├── README.md ├── datasets └── geetest │ ├── LICENSE │ ├── images │ └── train2017 │ │ ├── 1.jpg │ │ ├── 10.jpg │ │ ├── 100.jpg │ │ ├── 11.jpg │ │ ├── 12.jpg │ │ ├── 13.jpg │ │ ├── 14.jpg │ │ ├── 15.jpg │ │ ├── 16.jpg │ │ ├── 17.jpg │ │ ├── 18.jpg │ │ ├── 19.jpg │ │ ├── 2.jpg │ │ ├── 20.jpg │ │ ├── 21.jpg │ │ ├── 22.jpg │ │ ├── 23.jpg │ │ ├── 24.jpg │ │ ├── 25.jpg │ │ ├── 26.jpg │ │ ├── 27.jpg │ │ ├── 28.jpg │ │ ├── 29.jpg │ │ ├── 3.jpg │ │ ├── 30.jpg │ │ ├── 31.jpg │ │ ├── 32.jpg │ │ ├── 33.jpg │ │ ├── 34.jpg │ │ ├── 35.jpg │ │ ├── 36.jpg │ │ ├── 37.jpg │ │ ├── 38.jpg │ │ ├── 39.jpg │ │ ├── 4.jpg │ │ ├── 40.jpg │ │ ├── 41.jpg │ │ ├── 42.jpg │ │ ├── 43.jpg │ │ ├── 44.jpg │ │ ├── 45.jpg │ │ ├── 46.jpg │ │ ├── 47.jpg │ │ ├── 48.jpg │ │ ├── 49.jpg │ │ ├── 5.jpg │ │ ├── 50.jpg │ │ ├── 51.jpg │ │ ├── 52.jpg │ │ ├── 53.jpg │ │ ├── 54.jpg │ │ ├── 55.jpg │ │ ├── 56.jpg │ │ ├── 57.jpg │ │ ├── 58.jpg │ │ ├── 59.jpg │ │ ├── 6.jpg │ │ ├── 60.jpg │ │ ├── 61.jpg │ │ ├── 62.jpg │ │ ├── 63.jpg │ │ ├── 64.jpg │ │ ├── 65.jpg │ │ ├── 66.jpg │ │ ├── 67.jpg │ │ ├── 68.jpg │ │ ├── 69.jpg │ │ ├── 7.jpg │ │ ├── 70.jpg │ │ ├── 71.jpg │ │ ├── 72.jpg │ │ ├── 73.jpg │ │ ├── 74.jpg │ │ ├── 75.jpg │ │ ├── 76.jpg │ │ ├── 77.jpg │ │ ├── 78.jpg │ │ ├── 79.jpg │ │ ├── 8.jpg │ │ ├── 80.jpg │ │ ├── 81.jpg │ │ ├── 82.jpg │ │ ├── 83.jpg │ │ ├── 84.jpg │ │ ├── 85.jpg │ │ ├── 86.jpg │ │ ├── 87.jpg │ │ ├── 88.jpg │ │ ├── 89.jpg │ │ ├── 9.jpg │ │ ├── 90.jpg │ │ ├── 91.jpg │ │ ├── 92.jpg │ │ ├── 93.jpg │ │ ├── 94.jpg │ │ ├── 95.jpg │ │ ├── 96.jpg │ │ ├── 97.jpg │ │ ├── 98.jpg │ │ └── 99.jpg │ └── labels │ └── train2017 │ ├── 1.txt │ ├── 10.txt │ ├── 100.txt │ ├── 11.txt │ ├── 12.txt │ ├── 13.txt │ ├── 14.txt │ ├── 15.txt │ ├── 16.txt │ ├── 17.txt │ ├── 18.txt │ ├── 19.txt │ ├── 2.txt │ ├── 20.txt │ ├── 21.txt │ ├── 22.txt │ ├── 23.txt │ ├── 24.txt │ ├── 25.txt │ ├── 26.txt │ ├── 27.txt │ ├── 28.txt │ ├── 29.txt │ ├── 3.txt │ ├── 30.txt │ ├── 31.txt │ ├── 32.txt │ ├── 33.txt │ ├── 34.txt │ ├── 35.txt │ ├── 36.txt │ ├── 37.txt │ ├── 38.txt │ ├── 39.txt │ ├── 4.txt │ ├── 40.txt │ ├── 41.txt │ ├── 42.txt │ ├── 43.txt │ ├── 44.txt │ ├── 45.txt │ ├── 46.txt │ ├── 47.txt │ ├── 48.txt │ ├── 49.txt │ ├── 5.txt │ ├── 50.txt │ ├── 51.txt │ ├── 52.txt │ ├── 53.txt │ ├── 54.txt │ ├── 55.txt │ ├── 56.txt │ ├── 57.txt │ ├── 58.txt │ ├── 59.txt │ ├── 6.txt │ ├── 60.txt │ ├── 61.txt │ ├── 62.txt │ ├── 63.txt │ ├── 64.txt │ ├── 65.txt │ ├── 66.txt │ ├── 67.txt │ ├── 68.txt │ ├── 69.txt │ ├── 7.txt │ ├── 70.txt │ ├── 71.txt │ ├── 72.txt │ ├── 73.txt │ ├── 74.txt │ ├── 75.txt │ ├── 76.txt │ ├── 77.txt │ ├── 78.txt │ ├── 79.txt │ ├── 8.txt │ ├── 80.txt │ ├── 81.txt │ ├── 82.txt │ ├── 83.txt │ ├── 84.txt │ ├── 85.txt │ ├── 86.txt │ ├── 87.txt │ ├── 88.txt │ ├── 89.txt │ ├── 9.txt │ ├── 90.txt │ ├── 91.txt │ ├── 92.txt │ ├── 93.txt │ ├── 94.txt │ ├── 95.txt │ ├── 96.txt │ ├── 97.txt │ ├── 98.txt │ └── 99.txt ├── img ├── 1.jpg ├── 2.jpg ├── 3.jpg ├── 4.jpg └── 5.jpg └── yolov5 ├── .pre-commit-config.yaml ├── benchmarks.py ├── best.pt ├── data ├── geetest.yaml ├── hyps │ ├── hyp.Objects365.yaml │ ├── hyp.VOC.yaml │ ├── hyp.scratch-high.yaml │ ├── hyp.scratch-low.yaml │ └── hyp.scratch-med.yaml ├── images │ └── 6.jpg └── scripts │ ├── download_weights.sh │ ├── get_coco.sh │ ├── get_coco128.sh │ └── get_imagenet.sh ├── detect.py ├── export.py ├── hubconf.py ├── models ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── common.cpython-37.pyc │ ├── experimental.cpython-37.pyc │ └── yolo.cpython-37.pyc ├── common.py ├── experimental.py ├── hub │ ├── anchors.yaml │ ├── yolov3-spp.yaml │ ├── yolov3-tiny.yaml │ ├── yolov3.yaml │ ├── yolov5-bifpn.yaml │ ├── yolov5-fpn.yaml │ ├── yolov5-p2.yaml │ ├── yolov5-p34.yaml │ ├── yolov5-p6.yaml │ ├── yolov5-p7.yaml │ ├── yolov5-panet.yaml │ ├── yolov5l6.yaml │ ├── yolov5m6.yaml │ ├── yolov5n6.yaml │ ├── yolov5s-LeakyReLU.yaml │ ├── yolov5s-ghost.yaml │ ├── yolov5s-transformer.yaml │ ├── yolov5s6.yaml │ └── yolov5x6.yaml ├── segment │ ├── yolov5l-seg.yaml │ ├── yolov5m-seg.yaml │ ├── yolov5n-seg.yaml │ ├── yolov5s-seg.yaml │ └── yolov5x-seg.yaml ├── tf.py ├── yolo.py ├── yolov5l.yaml ├── yolov5m.yaml ├── yolov5n.yaml ├── yolov5s.yaml └── yolov5x.yaml ├── requirements.txt ├── segment ├── predict.py ├── train.py └── val.py ├── setup.cfg ├── train.py ├── tutorial.ipynb ├── utils ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── augmentations.cpython-37.pyc │ ├── autoanchor.cpython-37.pyc │ ├── autobatch.cpython-37.pyc │ ├── callbacks.cpython-37.pyc │ ├── dataloaders.cpython-37.pyc │ ├── downloads.cpython-37.pyc │ ├── general.cpython-37.pyc │ ├── loss.cpython-37.pyc │ ├── metrics.cpython-37.pyc │ ├── plots.cpython-37.pyc │ └── torch_utils.cpython-37.pyc ├── activations.py ├── augmentations.py ├── autoanchor.py ├── autobatch.py ├── aws │ ├── __init__.py │ ├── mime.sh │ ├── resume.py │ └── userdata.sh ├── callbacks.py ├── dataloaders.py ├── docker │ ├── Dockerfile │ ├── Dockerfile-arm64 │ └── Dockerfile-cpu ├── downloads.py ├── flask_rest_api │ ├── README.md │ ├── example_request.py │ └── restapi.py ├── general.py ├── google_app_engine │ ├── Dockerfile │ ├── additional_requirements.txt │ └── app.yaml ├── loggers │ ├── __init__.py │ ├── __pycache__ │ │ └── __init__.cpython-37.pyc │ ├── clearml │ │ ├── README.md │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ └── clearml_utils.cpython-37.pyc │ │ ├── clearml_utils.py │ │ └── hpo.py │ ├── comet │ │ ├── README.md │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ └── comet_utils.cpython-37.pyc │ │ ├── comet_utils.py │ │ ├── hpo.py │ │ └── optimizer_config.json │ └── wandb │ │ ├── README.md │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── wandb_utils.cpython-37.pyc │ │ ├── log_dataset.py │ │ ├── sweep.py │ │ ├── sweep.yaml │ │ └── wandb_utils.py ├── loss.py ├── metrics.py ├── plots.py ├── segment │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── general.cpython-37.pyc │ ├── augmentations.py │ ├── dataloaders.py │ ├── general.py │ ├── loss.py │ ├── metrics.py │ └── plots.py ├── torch_utils.py └── triton.py ├── val.py └── yolov5s.pt /README.md: -------------------------------------------------------------------------------- 1 | # GeetestCrack 2 | 基于yolov5的极验空间推理验证码模型训练 3 | ## 测试接口 4 | 接口随时都可能会关掉，如果不通就是被我关掉了。
