0/199 5.51G 0.1073 0.0373 0.02152 0.1661 101 640 0.07267 0.4602 0.1566 0.03113 0.0829 0.03484 0.007563
1/199 5.5G 0.07996 0.02738 0.007438 0.1148 59 640 0.06386 0.4904 0.249 0.08068 0.07274 0.02475 0.004912
2/199 5.5G 0.07205 0.02287 0.005353 0.1003 69 640 0.1457 0.6301 0.2885 0.1082 0.06449 0.0213 0.004275
3/199 5.5G 0.06458 0.02092 0.004851 0.09035 73 640 0.2459 0.7153 0.435 0.175 0.05115 0.02019 0.004015
4/199 5.5G 0.05754 0.01958 0.004645 0.08176 63 640 0.4178 0.8665 0.6637 0.346 0.0409 0.01832 0.003792
5/199 5.5G 0.05189 0.01884 0.004472 0.0752 109 640 0.4653 0.8075 0.6009 0.3556 0.03773 0.01721 0.003672
6/199 5.5G 0.04762 0.01707 0.003917 0.0686 77 640 0.5113 0.8596 0.714 0.3697 0.03714 0.01672 0.003626
7/199 5.5G 0.04438 0.0166 0.003723 0.06471 71 640 0.4742 0.8826 0.7491 0.3521 0.03442 0.01531 0.003417
8/199 5.5G 0.04196 0.01606 0.003738 0.06176 70 640 0.5388 0.9417 0.8256 0.4502 0.03039 0.01469 0.003303
9/199 5.5G 0.0399 0.01565 0.003894 0.05945 79 640 0.6144 0.9324 0.8875 0.4886 0.02787 0.0138 0.003189
10/199 5.5G 0.0398 0.01577 0.003713 0.05929 62 640 0.6374 0.853 0.7897 0.3457 0.03761 0.0213 0.003943
11/199 5.5G 0.04748 0.0171 0.003987 0.06857 80 640 0.6839 0.9358 0.9026 0.5017 0.0336 0.01518 0.003432
12/199 5.5G 0.04599 0.01599 0.00403 0.06601 61 640 0.5924 0.9651 0.8905 0.5207 0.02902 0.01455 0.003427
13/199 5.5G 0.04155 0.01535 0.003717 0.06062 66 640 0.6506 0.9557 0.9019 0.4987 0.03084 0.01422 0.003359
14/199 5.5G 0.04131 0.01511 0.003738 0.06015 69 640 0.6623 0.968 0.9321 0.5347 0.02946 0.01405 0.003264
15/199 5.5G 0.03972 0.01543 0.003876 0.05903 62 640 0.6647 0.9115 0.8511 0.4652 0.03141 0.01384 0.003272
16/199 5.5G 0.03698 0.01482 0.003686 0.05548 79 640 0.6831 0.9519 0.9126 0.5262 0.02993 0.01329 0.003104
17/199 5.5G 0.03532 0.01446 0.00349 0.05327 77 640 0.6958 0.9569 0.9215 0.5102 0.029 0.01281 0.003116
18/199 5.5G 0.03543 0.01463 0.003646 0.05371 88 640 0.679 0.942 0.9063 0.5124 0.0298 0.01286 0.003061
19/199 5.5G 0.03363 0.01397 0.003282 0.05089 91 640 0.765 0.9416 0.9285 0.5973 0.02465 0.0122 0.002963
20/199 5.5G 0.03272 0.01418 0.003109 0.05 65 640 0.6961 0.9763 0.9507 0.6061 0.02324 0.0122 0.002902
21/199 5.5G 0.03137 0.01379 0.003225 0.04838 73 640 0.7002 0.958 0.9331 0.557 0.02357 0.01227 0.002907
22/199 5.5G 0.02941 0.01346 0.002746 0.04561 110 640 0.7145 0.9393 0.9034 0.5235 0.02574 0.01198 0.002758
23/199 5.5G 0.02844 0.01312 0.002691 0.04425 61 640 0.7538 0.9641 0.9451 0.5865 0.02476 0.01212 0.002733
24/199 5.5G 0.02794 0.01307 0.003061 0.04407 77 640 0.7917 0.9625 0.953 0.6104 0.02222 0.01142 0.002723
25/199 5.5G 0.02666 0.01266 0.003206 0.04253 73 640 0.7684 0.967 0.9545 0.605 0.02178 0.0112 0.002701
26/199 5.5G 0.02568 0.01208 0.003123 0.04089 69 640 0.8077 0.9574 0.9495 0.624 0.0207 0.01111 0.002672
27/199 5.5G 0.02489 0.01271 0.003188 0.04079 104 640 0.8295 0.9738 0.9647 0.6792 0.01927 0.01076 0.002675
28/199 5.5G 0.02553 0.01246 0.00322 0.0412 88 640 0.8346 0.963 0.9547 0.6253 0.02178 0.01121 0.002647
29/199 5.5G 0.02517 0.01259 0.003102 0.04087 78 640 0.8061 0.9808 0.9713 0.672 0.0198 0.0112 0.002641
