0/199 3.14G 0.08299 0.01205 0 0.09504 27 640 0.1216 0.5082 0.1752 0.03651 0.05651 0.006962 0
1/199 3.3G 0.05734 0.009522 0 0.06687 33 640 0.04656 0.3327 0.03126 0.005977 0.0633 0.005924 0
2/199 3.3G 0.05326 0.008662 0 0.06192 34 640 0.1245 0.6751 0.2035 0.04465 0.04939 0.006771 0
3/199 3.3G 0.04755 0.008264 0 0.05582 24 640 0.1895 0.8002 0.4269 0.1281 0.04016 0.006235 0
4/199 3.3G 0.04704 0.007748 0 0.05479 34 640 0.1767 0.6596 0.2858 0.06241 0.04249 0.005322 0
5/199 3.3G 0.04545 0.007586 0 0.05304 19 640 0.1511 0.7042 0.3317 0.1007 0.03956 0.005499 0
6/199 3.3G 0.04223 0.007174 0 0.0494 40 640 0.2361 0.8691 0.5256 0.1962 0.03636 0.006036 0
7/199 3.3G 0.04193 0.007423 0 0.04935 31 640 0.3179 0.7488 0.4513 0.1383 0.03916 0.005496 0
8/199 3.3G 0.03903 0.006703 0 0.04573 33 640 0.3983 0.8001 0.5136 0.1959 0.03346 0.00516 0
9/199 3.3G 0.03828 0.006533 0 0.04481 31 640 0.3097 0.807 0.5099 0.2162 0.03362 0.005661 0
10/199 3.3G 0.03911 0.00628 0 0.04539 37 640 0.2586 0.8603 0.5424 0.2318 0.03317 0.005543 0
11/199 3.3G 0.0363 0.006251 0 0.04255 33 640 0.3802 0.871 0.582 0.2476 0.03315 0.005257 0
12/199 3.3G 0.03497 0.006031 0 0.041 31 640 0.2909 0.8914 0.5995 0.2681 0.03152 0.005308 0
13/199 3.3G 0.03473 0.005959 0 0.04069 18 640 0.4197 0.8477 0.5681 0.2157 0.03273 0.004904 0
14/199 3.3G 0.03483 0.005947 0 0.04078 24 640 0.2243 0.8109 0.5318 0.2072 0.03494 0.006166 0
15/199 3.3G 0.03407 0.005681 0 0.03975 23 640 0.2908 0.9127 0.6267 0.2709 0.03074 0.005231 0
16/199 3.3G 0.03361 0.005743 0 0.03935 47 640 0.3589 0.9011 0.6287 0.2612 0.02984 0.004787 0
17/199 3.3G 0.03325 0.005509 0 0.03876 56 640 0.5471 0.8535 0.5896 0.2932 0.02848 0.004711 0
18/199 3.3G 0.03303 0.00548 0 0.03851 38 640 0.399 0.8807 0.5855 0.2734 0.02931 0.00485 0
19/199 3.3G 0.032 0.005468 0 0.03746 23 640 0.4596 0.9166 0.6229 0.293 0.0292 0.00464 0
20/199 3.3G 0.03207 0.005396 0 0.03747 28 640 0.4796 0.9117 0.6288 0.2919 0.02847 0.004453 0
21/199 3.3G 0.03136 0.005258 0 0.03662 25 640 0.5134 0.904 0.6289 0.3136 0.02819 0.004323 0
22/199 3.3G 0.03201 0.005297 0 0.03731 23 640 0.4455 0.9331 0.65 0.3193 0.0282 0.00453 0
23/199 3.3G 0.03349 0.005269 0 0.03876 39 640 0.4176 0.9234 0.6848 0.3432 0.02798 0.00472 0
24/199 3.3G 0.03178 0.005227 0 0.03701 30 640 0.3774 0.9389 0.6698 0.3168 0.02873 0.004498 0
25/199 3.3G 0.03067 0.005209 0 0.03588 32 640 0.5274 0.9166 0.6733 0.3268 0.02768 0.004361 0
26/199 3.3G 0.03157 0.005122 0 0.03669 24 640 0.5367 0.9205 0.6783 0.3474 0.0269 0.004089 0
27/199 3.3G 0.03101 0.005176 0 0.03618 35 640 0.4685 0.934 0.6631 0.3293 0.0268 0.004298 0
28/199 3.3G 0.03076 0.005144 0 0.0359 33 640 0.5069 0.9146 0.6236 0.3132 0.02697 0.004365 0
29/199 3.3G 0.03023 0.005075 0 0.03531 27 640 0.563 0.9292 0.6856 0.3478 0.0268 0.004048 0