5 | 测试接口：`http://49.234.3.186:8888/index`
6 | 限制每天500次请求，每小时100次请求，每分钟60次请求。（总计）
7 | **该接口仅用于学术交流，不得任何商业使用！** 8 | ### GET请求参数 9 | 参数 | 值 | 10 | ---- | ---- | 11 | gt | c9428d9361cd70d26e28d7cd780ec640 | 12 | challenge | 9a5d3a1db9dadbaa867f6a34a5512740 | 13 | 14 | ### 请求成功示例 15 | ```json 16 | { 17 | "msg":[], 18 | "result":"success", 19 | "score":"1", 20 | "validate":"ac00fdb3bc74b012c3b82cca057675fa" #使用challenge和validate一起提交验证即可 21 | } 22 | ``` 23 | ## 验证码问题求解思路 24 | 训练模型比较简单，我自己标记了100张图的标签，可以直接训练，也可以直接用我训练好的权重文件`best.pt`，训练了大概700次左右，感觉效果还是不错的，我认为比较麻烦的部分还是分析问题找出符合条件的物体，我写出的逻辑识别成功率大概在80%，暂时不公开逻辑。
25 | `请点击在黄色圆柱体后面的黄色物体。`判断前后左右，可以根据物体底部的xy坐标。
26 | `请点击与绿色物品有相同大小的球。`判断相同大小，可以对比符合条件的物体的二维面积，最相近的可以认为相同大小。
27 | 28 | ![image](https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/master/img/1.jpg) 29 | ![image](https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/master/img/2.jpg) 30 | ![image](https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/master/img/3.jpg) 31 | ![image](https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/master/img/4.jpg) 32 | ## 免责声明 33 | 34 | **该项目仅用于学术交流，不得任何商业使用！** 35 | -------------------------------------------------------------------------------- /datasets/geetest/images/train2017/1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/datasets/geetest/images/train2017/1.jpg -------------------------------------------------------------------------------- /datasets/geetest/images/train2017/10.jpg: -------------------------------------------------------------------------------- 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0.307760 0.181125 0.164021 4 | 7 0.319860 0.364198 0.104344 0.121693 5 | 24 0.374000 0.497354 0.189000 0.183422 6 | 14 0.737234 0.467372 0.202781 0.201058 7 | 18 0.966562 0.383598 0.066876 0.156966 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/10.txt: -------------------------------------------------------------------------------- 1 | 18 0.089547 0.373016 0.179094 0.164021 2 | 18 0.254891 0.500000 0.194906 0.171076 3 | 18 0.891780 0.328924 0.189000 0.171076 4 | 3 0.580719 0.209877 0.216562 0.246914 5 | 15 0.486219 0.439153 0.189000 0.165785 6 | 6 0.696875 0.313933 0.106312 0.126984 7 | 9 0.822874 0.463845 0.181125 0.208113 8 | 24 0.974437 0.427690 0.051126 0.100529 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/100.txt: -------------------------------------------------------------------------------- 1 | 3 0.286391 0.265432 0.269718 0.262787 2 | 2 0.659468 0.225750 0.244125 0.253968 3 | 0 0.840593 0.299824 0.275624 0.268078 4 | 12 0.471453 0.343915 0.190968 0.201058 5 | 17 0.590562 0.412698 0.189000 0.169312 6 | 12 0.730343 0.531746 0.122062 0.121693 7 | 14 0.411406 0.550265 0.204750 0.201058 8 | 13 0.076720 0.412698 0.126000 0.123457 9 | 15 0.182047 0.473545 0.108281 0.097002 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/11.txt: -------------------------------------------------------------------------------- 1 | 21 0.163344 0.397707 0.208687 0.185185 2 | 21 0.643718 0.335979 0.200812 0.181658 3 | 20 0.412391 0.578483 0.120094 0.116402 4 | 19 0.563984 0.486772 0.190968 0.165785 5 | 19 0.897687 0.476190 0.118125 0.105820 6 | 3 0.353328 0.327160 0.167343 0.160494 7 | 12 0.527562 0.305996 0.118125 0.114638 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/12.txt: -------------------------------------------------------------------------------- 1 | 22 0.143657 0.343915 0.200812 0.183422 2 | 22 0.553156 0.323633 0.196875 0.178131 3 | 20 0.409438 0.191358 0.196875 0.181658 4 | 5 0.299188 0.522046 0.177187 0.204586 5 | 0 0.694906 0.530864 0.153562 0.151675 6 | 15 0.837640 0.313933 0.108281 0.105820 7 | 15 0.921281 0.376543 0.157438 0.171076 8 | 16 0.639781 0.253086 0.106312 0.100529 9 | 11 0.209610 0.470899 0.108281 0.116402 10 | 12 0.474406 0.505291 0.122062 0.121693 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/13.txt: -------------------------------------------------------------------------------- 1 | 2 0.058047 0.405644 0.116094 0.158730 2 | 7 0.342500 0.203704 0.173250 0.209877 3 | 6 0.626000 0.328042 0.173250 0.211640 4 | 8 0.756921 0.418871 0.171281 0.206349 5 | 1 0.532484 0.158730 0.159468 0.155203 6 | 11 0.846499 0.301587 0.196875 0.194004 7 | 0 0.269657 0.384480 0.153562 0.155203 8 | 17 0.403531 0.461199 0.192937 0.174603 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/14.txt: -------------------------------------------------------------------------------- 1 | 19 0.180079 0.439153 0.194906 0.172840 2 | 21 0.414360 0.555556 0.198843 0.179894 3 | 12 0.561031 0.248677 0.200812 0.197531 4 | 11 0.370063 0.333333 0.118125 0.116402 5 | 2 0.810078 0.476190 0.257906 0.261023 6 | 6 0.659468 0.425044 0.098437 0.123457 7 | 6 0.961640 0.383598 0.076720 0.118166 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/15.txt: -------------------------------------------------------------------------------- 1 | 1 0.166298 0.417989 0.265781 0.261023 2 | 1 0.460625 0.187831 0.240187 0.248677 3 | 16 0.605328 0.310406 0.187031 0.176367 4 | 24 0.371047 0.492945 0.198843 0.178131 5 | 7 0.320844 0.364198 0.106312 0.128748 6 | 14 0.736250 0.467372 0.208687 0.201058 7 | 18 0.966562 0.380952 0.066876 0.155203 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/16.txt: -------------------------------------------------------------------------------- 1 | 16 0.063953 0.365961 0.127906 0.178131 2 | 18 0.683093 0.408289 0.192937 0.178131 3 | 4 0.527562 0.201940 0.185062 0.178131 4 | 6 0.179094 0.287478 0.177187 0.208113 5 | 5 0.403531 0.329806 0.177187 0.204586 6 | 1 0.748062 0.251323 0.228375 0.241623 7 | 13 0.267688 0.503527 0.122062 0.118166 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/17.txt: -------------------------------------------------------------------------------- 1 | 22 0.048204 0.340388 0.096407 0.186949 2 | 20 0.170235 0.403880 0.116156 0.105820 3 | 5 0.490156 0.184303 0.169312 0.209877 4 | 0 0.318875 0.317460 0.228375 0.246914 5 | 10 0.688015 0.298060 0.198843 0.197531 6 | 19 0.515750 0.495591 0.192937 0.169312 7 | 6 0.832718 0.477954 