30/199 5.5G 0.02418 0.01201 0.003134 0.03932 93 640 0.8159 0.9818 0.9703 0.7031 0.01792 0.01055 0.002608
31/199 5.5G 0.02318 0.01209 0.002887 0.03816 103 640 0.8184 0.9739 0.9651 0.6684 0.01984 0.01075 0.002557
32/199 5.5G 0.02278 0.01198 0.003154 0.03792 77 640 0.8038 0.9758 0.963 0.6675 0.01784 0.01002 0.002556
33/199 5.5G 0.02251 0.01201 0.002631 0.03714 105 640 0.8109 0.9696 0.9601 0.6539 0.01886 0.01035 0.002519
34/199 5.5G 0.02223 0.01127 0.002827 0.03633 94 640 0.8291 0.9715 0.9619 0.68 0.01925 0.01038 0.002522
35/199 5.5G 0.02253 0.01173 0.002551 0.03681 101 640 0.8282 0.9718 0.9639 0.6711 0.01869 0.01046 0.002508
36/199 5.5G 0.0217 0.01161 0.003003 0.03631 68 640 0.8083 0.9629 0.9542 0.6303 0.02012 0.01041 0.002487
37/199 5.5G 0.02159 0.01134 0.002782 0.03571 74 640 0.8345 0.9639 0.9562 0.6158 0.0204 0.01058 0.002517
38/199 5.5G 0.0209 0.01127 0.002866 0.03504 69 640 0.8467 0.9746 0.9685 0.6845 0.01824 0.01024 0.00247
39/199 5.5G 0.02125 0.01112 0.003147 0.03552 88 640 0.8506 0.9796 0.972 0.6843 0.01783 0.01013 0.002489
40/199 5.5G 0.02107 0.01079 0.003228 0.03509 75 640 0.8408 0.9801 0.9726 0.6899 0.01788 0.01008 0.00249
41/199 5.5G 0.01984 0.0107 0.003184 0.03373 104 640 0.8264 0.9798 0.9714 0.6794 0.01733 0.00976 0.002449
42/199 5.5G 0.02049 0.01088 0.002656 0.03402 81 640 0.8543 0.9813 0.9755 0.7083 0.01679 0.009795 0.002432
43/199 5.5G 0.02056 0.01077 0.003152 0.03449 65 640 0.8321 0.9784 0.9689 0.6924 0.01729 0.009819 0.002442
44/199 5.5G 0.01972 0.01073 0.002701 0.03315 93 640 0.8579 0.9772 0.9709 0.6958 0.01726 0.009836 0.002438
45/199 5.5G 0.02028 0.01094 0.002755 0.03397 91 640 0.8654 0.9729 0.9682 0.6935 0.01723 0.009874 0.002422
46/199 5.5G 0.01876 0.01054 0.002466 0.03176 107 640 0.8726 0.9763 0.9722 0.6873 0.01729 0.009513 0.002389
47/199 5.5G 0.01985 0.01087 0.002883 0.0336 116 640 0.8725 0.9803 0.9731 0.6871 0.0172 0.00981 0.002432
48/199 5.5G 0.0197 0.01091 0.002649 0.03327 64 640 0.834 0.9796 0.9738 0.7034 0.01685 0.009948 0.00237
49/199 5.5G 0.01952 0.01066 0.002617 0.03279 88 640 0.8404 0.9755 0.9694 0.6924 0.01676 0.009517 0.002365
50/199 5.5G 0.01912 0.01061 0.002854 0.03259 97 640 0.8458 0.9822 0.9765 0.7183 0.01591 0.009379 0.002347
51/199 5.5G 0.01835 0.01042 0.002469 0.03124 89 640 0.8601 0.9818 0.9773 0.7302 0.01587 0.009428 0.002314
52/199 5.5G 0.01879 0.01039 0.003126 0.03231 76 640 0.844 0.9742 0.9693 0.6886 0.01726 0.009644 0.00233
yolov5检测源码+车辆车牌检测模型(监控视角)+使用说明.zip （79个子文件） 
yolov5检测源码+车辆车牌检测模型(监控视角)+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 使用说明.txt 439B exp_car_plat_jiankong opt.yaml 444B
results.png 214KB test_batch0_pred.jpg 200KB train_batch1.jpg 248KB hyp.yaml 356B labels.png 352KB precision-recall_curve.png 38KB test_batch0_gt.jpg 196KB train_batch2.jpg 276KB labels_correlogram.png 516KB train_batch0.jpg 278KB weights best.pt 41.32MB last.pt 41.32MB results.txt 29KB资源评论
MA_Zero2024-05-17资源值得借鉴的内容很多，那就浅学一下吧，值得下载！
onnx2024-11-01undefined
xhnwhhh2024-04-27资源内容详细，总结地很全面，与描述的内容一致，对我启发很大，学习了。 onnx2024-05-10谢谢认可和支持！
看、风吹落叶2023-03-11总算找到了想要的资源，搞定遇到的大问题，赞赞赞！ onnx2024-05-10嗯嗯，加油啊啊
