RT-DETR改进前后数据+YOLOv7

b站视频：https://www.bilibili.com/video/BV1fD421j7hQ/

【腾讯文档】RT-DETR改进前后数据

1.RT-DETR-r50

1.1.last

rtdetr-r50 summary: 480 layers, 41964383 parameters, 0 gradients, 129.6 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 380/380 [00:17<00:00, 21.27it/s]
                   all       3036       3427      0.699      0.685      0.659      0.507
                 weigh       3036        337       0.68      0.721      0.692      0.537
        height measure       3036        802      0.681        0.5      0.527      0.356
             drop ball       3036        820      0.601      0.741      0.609      0.475
          size measure       3036        601      0.672      0.631      0.619      0.481
                record       3036        867      0.863      0.834      0.847      0.688
Speed: 0.1ms preprocess, 4.3ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/train/exp

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1.2.best

val: Scanning /data/RT-DETR/yolo_behavior_Dataset_all2/labels/val.cache... 3036 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3036/3036 [00:00<?, ?it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 759/759 [00:26<00:00, 28.76it/s]
                   all       3036       3427       0.71      0.681      0.658      0.507
                 weigh       3036        337      0.688      0.721      0.691      0.535
        height measure       3036        802      0.693      0.481      0.525      0.356
             drop ball       3036        820       0.61      0.733      0.606      0.473
          size measure       3036        601      0.687      0.633      0.622      0.482
                record       3036        867       0.87      0.835      0.847       0.69
Speed: 0.1ms preprocess, 6.8ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/val/exp

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2.RT-DETR-r50-filter

2.1.last

rtdetr-r50 summary: 480 layers, 41964383 parameters, 0 gradients, 129.6 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 365/365 [00:17<00:00, 21.07it/s]
                   all       2915       3275      0.721      0.654      0.663      0.511
                 weigh       2915        337      0.678      0.668      0.657      0.512
        height measure       2915        726      0.669      0.482      0.536      0.385
             drop ball       2915        757      0.642      0.665      0.614      0.463
          size measure       2915        589      0.761      0.643      0.671      0.519
                record       2915        866      0.856      0.813      0.838      0.677
Speed: 0.1ms preprocess, 4.3ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/train/exp3

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2.2.best

val: Scanning /data/RT-DETR/yolo_behavior_Dataset_all3_filter/labels/val.cache... 2915 images, 0 backgrounds, 0 corrupt: 100%|██████████| 2915/2915 [00:00<?, ?it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 729/729 [00:25<00:00, 28.35it/s]
                   all       2915       3275      0.722      0.654      0.662      0.511
                 weigh       2915        337      0.683      0.671      0.654      0.511
        height measure       2915        726      0.661      0.482       0.54      0.388
             drop ball       2915        757      0.643      0.666      0.613      0.464
          size measure       2915        589      0.764      0.635      0.665      0.515
                record       2915        866      0.858      0.816      0.839      0.677
Speed: 0.1ms preprocess, 6.8ms inference, 0.0ms loss, 0.2ms postprocess per image

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3.RT-DETR-r50-filter-data-enhance

3.1.last

rtdetr-r50 summary: 480 layers, 41964383 parameters, 0 gradients, 129.6 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 407/407 [00:18<00:00, 21.50it/s]
                   all       3252       3624      0.741      0.687      0.667       0.53
                 weigh       3252        674      0.876      0.653      0.669      0.554
        height measure       3252        726      0.642      0.519      0.518      0.376
             drop ball       3252        758       0.62      0.781      0.673       0.52
          size measure       3252        589      0.781      0.628      0.629      0.489
                record       3252        877      0.787      0.855      0.848      0.713
Speed: 0.1ms preprocess, 4.3ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/train/exp5

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3.2.best

val: Scanning /data/RT-DETR/yolo_behavior_Dataset_all4_data_enhance/labels/val.cache... 3252 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3252/
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 813/813 [00:28<00:00, 29.03it/s]
                   all       3252       3624      0.739      0.684      0.663      0.527
                 weigh       3252        674      0.876      0.651      0.671      0.556
        height measure       3252        726      0.641      0.514      0.512       0.37
             drop ball       3252        758      0.625      0.776       0.67      0.518
          size measure       3252        589      0.768      0.628       0.62      0.481
                record       3252        877      0.783      0.852      0.843      0.711
Speed: 0.1ms preprocess, 6.7ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/val/exp5

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4.RT-DETR-r50-filter-data-enhance2

4.1.last

Ultralytics YOLOv8.0.201 
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