0.106312 0.130511 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/18.txt: -------------------------------------------------------------------------------- 1 | 17 0.041313 0.377425 0.082626 0.179894 2 | 8 0.467516 0.277778 0.108281 0.132275 3 | 8 0.727390 0.387125 0.175218 0.202822 4 | 8 0.904577 0.327160 0.175218 0.213404 5 | 21 0.562016 0.486772 0.120094 0.109347 6 | 24 0.662422 0.542328 0.120094 0.107584 7 | 5 0.250954 0.313933 0.179156 0.201058 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/19.txt: -------------------------------------------------------------------------------- 1 | 0 0.769718 0.270723 0.287437 0.266314 2 | 17 0.845515 0.416226 0.194906 0.176367 3 | 16 0.440938 0.537037 0.196875 0.174603 4 | 13 0.527562 0.276014 0.118125 0.114638 5 | 10 0.612219 0.362434 0.118125 0.121693 6 | 5 0.248985 0.488536 0.179156 0.208113 7 | 14 0.181063 0.398589 0.196875 0.197531 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/2.txt: -------------------------------------------------------------------------------- 1 | 5 0.080688 0.347443 0.161376 0.211640 2 | 9 0.253907 0.252205 0.177187 0.211640 3 | 24 0.390735 0.561728 0.198843 0.178131 4 | 15 0.578750 0.213404 0.189000 0.172840 5 | 15 0.942937 0.439153 0.114126 0.109347 6 | 7 0.647656 0.524691 0.173250 0.206349 7 | 11 0.157438 0.445326 0.212625 0.199295 8 | 17 0.461609 0.522046 0.112219 0.105820 9 | 14 0.809093 0.474427 0.196875 0.201058 10 | 12 0.732312 0.325397 0.196875 0.195767 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/20.txt: -------------------------------------------------------------------------------- 1 | 5 0.751015 0.261023 0.171281 0.204586 2 | 5 0.917374 0.434744 0.110250 0.125220 3 | 3 0.460625 0.527337 0.165375 0.155203 4 | 3 0.553156 0.261905 0.165375 0.149912 5 | 19 0.402547 0.283951 0.187031 0.176367 6 | 17 0.167282 0.348325 0.118125 0.111111 7 | 11 0.678172 0.471781 0.120094 0.121693 8 | 1 0.254891 0.492063 0.127969 0.144621 9 | 4 0.085579 0.397707 0.135843 0.146384 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/21.txt: -------------------------------------------------------------------------------- 1 | 16 0.153501 0.271605 0.196875 0.169312 2 | 11 0.099391 0.433862 0.198782 0.204586 3 | 21 0.626984 0.333333 0.198843 0.179894 4 | 21 0.547250 0.580247 0.118125 0.109347 5 | 8 0.496063 0.244268 0.173250 0.213404 6 | 9 0.879968 0.392416 0.177187 0.206349 7 | 3 0.294266 0.545855 0.151593 0.149912 8 | 1 0.392703 0.473545 0.171281 0.156966 9 | 15 0.748062 0.485891 0.189000 0.164021 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/22.txt: -------------------------------------------------------------------------------- 1 | 23 0.177126 0.280423 0.196875 0.183422 2 | 21 0.474406 0.339506 0.122062 0.107584 3 | 8 0.780546 0.454145 0.171281 0.209877 4 | 2 0.378922 0.538801 0.277593 0.269841 5 | 1 0.545281 0.187831 0.255937 0.252205 6 | 0 0.706718 0.328042 0.279562 0.261023 7 | 10 0.275563 0.422399 0.114187 0.121693 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/23.txt: -------------------------------------------------------------------------------- 1 | 4 0.309032 0.521164 0.232312 0.252205 2 | 9 0.250954 0.425926 0.179156 0.209877 3 | 19 0.585640 0.377425 0.187031 0.169312 4 | 11 0.774640 0.521164 0.198843 0.202822 5 | 11 0.515750 0.210758 0.110250 0.118166 6 | 17 0.432078 0.328924 0.112219 0.107584 7 | 21 0.921312 0.378307 0.122062 0.111111 8 | 0 0.737234 0.214286 0.253968 0.262787 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/24.txt: -------------------------------------------------------------------------------- 1 | 1 0.121047 0.312169 0.242094 0.257496 2 | 2 0.199766 0.440035 0.250031 0.259259 3 | 12 0.421250 0.283069 0.192937 0.199295 4 | 12 0.649625 0.417108 0.200812 0.202822 5 | 20 0.490156 0.626984 0.122062 0.114638 6 | 9 0.805156 0.504409 0.106312 0.126984 7 | 24 0.594500 0.257496 0.122062 0.109347 8 | 16 0.278516 0.548501 0.108281 0.098765 9 | 22 0.748062 0.296296 0.204750 0.179894 10 | 5 0.893749 0.402116 0.110250 0.119929 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/25.txt: -------------------------------------------------------------------------------- 1 | 7 0.056079 0.336861 0.112157 0.211640 2 | 6 0.273594 0.472663 0.106312 0.123457 3 | 7 0.571859 0.305996 0.175218 0.209877 4 | 4 0.429125 0.208995 0.216562 0.245150 5 | 18 0.334625 0.567901 0.122062 0.102293 6 | 9 0.493109 0.604056 0.108281 0.125220 7 | 17 0.777593 0.468254 0.192937 0.174603 8 | 13 0.970500 0.432981 0.059000 0.125220 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/26.txt: -------------------------------------------------------------------------------- 1 | 13 0.091516 0.355379 0.183032 0.202822 2 | 11 0.422235 0.386243 0.202781 0.201058 3 | 10 0.187954 0.476190 0.194906 0.197531 4 | 16 0.601390 0.215168 0.187031 0.169312 5 | 7 0.297219 0.562610 0.106312 0.123457 6 | 7 0.820906 0.298060 0.169312 0.208113 7 | 4 0.512797 0.589065 0.155531 0.155203 8 | 8 0.647656 0.524691 0.102375 0.128748 9 | 0 0.883906 0.427690 0.228375 0.255732 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/27.txt: -------------------------------------------------------------------------------- 1 | 17 0.688015 0.439153 0.194906 0.176367 2 | 17 0.788421 0.305115 0.112219 0.098765 3 | 18 0.892765 0.360670 0.187031 0.167549 4 | 13 0.084595 0.457672 0.114187 0.118166 5 | 13 0.508859 0.598765 0.198843 0.202822 6 | 12 0.626000 0.239859 0.196875 0.194004 7 | 9 0.037375 0.365961 0.074750 0.128748 8 | 1 0.337578 0.575838 0.163406 0.156966 9 | 2 0.417313 0.414462 0.165375 0.151675 10 | 15 0.286391 0.298060 0.190968 0.172840 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/28.txt: -------------------------------------------------------------------------------- 1 | 15 0.597453 0.557319 0.120094 0.105820 2 | 6 0.330688 0.498236 0.173250 0.216931 3 | 9 0.546266 0.421517 0.175218 0.215168 4 | 4 0.062969 0.375661 0.125938 0.155203 5 | 17 0.142673 0.315697 0.108281 0.105820 6 | 23 0.235204 0.371252 0.198843 0.185185 7 | 11 0.452750 0.218695 0.114187 0.119929 8 | 13 0.858312 0.490300 0.118125 0.119929 9 | 1 0.689000 0.277778 0.173250 0.156966 10 | 12 0.791374 0.335979 0.200812 