7 | mAP: 69.5 |
| object-detection-on-coco | YOLOv7-D6 (44 fps) | box mAP: 56.6 |
| object-detection-on-coco | YOLOv7-E6 (56 fps) | box mAP: 56 |
| object-detection-on-coco | YOLOv7 (161 fps) | box mAP: 51.4 |
| object-detection-on-coco | YOLOv7-X (114 fps) | box mAP: 53.1 |
| object-detection-on-coco | YOLOv7-W6 (84 fps) | box mAP: 54.9 |
| object-detection-on-coco-o | YOLOv7-E6E | Average mAP: 32.0 Effective Robustness: 6.42 |
| pedestrian-detection-on-dvtod | YOLOv7 (Visible) | mAP: 35.3 |
| pedestrian-detection-on-dvtod | YOLOv7 (Thermal) | mAP: 77.8 |
| real-time-object-detection-on-coco | YOLOv7-X | FPS (V100, b=1): 114 box AP: 53.1 |
| real-time-object-detection-on-coco | YOLOv7-D6(1280) | FPS (V100, b=1): 44 box AP: 56.6 |
| real-time-object-detection-on-coco | YOLOv7-E6E(1280) | FPS (V100, b=1): 36 box AP: 56.8 |
| real-time-object-detection-on-coco | YOLOv7-W6(1280) | FPS (V100, b=1): 84 box AP: 54.9 |
| real-time-object-detection-on-coco | YOLOv7-E6(1280) | FPS (V100, b=1): 56 box AP: 56 |