0/199 2.77G 0.09184 0.0838 0 0.1756 73 640 0 0 0.01002 0.001669 0.08906 0.06611 0
1/199 3.93G 0.06719 0.0675 0 0.1347 55 640 0.4459 0.506 0.4244 0.1357 0.05869 0.06258 0
2/199 3.93G 0.05919 0.06108 0 0.1203 52 640 0.4129 0.8104 0.6161 0.2428 0.04987 0.05064 0
3/199 3.93G 0.05382 0.05845 0 0.1123 81 640 0.3845 0.8243 0.5667 0.2094 0.05111 0.04675 0
4/199 3.93G 0.04978 0.05741 0 0.1072 49 640 0.565 0.7207 0.6383 0.2762 0.04872 0.05278 0
5/199 3.93G 0.04718 0.05534 0 0.1025 56 640 0.3701 0.8741 0.7168 0.3107 0.04768 0.04402 0
6/199 3.93G 0.04529 0.05478 0 0.1001 57 640 0.572 0.8221 0.7369 0.3293 0.04444 0.04767 0
7/199 3.93G 0.04372 0.05416 0 0.09788 68 640 0.4874 0.8221 0.6976 0.2786 0.04665 0.04602 0
8/199 3.93G 0.04203 0.05309 0 0.09511 81 640 0.5083 0.7987 0.6915 0.3135 0.04502 0.04826 0
9/199 3.93G 0.04121 0.05409 0 0.0953 67 640 0.4464 0.8865 0.7632 0.3686 0.04153 0.04529 0
10/199 3.93G 0.0402 0.05314 0 0.09335 61 640 0.5988 0.8075 0.7339 0.3609 0.04217 0.04745 0
11/199 3.93G 0.03967 0.05327 0 0.09294 70 640 0.4126 0.8095 0.6516 0.2827 0.04384 0.05265 0
12/199 3.93G 0.03883 0.05356 0 0.09239 56 640 0.4473 0.8843 0.7364 0.3744 0.03938 0.04597 0
13/199 3.93G 0.03789 0.0525 0 0.0904 80 640 0.5558 0.8734 0.7906 0.4123 0.03876 0.04462 0
14/199 3.93G 0.03763 0.05182 0 0.08945 56 640 0.4767 0.8777 0.7842 0.4272 0.03757 0.04421 0
15/199 3.93G 0.03661 0.05132 0 0.08792 49 640 0.6084 0.8712 0.7869 0.4258 0.03846 0.04373 0
16/199 3.93G 0.03573 0.04994 0 0.08566 63 640 0.6648 0.8616 0.8131 0.4358 0.03698 0.04577 0
17/199 3.93G 0.03545 0.05007 0 0.08552 73 640 0.6806 0.8558 0.82 0.4626 0.03645 0.0439 0
18/199 3.93G 0.03489 0.04911 0 0.08401 57 640 0.6607 0.8426 0.799 0.4399 0.03674 0.04549 0
19/199 3.93G 0.03452 0.04915 0 0.08367 52 640 0.5281 0.8997 0.817 0.4597 0.0365 0.04304 0
20/199 3.93G 0.03433 0.04915 0 0.08348 63 640 0.5573 0.894 0.829 0.4815 0.03489 0.0415 0
21/199 3.93G 0.03409 0.04949 0 0.08358 61 640 0.5423 0.9019 0.8036 0.4621 0.03517 0.04171 0
22/199 3.93G 0.03333 0.04769 0 0.08102 57 640 0.6901 0.8309 0.7966 0.4438 0.03675 0.04609 0
23/199 3.93G 0.0327 0.04762 0 0.08032 70 640 0.4672 0.937 0.8103 0.4726 0.03382 0.04273 0
24/199 3.93G 0.03266 0.04764 0 0.0803 71 640 0.6258 0.8985 0.8368 0.4829 0.03421 0.04211 0
25/199 3.93G 0.03293 0.04778 0 0.08071 56 640 0.4478 0.9356 0.8126 0.4903 0.03347 0.04323 0
26/199 3.93G 0.03267 0.04725 0 0.07992 79 640 0.5515 0.9107 0.8165 0.4716 0.0346 0.04208 0
27/199 3.93G 0.03222 0.04665 0 0.07886 54 640 0.7399 0.8199 0.8192 0.4642 0.03457 0.04713 0
28/199 3.93G 0.03181 0.04604 0 0.07785 69 640 0.6334 0.9026 0.8367 0.4947 0.03329 0.04134 0
29/199 3.93G 0.03197 0.04574 0 0.07771 53 640 0.6149 0.8902 0.8342 0.5028 0.03322 0.04168 0