0.209877 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/29.txt: -------------------------------------------------------------------------------- 1 | 3 0.084625 0.373016 0.169250 0.255732 2 | 11 0.342500 0.443563 0.204750 0.206349 3 | 5 0.584656 0.323633 0.173250 0.206349 4 | 5 0.692937 0.277778 0.102375 0.128748 5 | 10 0.507875 0.236332 0.192937 0.197531 6 | 23 0.478344 0.581129 0.196875 0.185185 7 | 21 0.657500 0.474427 0.204750 0.186949 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/3.txt: -------------------------------------------------------------------------------- 1 | 21 0.401563 0.361552 0.196875 0.183422 2 | 23 0.518703 0.227513 0.124031 0.112875 3 | 24 0.849452 0.318342 0.120094 0.111111 4 | 22 0.186969 0.477954 0.200812 0.179894 5 | 5 0.055094 0.315697 0.110188 0.208113 6 | 10 0.658484 0.470899 0.198843 0.201058 7 | 1 0.583672 0.555556 0.226406 0.246914 8 | 14 0.285407 0.284832 0.110250 0.114638 9 | 7 0.927187 0.411817 0.145626 0.206349 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/30.txt: -------------------------------------------------------------------------------- 1 | 20 0.130860 0.438272 0.120094 0.107584 2 | 4 0.227329 0.317460 0.163406 0.151675 3 | 2 0.265719 0.523810 0.145687 0.155203 4 | 12 0.379906 0.255732 0.200812 0.197531 5 | 12 0.573828 0.491182 0.198843 0.195767 6 | 14 0.820906 0.402998 0.126000 0.118166 7 | 1 0.475391 0.577601 0.269718 0.273369 8 | 9 0.721484 0.269841 0.175218 0.211640 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/31.txt: -------------------------------------------------------------------------------- 1 | 16 0.178110 0.305115 0.116156 0.105820 2 | 16 0.569890 0.301587 0.190968 0.172840 3 | 2 0.326750 0.468254 0.137812 0.149912 4 | 2 0.931125 0.371252 0.137750 0.255732 5 | 12 0.102344 0.425044 0.204688 0.201058 6 | 18 0.487203 0.641093 0.112219 0.104056 7 | 9 0.382860 0.245150 0.175218 0.211640 8 | 0 0.644703 0.520282 0.277593 0.271605 9 | 5 0.863234 0.462963 0.171281 0.206349 10 | 13 0.735265 0.402116 0.151593 0.141093 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/32.txt: -------------------------------------------------------------------------------- 1 | 1 0.178110 0.436508 0.269718 0.273369 2 | 2 0.679156 0.361552 0.157500 0.155203 3 | 2 0.762828 0.283951 0.151593 0.151675 4 | 14 0.587609 0.198413 0.183093 0.195767 5 | 8 0.427156 0.288360 0.173250 0.209877 6 | 18 0.316907 0.253968 0.114187 0.105820 7 | 6 0.371047 0.437390 0.108281 0.134039 8 | 0 0.505906 0.516755 0.248062 0.264550 9 | 0 0.958687 0.390653 0.082626 0.153439 10 | 21 0.859296 0.500000 0.120094 0.111111 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/33.txt: -------------------------------------------------------------------------------- 1 | 20 0.605328 0.540564 0.120094 0.111111 2 | 23 0.855359 0.432099 0.124031 0.109347 3 | 23 0.096438 0.438272 0.192876 0.185185 4 | 19 0.438969 0.567901 0.114187 0.105820 5 | 1 0.170235 0.320106 0.285468 0.266314 6 | 7 0.369078 0.191358 0.179156 0.213404 7 | 12 0.548234 0.163139 0.112219 0.118166 8 | 9 0.678172 0.370370 0.179156 0.208113 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/34.txt: -------------------------------------------------------------------------------- 1 | 3 0.959671 0.384480 0.080657 0.148148 2 | 23 0.116094 0.356261 0.118125 0.109347 3 | 13 0.508859 0.419753 0.206718 0.201058 4 | 14 0.319860 0.571429 0.120094 0.119929 5 | 12 0.584656 0.301587 0.118125 0.112875 6 | 15 0.414360 0.201058 0.187031 0.169312 7 | 20 0.697859 0.514109 0.124031 0.107584 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/35.txt: -------------------------------------------------------------------------------- 1 | 12 0.046235 0.381834 0.092470 0.192240 2 | 7 0.383844 0.606702 0.094500 0.119929 3 | 14 0.440938 0.509700 0.196875 0.194004 4 | 14 0.677187 0.470899 0.196875 0.197531 5 | 15 0.198782 0.284832 0.110250 0.100529 6 | 23 0.300172 0.349206 0.116156 0.098765 7 | 19 0.579734 0.253968 0.112219 0.095238 8 | 4 0.502953 0.141975 0.269718 0.255732 9 | 16 0.744125 0.278660 0.189000 0.162257 10 | 6 0.899655 0.398589 0.169312 0.208113 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/36.txt: -------------------------------------------------------------------------------- 1 | 16 0.341516 0.340388 0.187031 0.169312 2 | 13 0.500984 0.146384 0.187031 0.197531 3 | 18 0.588594 0.249559 0.185062 0.167549 4 | 23 0.668328 0.384480 0.198843 0.183422 5 | 17 0.860281 0.328924 0.098437 0.093474 6 | 7 0.737234 0.479718 0.175218 0.204586 7 | 1 0.911437 0.407407 0.177126 0.271605 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/37.txt: -------------------------------------------------------------------------------- 1 | 4 0.124985 0.281305 0.249969 0.259259 2 | 8 0.049188 0.411817 0.098376 0.121693 3 | 8 0.388766 0.570547 0.096469 0.121693 4 | 8 0.900640 0.409171 0.171281 0.204586 5 | 9 0.452750 0.619048 0.173250 0.208113 6 | 4 0.517719 0.381834 0.169312 0.153439 7 | 12 0.320844 0.475309 0.196875 0.199295 8 | 18 0.589578 0.202822 0.116156 0.098765 9 | 0 0.662422 0.498236 0.265781 0.269841 10 | 22 0.763812 0.253968 0.196875 0.176367 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/38.txt: -------------------------------------------------------------------------------- 1 | 7 0.086563 0.435626 0.169312 0.208113 2 | 11 0.188938 0.302469 0.196875 0.195767 3 | 1 0.294266 0.515873 0.257906 0.259259 4 | 19 0.375969 0.209877 0.181125 0.165785 5 | 0 0.474406 0.607584 0.153562 0.149912 6 | 0 0.735265 0.236332 0.242156 0.250441 7 | 6 0.925218 0.347443 0.149563 0.201058 8 | 17 0.516734 0.443563 0.183093 0.167549 9 | 23 0.677187 0.430335 0.118125 0.105820 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/39.txt: -------------------------------------------------------------------------------- 1 | 7 0.085579 0.464727 0.100406 0.125220 2 | 7 0.454719 0.302469 0.173250 0.206349 3 | 16 0.240126 0.348325 0.189000 0.167549 4 | 16 0.334625 0.465608 0.189000 0.162257 5 | 15 0.381875 0.604938 0.110250 0.098765 6 | 15 0.750031 0.268078 0.177187 0.162257 7 | 13 0.545281 0.246032 0.098437 0.107584 8 | 23 0.600406 0.398589 0.196875 0.169312 9 | 24 0.669312 0.528219 0.196875 0.178131 