30/199 3.3G 0.03043 0.005 0 0.03543 35 640 0.4753 0.937 0.6912 0.3451 0.02714 0.004176 0
31/199 3.3G 0.03149 0.005149 0 0.03664 21 640 0.5241 0.9166 0.6683 0.3375 0.02735 0.004174 0
32/199 3.3G 0.03143 0.005049 0 0.03648 34 640 0.4785 0.9214 0.6803 0.3462 0.02655 0.0044 0
33/199 3.3G 0.03275 0.005079 0 0.03783 35 640 0.5461 0.9321 0.7398 0.3614 0.02756 0.004054 0
34/199 3.3G 0.03066 0.005042 0 0.0357 33 640 0.4108 0.934 0.7874 0.4087 0.02631 0.004289 0
35/199 3.3G 0.03043 0.004985 0 0.03542 23 640 0.4728 0.9302 0.7158 0.3437 0.02809 0.004243 0
36/199 3.3G 0.03085 0.005052 0 0.0359 25 640 0.5064 0.934 0.6968 0.3601 0.02662 0.004156 0
37/199 3.3G 0.03107 0.004962 0 0.03603 30 640 0.5471 0.9331 0.6631 0.3393 0.02687 0.004181 0
38/199 3.3G 0.0302 0.005068 0 0.03527 25 640 0.4498 0.9389 0.6631 0.3441 0.02677 0.004436 0
39/199 3.3G 0.03 0.004937 0 0.03494 44 640 0.482 0.9486 0.6834 0.3479 0.02686 0.004162 0
40/199 3.3G 0.02948 0.004944 0 0.03442 32 640 0.5321 0.9447 0.6776 0.3347 0.02733 0.003987 0
41/199 3.3G 0.03045 0.004915 0 0.03537 42 640 0.5403 0.9437 0.7115 0.3667 0.02659 0.004031 0
42/199 3.3G 0.02951 0.004863 0 0.03438 16 640 0.5904 0.9331 0.7245 0.3792 0.02647 0.003958 0
43/199 3.3G 0.02914 0.004947 0 0.03409 41 640 0.5578 0.9321 0.7147 0.3757 0.02657 0.004088 0
44/199 3.3G 0.02966 0.004966 0 0.03463 53 640 0.5185 0.9418 0.6943 0.3673 0.02632 0.004156 0
45/199 3.3G 0.03036 0.004946 0 0.0353 32 640 0.5043 0.9418 0.6966 0.3384 0.02681 0.004081 0
46/199 3.3G 0.03082 0.004859 0 0.03568 36 640 0.5462 0.9486 0.6883 0.3641 0.02597 0.003992 0
47/199 3.3G 0.03015 0.004798 0 0.03495 14 640 0.5384 0.9399 0.7446 0.3895 0.02604 0.003975 0
48/199 3.3G 0.03088 0.004872 0 0.03575 32 640 0.5463 0.9457 0.7038 0.3755 0.02633 0.003913 0
49/199 3.3G 0.02959 0.004856 0 0.03444 35 640 0.4036 0.9486 0.6878 0.3062 0.02923 0.004473 0
50/199 3.3G 0.02933 0.004777 0 0.03411 34 640 0.5923 0.9379 0.6666 0.3554 0.02578 0.004013 0
51/199 3.3G 0.02908 0.004767 0 0.03385 29 640 0.5629 0.9408 0.7229 0.3941 0.02611 0.003913 0
52/199 3.3G 0.02893 0.004808 0 0.03374 33 640 0.4954 0.9437 0.6713 0.355 0.02588 0.004237
基于yolov5算法实现红外热成像人形识别检测源码+模型文件+评估指标曲线+使用说明.zip
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基于yolov5算法实现红外热成像人形识别检测源码+模型文件+评估指标曲线+使用说明.zip （78个子文件） 
基于yolov5算法实现红外热成像人形识别检测源码+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_hongwai_person opt.yaml 469B
results.png 207KB test_batch0_pred.jpg 231KB train_batch1.jpg 261KB hyp.yaml 356B labels.png 393KB precision-recall_curve.png 40KB test_batch0_gt.jpg 229KB train_batch2.jpg 257KB train_batch0.jpg 262KB weights best.pt 14.1MB last.pt 14.1MB results.txt 29KB 使用说明.txt 1KB资源评论
m0_711577052024-04-27感谢大佬分享的资源给了我灵感，果断支持！感谢分享~
