0/199 2.43G 0.1034 0.02849 0 0.1319 104 640 0 0 0.007315 0.001336 0.07286 0.01585 0
1/199 2.74G 0.07152 0.02648 0 0.09801 121 640 0.1993 0.5549 0.2771 0.0766 0.05101 0.01782 0
2/199 4.3G 0.05857 0.02409 0 0.08266 124 640 0.1904 0.8065 0.5085 0.1929 0.04321 0.01567 0
3/199 4.3G 0.05342 0.02185 0 0.07527 117 640 0.3379 0.7709 0.589 0.2419 0.04023 0.01328 0
4/199 4.3G 0.04954 0.02081 0 0.07035 120 640 0.404 0.8206 0.6924 0.29 0.03962 0.01215 0
5/199 4.3G 0.04899 0.01993 0 0.06892 117 640 0.5271 0.7809 0.73 0.3379 0.03651 0.01127 0
6/199 4.3G 0.04632 0.01921 0 0.06554 108 640 0.6256 0.7744 0.7593 0.3552 0.03667 0.01108 0
7/199 4.3G 0.04489 0.01855 0 0.06344 109 640 0.5457 0.8698 0.8096 0.4075 0.03353 0.0108 0
8/199 4.3G 0.04326 0.0188 0 0.06206 110 640 0.5287 0.887 0.8088 0.3989 0.03212 0.01044 0
9/199 4.3G 0.04269 0.01806 0 0.06075 103 640 0.5529 0.9106 0.859 0.4331 0.0329 0.01001 0
10/199 4.3G 0.04105 0.0182 0 0.05925 105 640 0.5999 0.9038 0.8613 0.4322 0.03089 0.009879 0
11/199 4.3G 0.04002 0.01754 0 0.05757 112 640 0.586 0.9043 0.861 0.4566 0.03133 0.009963 0
12/199 4.3G 0.0407 0.01727 0 0.05796 96 640 0.6714 0.8976 0.8805 0.4578 0.03065 0.009667 0
13/199 4.3G 0.03942 0.01702 0 0.05644 116 640 0.7061 0.9059 0.9037 0.4965 0.02936 0.009533 0
14/199 4.3G 0.03934 0.01645 0 0.0558 99 640 0.603 0.8719 0.8188 0.4242 0.03134 0.01012 0
15/199 4.3G 0.03862 0.01675 0 0.05537 99 640 0.5594 0.9519 0.9066 0.4991 0.02978 0.009594 0
16/199 4.3G 0.03773 0.01733 0 0.05505 128 640 0.5238 0.9608 0.8831 0.4987 0.028 0.009366 0
17/199 4.3G 0.03747 0.01628 0 0.05376 111 640 0.552 0.9414 0.8673 0.4566 0.02858 0.009357 0
18/199 4.3G 0.03683 0.01652 0 0.05335 100 640 0.6739 0.9085 0.8859 0.4658 0.03001 0.009273 0
19/199 4.3G 0.03715 0.01644 0 0.05359 130 640 0.6562 0.8421 0.8075 0.4052 0.03113 0.01017 0
20/199 4.3G 0.03577 0.01675 0 0.05252 111 640 0.5651 0.9498 0.8884 0.4884 0.02858 0.009563 0
21/199 4.3G 0.0357 0.01626 0 0.05195 99 640 0.6036 0.9482 0.9002 0.518 0.02735 0.008939 0
22/199 4.3G 0.03523 0.01652 0 0.05175 108 640 0.6584 0.8337 0.8191 0.4095 0.03072 0.0102 0
23/199 4.3G 0.03609 0.01662 0 0.05271 107 640 0.557 0.9106 0.8479 0.4096 0.03021 0.009703 0
24/199 4.3G 0.03556 0.01611 0 0.05167 99 640 0.7122 0.8876 0.8722 0.4666 0.02893 0.009283 0
25/199 4.3G 0.03551 0.01661 0 0.05212 101 640 0.5188 0.8985 0.8183 0.4279 0.0301 0.009984 0
26/199 4.3G 0.03593 0.0165 0 0.05243 104 640 0.5371 0.7406 0.6756 0.3085 0.03624 0.01129 0
27/199 4.3G 0.03573 0.01661 0 0.05234 105 640 0.603 0.8996 0.8552 0.4551 0.03108 0.009552 0
28/199 4.3G 0.03546 0.01625 0 0.05172 109 640 0.488 0.9393 0.8379 0.4533 0.02951 0.01002 0
29/199 4.3G 0.03565 0.01663 0 0.05228 113 640 0.677 0.8745 0.8569 0.448 0.02993 0.01016 0
30/199 4.3G 0.03547 0.01639 0 0.05186 107 640 0.3283 0.9514 0.7647 0.4095 0.0319 0.01164 0
31/199 4.3G 0.03532 0.01644 0 0.05176 114 640 0.5018 0.8881 0.7971 0.3886 0.03191 0.01021 0
32/199 4.3G 0.03573 0.01674 0 0.05246 103 640 0.4717 0.9022 0.7898 0.3879 0.03107 0.01038 0
33/199 4.3G 0.03638 0.01691 0 0.05329 105 640 0.2622 0.7751 0.5147 0.2153 0.03944 0.01312 0
34/199 4.3G 0.03573 0.01663 0 0.05236 106 640 0.6569 0.8044 0.7871 0.3856 0.03177 0.01122 0
35/199 4.3G 0.03564 0.01692 0 0.05256 101 640 0.4402 0.8499 0.7288 0.3564 0.03447 0.0112 0
36/199 4.3G 0.03614 0.01644 0 0.05259 103 640 0.6767 0.7997 0.775 0.3911 0.03177 0.01098 0
37/199 4.3G 0.03505 0.01664 0 0.05168 96 640 0.5741 0.6109 0.5865 0.2828 0.03796 0.01393 0