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/4.txt: -------------------------------------------------------------------------------- 1 | 10 0.172204 0.467372 0.202781 0.197531 2 | 24 0.431094 0.195767 0.196875 0.172840 3 | 22 0.502953 0.308642 0.194906 0.172840 4 | 20 0.219454 0.316578 0.112219 0.107584 5 | 20 0.934078 0.327160 0.131844 0.178131 6 | 21 0.418297 0.569665 0.198843 0.176367 7 | 4 0.303125 0.427690 0.157500 0.149912 8 | 19 0.689000 0.247795 0.110250 0.093474 9 | 23 0.648640 0.393298 0.116156 0.098765 10 | 6 0.588594 0.479718 0.177187 0.204586 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/40.txt: -------------------------------------------------------------------------------- 1 | 7 0.359235 0.216049 0.175218 0.202822 2 | 7 0.529531 0.317460 0.106312 0.112875 3 | 2 0.424203 0.392416 0.269718 0.266314 4 | 18 0.208626 0.368607 0.114187 0.095238 5 | 24 0.286391 0.473545 0.190968 0.178131 6 | 12 0.488188 0.584656 0.118125 0.118166 7 | 4 0.595484 0.477954 0.155531 0.151675 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/41.txt: -------------------------------------------------------------------------------- 1 | 14 0.027532 0.429453 0.055063 0.118166 2 | 18 0.164329 0.503527 0.104344 0.097002 3 | 20 0.227329 0.293651 0.194906 0.178131 4 | 6 0.382860 0.232804 0.108281 0.119929 5 | 8 0.491141 0.627866 0.096469 0.119929 6 | 9 0.554141 0.514991 0.171281 0.204586 7 | 19 0.670297 0.292769 0.112219 0.095238 8 | 17 0.740187 0.423280 0.189000 0.162257 9 | 11 0.946875 0.398589 0.106250 0.112875 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/42.txt: -------------------------------------------------------------------------------- 1 | 10 0.170235 0.358025 0.202781 0.194004 2 | 10 0.316907 0.316578 0.114187 0.114638 3 | 10 0.818937 0.340388 0.118125 0.116402 4 | 2 0.522641 0.143739 0.238218 0.248677 5 | 22 0.444875 0.493827 0.118125 0.105820 6 | 24 0.392703 0.571429 0.116156 0.102293 7 | 21 0.613203 0.498236 0.198843 0.181658 8 | 5 0.670297 0.400353 0.175218 0.197531 9 | 13 0.918359 0.474427 0.116156 0.116402 10 | 1 0.935062 0.359788 0.129876 0.257496 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/43.txt: -------------------------------------------------------------------------------- 1 | 18 0.035407 0.432981 0.070813 0.097002 2 | 3 0.280485 0.321869 0.269718 0.262787 3 | 13 0.227329 0.455026 0.198843 0.201058 4 | 23 0.463578 0.613757 0.116156 0.105820 5 | 20 0.556109 0.372134 0.190968 0.179894 6 | 12 0.606312 0.264550 0.196875 0.197531 7 | 9 0.728375 0.534392 0.098437 0.119929 8 | 9 0.869140 0.492063 0.100406 0.119929 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/44.txt: -------------------------------------------------------------------------------- 1 | 11 0.078719 0.429453 0.157438 0.195767 2 | 10 0.261782 0.283069 0.196875 0.192240 3 | 15 0.207641 0.510582 0.108281 0.097002 4 | 3 0.391719 0.537919 0.220500 0.246914 5 | 3 0.424203 0.239859 0.163406 0.155203 6 | 18 0.524609 0.194885 0.100406 0.097002 7 | 22 0.629937 0.232804 0.196875 0.176367 8 | 16 0.701796 0.342152 0.104344 0.098765 9 | 4 0.796296 0.463845 0.143718 0.144621 10 | 14 0.966562 0.394180 0.066876 0.121693 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/45.txt: -------------------------------------------------------------------------------- 1 | 24 0.418297 0.538801 0.198843 0.181658 2 | 24 0.593515 0.149030 0.190968 0.174603 3 | 21 0.054110 0.346561 0.108220 0.178131 4 | 7 0.194844 0.462081 0.173250 0.208113 5 | 10 0.267688 0.261905 0.114187 0.111111 6 | 18 0.493109 0.285714 0.187031 0.165785 7 | 19 0.709671 0.251323 0.183093 0.167549 8 | 4 0.869140 0.336861 0.163406 0.148148 9 | 2 0.802203 0.416226 0.159468 0.151675 10 | 13 0.947859 0.428571 0.104282 0.211640 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/46.txt: -------------------------------------------------------------------------------- 1 | 21 0.088563 0.316578 0.177126 0.181658 2 | 12 0.322813 0.294533 0.118125 0.116402 3 | 7 0.486219 0.214286 0.102375 0.121693 4 | 9 0.205672 0.475309 0.100406 0.121693 5 | 6 0.974437 0.388007 0.051126 0.119929 6 | 15 0.436016 0.582011 0.183093 0.165785 7 | 2 0.647656 0.525573 0.161437 0.155203 8 | 11 0.754953 0.412698 0.194906 0.194004 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/47.txt: -------------------------------------------------------------------------------- 1 | 9 0.027532 0.360670 0.055063 0.121693 2 | 14 0.075766 0.428571 0.151532 0.194004 3 | 18 0.251938 0.501764 0.181125 0.164021 4 | 1 0.345453 0.273369 0.281531 0.271605 5 | 6 0.499016 0.192240 0.104344 0.123457 6 | 6 0.545281 0.537037 0.177187 0.206349 7 | 22 0.664390 0.239859 0.198843 0.176367 8 | 8 0.942937 0.380952 0.114126 0.204586 9 | 15 0.402547 0.632275 0.104344 0.097002 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/48.txt: -------------------------------------------------------------------------------- 1 | 15 0.425188 0.211640 0.181125 0.162257 2 | 0 0.076750 0.317460 0.153500 0.275132 3 | 2 0.151532 0.414462 0.271687 0.264550 4 | 6 0.227329 0.326279 0.100406 0.116402 5 | 23 0.331672 0.425926 0.116156 0.107584 6 | 21 0.633875 0.396825 0.110250 0.102293 7 | 11 0.770703 0.272487 0.198843 0.195767 8 | 12 0.465547 0.470018 0.202781 0.192240 9 | 1 0.552172 0.582892 0.151593 0.149912 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/49.txt: -------------------------------------------------------------------------------- 1 | 21 0.196813 0.329806 0.196875 0.169312 2 | 18 0.278516 0.527337 0.179156 0.158730 3 | 5 0.474406 0.451499 0.169312 0.204586 4 | 5 0.443891 0.217813 0.100406 0.118166 5 | 4 0.618125 0.148148 0.220500 0.246914 6 | 19 0.680140 0.363316 0.187031 0.158730 7 | 19 0.721484 0.546737 0.104344 0.095238 8 | 15 0.895718 0.312169 0.181125 0.165785 9 | 16 0.944937 0.442681 0.106312 0.095238 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/5.txt: -------------------------------------------------------------------------------- 1 | 24 0.083610 0.385362 0.112219 0.104056 2 | 9 0.395656 0.235450 0.102375 0.121693 3 | 13 0.261782 0.343034 0.114187 0.114638 4 | 12 0.453734 0.485891 0.183093 0.195767 5 | 11 0.534453 0.582892 0.190968 0.199295 6 | 10 