30/199 3.93G 0.03154 0.04598 0 0.07752 57 640 0.64 0.8843 0.8345 0.4962 0.03378 0.04175 0
31/199 3.93G 0.03165 0.04581 0 0.07746 68 640 0.4819 0.9414 0.8096 0.4851 0.03253 0.04269 0
32/199 3.93G 0.03136 0.0459 0 0.07727 56 640 0.6662 0.8917 0.8258 0.5015 0.03241 0.04141 0
33/199 3.93G 0.03079 0.04565 0 0.07644 56 640 0.6077 0.8939 0.8247 0.4926 0.03338 0.04178 0
34/199 3.93G 0.03095 0.04602 0 0.07697 53 640 0.641 0.8946 0.8445 0.5084 0.03293 0.04128 0
35/199 3.93G 0.03076 0.04503 0 0.07579 71 640 0.6343 0.888 0.8429 0.4924 0.03325 0.04331 0
36/199 3.93G 0.03041 0.04516 0 0.07557 61 640 0.6145 0.91 0.8449 0.5059 0.03216 0.04067 0
37/199 3.93G 0.03043 0.04443 0 0.07487 52 640 0.6507 0.8785 0.8261 0.4941 0.03325 0.04281 0
38/199 3.93G 0.0301 0.04433 0 0.07443 64 640 0.6258 0.8968 0.8405 0.5033 0.03296 0.04069 0
39/199 3.93G 0.03012 0.04385 0 0.07398 73 640 0.5769 0.9187 0.8522 0.521 0.03182 0.04017 0
40/199 3.93G 0.03001 0.04415 0 0.07417 60 640 0.6078 0.9114 0.8255 0.507 0.03193 0.04116 0
41/199 3.93G 0.0297 0.04502 0 0.07472 78 640 0.6503 0.8946 0.8346 0.5127 0.03245 0.04095 0
42/199 3.93G 0.02937 0.04465 0 0.07402 79 640 0.7374 0.8543 0.85 0.5196 0.03232 0.04409 0
43/199 3.93G 0.02959 0.04386 0 0.07345 71 640 0.7032 0.8631 0.839 0.5111 0.03186 0.0432 0
44/199 3.93G 0.02934 0.0436 0 0.07295 70 640 0.6215 0.9004 0.8392 0.5295 0.03158 0.04156 0
45/199 3.93G 0.02905 0.04294 0 0.07199 66 640 0.6699 0.877 0.8265 0.5086 0.03253 0.04331 0
46/199 3.93G 0.02916 0.04253 0 0.07169 57 640 0.6482 0.8946 0.8436 0.5175 0.03147 0.04121 0
47/199 3.93G 0.02886 0.04327 0 0.07213 60 640 0.615 0.918 0.82 0.5097 0.03132 0.04127 0
48/199 3.93G 0.02889 0.04307 0 0.07196 68 640 0.5536 0.9283 0.8311 0.5237 0.03079 0.04122 0
49/199 3.93G 0.0286 0.04269 0 0.07129 65 640 0.6395 0.9019 0.8375 0.5101 0.0315 0.0413 0
50/199 3.93G 0.02889 0.04258 0 0.07146 49 640 0.6662 0.9 0.8596 0.5318 0.03136 0.04046 0
51/199 3.93G 0.02852 0.04166 0 0.07018 61 640 0.678 0.8939 0.8545 0.5321 0.03131 0.04112 0
52/199 3.93G 0.02829 0.0424 0 0.07069 61 640 0.671 0.9012 0.8462 0.5224 0.03132 0.04057
基于yolov5算法实现电动自行车识别检测源码+模型文件(5000多张数据集训练)+评估指标曲线+使用说明.7z
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基于yolov5算法实现电动自行车识别检测源码+模型文件+评估指标曲线+使用说明.7z （77个子文件） 
基于yolov5算法实现电动自行车识别检测源码+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 使用说明.txt 1KB exp_Ebike opt.yaml 451B
results.png 196KB test_batch0_pred.jpg 454KB train_batch1.jpg 733KB hyp.yaml 276B labels.png 451KB test_batch0_gt.jpg 479KB train_batch2.jpg 643KB train_batch0.jpg 736KB weights best.pt 10.7MB last.pt 10.7MB results.txt 29KB资源评论
tf07192024-04-12资源中能够借鉴的内容很多，值得学习的地方也很多，大家一起进步！- GW7862288362023-01-10感谢资源主分享的资源解决了我当下的问题，非常有用的资源。
Make程序设计2023-01-11感谢老铁支持！实际项目所用，很好的数据训练的模型。放心使用~