38/199 4.3G 0.03446 0.01663 0 0.05109 122 640 0.3684 0.9278 0.7684 0.3907 0.02995 0.01125 0
39/199 4.3G 0.0342 0.01653 0 0.05073 115 640 0.3346 0.5988 0.4149 0.1657 0.0441 0.01348 0
40/199 4.3G 0.03519 0.01649 0 0.05168 102 640 0.3489 0.9446 0.7824 0.401 0.03084 0.01181 0
41/199 4.3G 0.034 0.01643 0 0.05042 104 640 0.4217 0.9069 0.7424 0.3991 0.02919 0.01106 0
42/199 4.3G 0.03309 0.01651 0 0.0496 123 640 0.4679 0.8907 0.7779 0.4132 0.03187 0.01021 0
43/199 4.3G 0.03294 0.01617 0 0.04911 119 640 0.5412 0.8954 0.8321 0.4412 0.03031 0.009958 0
44/199 4.3G 0.03349 0.0163 0 0.04979 105 640 0.5523 0.9399 0.8816 0.5152 0.02686 0.009467 0
45/199 4.3G 0.03332 0.01583 0 0.04915 98 640 0.5544 0.8912 0.8281 0.4612 0.02822 0.009839 0
46/199 4.3G 0.03306 0.01607 0 0.04913 97 640 0.6421 0.8539 0.8214 0.447 0.02826 0.01016 0
47/199 4.3G 0.03269 0.01578 0 0.04847 101 640 0.6414 0.8839 0.8509 0.4695 0.02747 0.00964 0
48/199 4.3G 0.03264 0.01569 0 0.04833 104 640 0.7734 0.8514 0.8811 0.507 0.02828 0.009662 0
49/199 4.3G 0.03169 0.01556 0 0.04725 110 640 0.6375 0.9686 0.9126 0.5513 0.02558 0.008467 0
50/199 4.3G 0.03216 0.01568 0 0.04784 119 640 0.54 0.9582 0.8868 0.5076 0.02592 0.009294 0
51/199 4.3G 0.03203 0.01564 0 0.04767 116 640 0.5942 0.8928 0.8158 0.4484 0.02838 0.009971 0
52/199 4.3G 0.03055 0.01571 0 0.04626 108 640 0.5553 0.8556 0.8121 0.4311 0.03137 0.009978
基于yolov5算法实现跌倒识别检测告警源码+模型文件+评估指标曲线+使用说明.7z
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基于yolov5算法实现跌倒识别检测源码+模型文件+评估指标曲线+使用说明.7z （79个子文件） 
基于yolov5算法实现跌倒识别检测源码+模型文件+评估指标曲线+使用说明 yolov5_code models 
common.py 7KB yolo.py 12KB hub yolov3-spp.yaml 1KB yolov5-fpn.yaml 1KB yolov5-panet.yaml 1KB yolov5s.yaml 1KB __pycache__ experimental.cpython-36.pyc 7KB common.cpython-38.pyc 9KB yolo.cpython-36.pyc 10KB __init__.cpython-38.pyc 147B experimental.cpython-38.pyc 6KB common.cpython-36.pyc 9KB __init__.cpython-36.pyc 137B yolo.cpython-38.pyc 10KB __init__.py 0B yolov5x.yaml 1KB yolov5l.yaml 1KB export.py 4KB yolov5m.yaml 1KB experimental.py 6KB runs evolve opt.yaml 466B hyp.yaml 356B weights sotabench.py 14KB data coco.yaml 2KB hyp.scratch.yaml 2KB hyp.finetune.yaml 846B voc.yaml 735B coco128.yaml 1KB scripts get_coco.sh 935B get_voc.sh 4KB onnx.py 413B test.py 14KB train.py 28KB __pycache__ test.cpython-38.pyc 9KB test.cpython-36.pyc 9KB onnx.cpython-38.pyc 559B detect.py 8KB
requirements.txt 569B inference images 
1.jpg 34KB output 1.jpg 75KB weights utils __pycache__ activations.cpython-36.pyc 3KB torch_utils.cpython-36.pyc 8KB activations.cpython-38.pyc 3KB general.cpython-38.pyc 41KB google_utils.cpython-36.pyc 3KB __init__.cpython-38.pyc 146B torch_utils.cpython-38.pyc 9KB datasets.cpython-36.pyc 27KB datasets.cpython-38.pyc 27KB google_utils.cpython-38.pyc 3KB general.cpython-36.pyc 41KB __init__.cpython-36.pyc 136B general.py 53KB datasets.py 38KB evolve.sh 747B activations.py 2KB torch_utils.py 9KB __init__.py 0B google_app_engine additional_requirements.txt 105B Dockerfile 821B app.yaml 173B google_utils.py 5KB yolov5s.pt 14.48MB hubconf.py 4KB exp_falldown opt.yaml 459B
results.png 239KB test_batch0_pred.jpg 159KB train_batch1.jpg 254KB hyp.yaml 356B labels.png 377KB precision-recall_curve.png 35KB test_batch0_gt.jpg 157KB train_batch2.jpg 253KB labels_correlogram.png 483KB train_batch0.jpg 267KB weights best.pt 14.08MB last.pt 14.08MB results.txt 29KB 使用说明.txt 1KB资源评论