0.813031 0.279541 0.192937 0.192240 7 | 19 0.965578 0.423280 0.068844 0.162257 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/50.txt: -------------------------------------------------------------------------------- 1 | 4 0.248000 0.299824 0.283499 0.268078 2 | 24 0.431094 0.185185 0.192937 0.176367 3 | 18 0.068875 0.388889 0.137750 0.164021 4 | 12 0.152516 0.505291 0.112219 0.114638 5 | 11 0.344469 0.538801 0.114187 0.114638 6 | 5 0.568906 0.363316 0.173250 0.204586 7 | 7 0.644703 0.536155 0.167343 0.204586 8 | 17 0.771687 0.424162 0.173250 0.164021 9 | 16 0.929187 0.366843 0.102375 0.095238 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/51.txt: -------------------------------------------------------------------------------- 1 | 23 0.237172 0.251323 0.194906 0.178131 2 | 12 0.145626 0.402998 0.110250 0.107584 3 | 12 0.285407 0.504409 0.122062 0.112875 4 | 2 0.433063 0.406526 0.271687 0.266314 5 | 6 0.521656 0.316578 0.165375 0.199295 6 | 14 0.616156 0.212522 0.200812 0.199295 7 | 22 0.734281 0.380952 0.118125 0.109347 8 | 17 0.658484 0.507055 0.179156 0.160494 9 | 0 0.883875 0.440917 0.232250 0.261023 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/52.txt: -------------------------------------------------------------------------------- 1 | 4 0.064938 0.368607 0.129876 0.151675 2 | 22 0.149563 0.409171 0.196875 0.176367 3 | 3 0.277532 0.344797 0.153562 0.149912 4 | 17 0.407469 0.385362 0.185062 0.167549 5 | 20 0.343485 0.526455 0.190968 0.181658 6 | 15 0.459641 0.270723 0.104344 0.089947 7 | 15 0.550203 0.592593 0.104344 0.095238 8 | 14 0.634859 0.208995 0.190968 0.199295 9 | 21 0.758890 0.482363 0.108281 0.104056 10 | 7 0.824843 0.281305 0.173250 0.199295 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/53.txt: -------------------------------------------------------------------------------- 1 | 7 0.239141 0.354497 0.167343 0.201058 2 | 5 0.145626 0.467372 0.094500 0.123457 3 | 8 0.662422 0.202822 0.171281 0.201058 4 | 8 0.919312 0.402116 0.161376 0.201058 5 | 11 0.338563 0.248677 0.196875 0.194004 6 | 6 0.399594 0.484127 0.169312 0.199295 7 | 19 0.459641 0.597884 0.104344 0.095238 8 | 23 0.568906 0.555556 0.114187 0.102293 9 | 3 0.743140 0.311287 0.155531 0.149912 10 | 22 0.827796 0.365079 0.104344 0.102293 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/54.txt: -------------------------------------------------------------------------------- 1 | 22 0.021625 0.388007 0.043250 0.105820 2 | 24 0.119048 0.286596 0.194906 0.178131 3 | 14 0.276547 0.532628 0.198843 0.197531 4 | 9 0.856343 0.501764 0.102375 0.121693 5 | 10 0.454719 0.227513 0.114187 0.109347 6 | 10 0.520672 0.414462 0.202781 0.197531 7 | 21 0.689000 0.258377 0.196875 0.174603 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/55.txt: -------------------------------------------------------------------------------- 1 | 8 0.144641 0.436508 0.175218 0.202822 2 | 0 0.398610 0.296296 0.238218 0.250441 3 | 24 0.356282 0.492063 0.114187 0.102293 4 | 12 0.454719 0.212522 0.192937 0.188713 5 | 2 0.555125 0.547619 0.157500 0.156966 6 | 7 0.690968 0.340388 0.169312 0.208113 7 | 7 0.817953 0.478836 0.171281 0.206349 8 | 22 0.935093 0.403880 0.106312 0.105820 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/56.txt: -------------------------------------------------------------------------------- 1 | 22 0.767749 0.476190 0.192937 0.176367 2 | 23 0.039344 0.347443 0.078688 0.102293 3 | 13 0.200751 0.447090 0.204750 0.199295 4 | 5 0.305094 0.254850 0.102375 0.121693 5 | 10 0.479328 0.180776 0.194906 0.195767 6 | 11 0.620094 0.586420 0.118125 0.118166 7 | 19 0.709671 0.395944 0.100406 0.089947 8 | 14 0.877999 0.320988 0.189000 0.197531 9 | 21 0.938015 0.410053 0.123969 0.178131 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/57.txt: -------------------------------------------------------------------------------- 1 | 8 0.013750 0.392416 0.027500 0.125220 2 | 8 0.726406 0.278660 0.106312 0.119929 3 | 23 0.212563 0.308642 0.196875 0.172840 4 | 21 0.511812 0.166667 0.196875 0.178131 5 | 16 0.341516 0.508818 0.183093 0.164021 6 | 12 0.460625 0.454145 0.114187 0.114638 7 | 2 0.585640 0.545855 0.269718 0.262787 8 | 20 0.673250 0.451499 0.200812 0.176367 9 | 13 0.979359 0.435626 0.041282 0.119929 10 | 11 0.886859 0.345679 0.190968 0.201058 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/58.txt: -------------------------------------------------------------------------------- 1 | 14 0.043282 0.377425 0.086563 0.112875 2 | 7 0.249969 0.300705 0.173250 0.202822 3 | 17 0.317891 0.396825 0.179156 0.165785 4 | 22 0.420266 0.585538 0.112219 0.102293 5 | 5 0.489172 0.278660 0.171281 0.204586 6 | 13 0.546266 0.387125 0.190968 0.202822 7 | 20 0.644703 0.515873 0.194906 0.178131 8 | 1 0.723453 0.276896 0.143718 0.144621 9 | 4 0.868125 0.432099 0.263750 0.261023 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/59.txt: -------------------------------------------------------------------------------- 1 | 10 0.085610 0.422399 0.171219 0.199295 2 | 7 0.429125 0.312169 0.102375 0.126984 3 | 18 0.475391 0.191358 0.187031 0.160494 4 | 18 0.766765 0.483245 0.100406 0.095238 5 | 11 0.610250 0.324515 0.200812 0.201058 6 | 19 0.384828 0.553792 0.179156 0.162257 7 | 5 0.879968 0.364198 0.169312 0.206349 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/6.txt: -------------------------------------------------------------------------------- 1 | 6 0.057032 0.438272 0.098437 0.121693 2 | 6 0.920297 0.388889 0.159406 0.202822 3 | 11 0.290329 0.561728 0.116156 0.118166 4 | 2 0.487203 0.215168 0.257906 0.261023 5 | 17 0.366125 0.342152 0.094500 0.091711 6 | 8 0.526578 0.482363 0.171281 0.202822 7 | 14 0.403531 0.643739 0.118125 0.116402 8 | 0 0.617140 0.185185 0.155531 0.151675 9 | 15 0.684078 0.345679 0.183093 0.162257 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/60.txt: -------------------------------------------------------------------------------- 1 | 10 0.085610 0.423280 0.171219 0.197531 2 | 19 0.383844 0.553792 0.177187 0.155203 3 | 7 0.428141 0.312169 0.100406 0.123457 4 | 18 0.475391 0.189594 0.183093 0.160494 5 | 11 0.610250 0.323633 0.200812 0.199295 6 | 18 0.765781 0.485891 0.110250 0.097002 7 | 5 0.882921 0.364198 0.171281 0.202822 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/61.txt: -------------------------------------------------------------------------------- 1 | 23 0.058016 0.343034 0.112219 0.100529 2 | 18 0.370063 0.241623 0.110250 0.095238 3 | 13 0.572844 0.164021 0.192937 0.190476 4 | 12 0.163344 0.428571 0.200812 0.197531 5 | 6 0.473422 0.582011 0.179156 0.204586 6 | 1 0.646672 0.377425 0.273656 0.250441 7 | 9 0.797281 0.296296 0.173250 0.204586 8 | 4 0.706718 0.521164 0.267749 0.262787 9 | 22 0.938015 0.360670 0.123969 0.178131 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/62.txt: -------------------------------------------------------------------------------- 1 | 13 0.075766 0.383598 0.151532 0.195767 2 | 16 0.159407 0.469136 0.106312 0.095238 3 | 6 0.339547 0.268078 0.167343 0.204586 4 | 16 0.790390 0.496473 0.183093 0.156966 5 | 15 0.460625 0.582892 0.185062 0.160494 6 | 7 0.646672 0.200176 0.171281 0.209877 7 | 7 0.714593 0.419753 0.106312 0.123457 8 | 18 0.882921 0.344797 0.108281 0.097002 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/63.txt: -------------------------------------------------------------------------------- 1 | 18 0.147594 0.345679 0.185062 0.162257 2 | 18 0.512797 0.169312 0.104344 0.098765 3 | 13 0.326750 0.290123 0.204750 0.188713 4 | 22 0.401563 0.425926 0.196875 0.174603 5 | 22 0.458656 0.538801 0.114187 0.100529 6 | 23 0.611234 0.223104 0.112219 0.100529 7 | 3 0.737234 0.480600 0.257906 0.259259 8 | 13 0.879968 0.340388 0.200812 0.194004 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/64.txt: -------------------------------------------------------------------------------- 1 | 8 0.522641 0.539683 0.171281 0.204586 2 | 8 0.661437 0.436508 0.165375 0.206349 3 | 9 0.374000 0.189594 0.173250 0.206349 4 | 9 0.832718 0.365961 0.173250 0.206349 5 | 21 0.046235 0.450617 0.092470 0.107584 6 | 21 0.442906 0.311287 0.114187 0.104056 7 | 20 0.258829 0.493827 0.190968 0.176367 8 | 22 0.720500 0.251323 0.196875 0.174603 9 | 4 0.138735 0.319224 0.238218 0.246914 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/65.txt: -------------------------------------------------------------------------------- 1 | 4 0.564969 0.216049 0.153562 0.153439 2 | 12 0.071828 0.344797 0.143656 0.199295 3 | 22 0.182047 0.486772 0.190968 0.176367 4 | 0 0.258829 0.422399 0.163406 0.146384 5 | 14 0.407469 0.560847 0.200812 0.197531 6 | 16 0.642734 0.455026 0.183093 0.155203 7 | 18 0.745109 0.265432 0.108281 0.093474 8 | 13 0.950812 0.386243 0.098376 0.211640 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/66.txt: -------------------------------------------------------------------------------- 1 | 19 0.022610 0.383598 0.045220 0.097002 2 | 8 0.133813 0.347443 0.102375 0.123457 3 | 2 0.261782 0.244268 0.224437 0.241623 4 | 20 0.549219 0.229277 0.110250 0.105820 5 | 23 0.491141 0.309524 0.112219 0.097002 6 | 20 0.419281 0.538801 0.189000 0.178131 7 | 23 0.625015 0.503527 0.194906 0.171076 8 | 21 0.773656 0.373898 0.192937 0.176367 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/67.txt: -------------------------------------------------------------------------------- 1 | 21 0.214532 0.448854 0.192937 0.171076 2 | 14 0.082626 0.371252 0.110250 0.111111 3 | 2 0.273594 0.405644 0.161437 0.141093 4 | 12 0.357266 0.320988 0.202781 0.194004 5 | 6 0.418297 0.447090 0.104344 0.118166 6 | 6 0.839609 0.316578 0.092531 0.118166 7 | 19 0.496063 0.279541 0.106312 0.097002 8 | 5 0.581703 0.547619 0.100406 0.118166 9 | 9 0.650609 0.253968 0.104344 0.116402 10 | 8 0.922265 0.354497 0.155469 0.201058 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/68.txt: -------------------------------------------------------------------------------- 1 | 20 0.109204 0.311287 0.116156 0.100529 2 | 24 0.362188 0.344797 0.185062 0.167549 3 | 18 0.291313 0.527337 0.185062 0.158730 4 | 18 0.769718 0.450617 0.110250 0.089947 5 | 18 0.849452 0.325397 0.187031 0.160494 6 | 17 0.470469 0.188713 0.173250 0.158730 7 | 21 0.681125 0.248677 0.196875 0.172840 8 | 15 0.516734 0.493827 0.179156 0.162257 9 | 5 0.909499 0.489418 0.102375 0.118166 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/69.txt: -------------------------------------------------------------------------------- 1 | 20 0.285407 0.228395 0.192937 0.178131 2 | 23 0.372031 0.373898 0.114187 0.105820 3 | 22 0.764796 0.273369 0.190968 0.176367 4 | 14 0.204688 0.414462 0.196875 0.194004 5 | 14 0.436016 0.621693 0.112219 0.114638 6 | 12 0.599422 0.533510 0.206718 0.199295 7 | 9 0.649625 0.214286 0.106312 0.121693 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/7.txt: -------------------------------------------------------------------------------- 1 | 11 0.037375 0.364198 0.074750 0.118166 2 | 11 0.289344 0.545855 0.200812 0.199295 3 | 21 0.374985 0.270723 0.194906 0.178131 4 | 4 0.180079 0.425926 0.273656 0.269841 5 | 13 0.518703 0.373898 0.194906 0.194004 6 | 12 0.573828 0.579365 0.198843 0.195767 7 | 0 0.642734 0.496473 0.159468 0.153439 8 | 18 0.784484 0.410935 0.183093 0.162257 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/70.txt: -------------------------------------------------------------------------------- 1 | 3 0.065891 0.415344 0.124031 0.142857 2 | 18 0.200751 0.456790 0.106312 0.098765 3 | 6 0.119048 0.353616 0.100406 0.121693 4 | 8 0.397625 0.248677 0.173250 0.204586 5 | 8 0.561031 0.152557 0.161437 0.202822 6 | 19 0.334625 0.506173 0.189000 0.162257 7 | 23 0.735265 0.451499 0.194906 0.176367 8 | 12 0.492125 0.647266 0.110250 0.116402 9 | 22 0.820906 0.307760 0.106312 0.097002 10 | 7 0.966562 0.372134 0.066876 0.119929 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/71.txt: -------------------------------------------------------------------------------- 1 | 22 0.224376 0.483245 0.192937 0.176367 2 | 22 0.502953 0.188713 0.116156 0.102293 3 | 24 0.367110 0.230159 0.112219 0.104056 4 | 15 0.019657 0.352734 0.039313 0.158730 5 | 5 0.943952 0.471781 0.096469 0.121693 6 | 9 0.802203 0.326279 0.100406 0.123457 7 | 14 0.670297 0.573192 0.112219 0.116402 8 | 2 0.427156 0.405644 0.279562 0.268078 9 | 18 0.580719 0.239859 0.181125 0.162257 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/72.txt: -------------------------------------------------------------------------------- 1 | 22 0.126923 0.366843 0.108281 0.098765 2 | 21 0.196813 0.484127 0.192937 0.174603 3 | 21 0.445859 0.205467 0.108281 0.100529 4 | 11 0.269657 0.270723 0.114187 0.107584 5 | 11 0.795312 0.464727 0.196875 0.195767 6 | 23 0.384828 0.373898 0.194906 0.176367 7 | 13 0.563984 0.529101 0.198843 0.197531 8 | 15 0.692937 0.276896 0.102375 0.095238 9 | 5 0.873077 0.292769 0.171281 0.197531 10 | 1 0.960656 0.434744 0.078688 0.142857 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/73.txt: -------------------------------------------------------------------------------- 1 | 14 0.149563 0.392416 0.114187 0.111111 2 | 9 0.286391 0.508818 0.167343 0.202822 3 | 10 0.347422 0.342152 0.194906 0.197531 4 | 19 0.427156 0.468254 0.102375 0.100529 5 | 10 0.523625 0.618166 0.110250 0.111111 6 | 5 0.588594 0.219577 0.169312 0.199295 7 | 6 0.645687 0.401235 0.169312 0.202822 8 | 3 0.752000 0.504409 0.169312 0.148148 9 | 18 0.857327 0.324515 0.096469 0.091711 10 | 0 0.939984 0.373016 0.120032 0.245150 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/74.txt: -------------------------------------------------------------------------------- 1 | 21 0.185001 0.364198 0.189000 0.171076 2 | 7 0.264735 0.300705 0.092531 0.118166 3 | 19 0.461609 0.520282 0.179156 0.165785 4 | 11 0.584656 0.443563 0.196875 0.202822 5 | 15 0.652578 0.190476 0.171281 0.162257 6 | 0 0.816968 0.289242 0.224437 0.250441 7 | 8 0.879968 0.447972 0.173250 0.208113 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/75.txt: -------------------------------------------------------------------------------- 1 | 17 0.013750 0.385362 0.027500 0.082892 2 | 9 0.180079 0.458554 0.171281 0.204586 3 | 4 0.290329 0.415344 0.135843 0.139330 4 | 5 0.364157 0.269841 0.169312 0.201058 5 | 7 0.441922 0.510582 0.171281 0.202822 6 | 11 0.526578 0.190476 0.190968 0.194004 7 | 10 0.713609 0.350970 0.198843 0.201058 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/76.txt: -------------------------------------------------------------------------------- 1 | 7 0.045250 0.348325 0.090500 0.121693 2 | 22 0.212563 0.466490 0.192937 0.178131 3 | 14 0.139719 0.426808 0.114187 0.109347 4 | 21 0.278516 0.269841 0.108281 0.102293 5 | 9 0.417313 0.364198 0.173250 0.202822 6 | 6 0.513781 0.214286 0.173250 0.199295 7 | 23 0.759874 0.335979 0.189000 0.171076 8 | 18 0.597453 0.527337 0.187031 0.165785 9 | 11 0.867171 0.447972 0.198843 0.197531 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/77.txt: -------------------------------------------------------------------------------- 1 | 0 0.080657 0.351852 0.137812 0.146384 2 | 8 0.310016 0.438272 0.171281 0.206349 3 | 8 0.374985 0.555556 0.096469 0.126984 4 | 6 0.483266 0.613757 0.171281 0.204586 5 | 14 0.517719 0.306878 0.196875 0.197531 6 | 19 0.585640 0.223986 0.108281 0.088183 7 | 19 0.765781 0.493827 0.102375 0.091711 8 | 7 0.837640 0.389771 0.100406 0.116402 9 | 9 0.972468 0.413580 0.055063 0.118166 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/78.txt: -------------------------------------------------------------------------------- 1 | 9 0.289344 0.231922 0.173250 0.206349 2 | 19 0.437984 0.330688 0.179156 0.160494 3 | 12 0.340532 0.558201 0.196875 0.199295 4 | 12 0.699828 0.483245 0.112219 0.112875 5 | 1 0.528547 0.493827 0.139781 0.137566 6 | 8 0.830749 0.294533 0.173250 0.197531 7 | 16 0.953765 0.380952 0.092470 0.169312 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/79.txt: -------------------------------------------------------------------------------- 1 | 5 0.115110 0.402116 0.171281 0.204586 2 | 0 0.538391 0.381834 0.269718 0.266314 3 | 22 0.437000 0.639330 0.189000 0.171076 4 | 3 0.674234 0.502646 0.127969 0.144621 5 | 11 0.864218 0.304233 0.192937 0.192240 6 | 19 0.248985 0.328924 0.104344 0.089947 7 | 14 0.308047 0.250441 0.112219 0.109347 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/8.txt: -------------------------------------------------------------------------------- 1 | 19 0.268672 0.219577 0.179156 0.153439 2 | 19 0.668328 0.471781 0.104344 0.093474 3 | 22 0.062938 0.430335 0.110250 0.105820 4 | 2 0.146610 0.321869 0.155531 0.149912 5 | 0 0.349391 0.325397 0.269718 0.266314 6 | 16 0.456688 0.514109 0.102375 0.097002 7 | 18 0.548234 0.120811 0.183093 0.160494 8 | 4 0.795312 0.298942 0.165375 0.156966 9 | 7 0.876031 0.395944 0.102375 0.121693 10 | 10 0.600406 0.587302 0.196875 0.194004 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/80.txt: -------------------------------------------------------------------------------- 1 | 0 0.101329 0.328924 0.155531 0.146384 2 | 0 0.200751 0.410053 0.208687 0.241623 3 | 22 0.321828 0.253968 0.194906 0.176367 4 | 19 0.417313 0.208995 0.102375 0.089947 5 | 18 0.666359 0.373898 0.100406 0.091711 6 | 21 0.571859 0.551146 0.194906 0.174603 7 | 9 0.862249 0.385362 0.169312 0.202822 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/81.txt: -------------------------------------------------------------------------------- 1 | 21 0.061985 0.336861 0.123969 0.176367 2 | 21 0.237172 0.483245 0.194906 0.172840 3 | 22 0.518703 0.136684 0.194906 0.171076 4 | 15 0.472438 0.296296 0.177187 0.162257 5 | 17 0.702781 0.443563 0.106312 0.093474 6 | 3 0.639781 0.560847 0.165375 0.151675 7 | 3 0.902578 0.370370 0.194844 0.239859 8 | 13 0.743140 0.262787 0.187031 0.190476 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/82.txt: -------------------------------------------------------------------------------- 1 | 21 0.081641 0.397707 0.112219 0.100529 2 | 10 0.139719 0.485891 0.114187 0.118166 3 | 16 0.249969 0.288360 0.102375 0.093474 4 | 8 0.389750 0.478836 0.173250 0.202822 5 | 22 0.489172 0.337743 0.198843 0.171076 6 | 7 0.912452 0.394180 0.167343 0.206349 7 | 12 0.554141 0.214286 0.198843 0.192240 8 | 0 0.614187 0.454145 0.133875 0.139330 9 | 19 0.671281 0.537037 0.102375 0.097002 10 | 19 0.807124 0.306878 0.173250 0.169312 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/83.txt: -------------------------------------------------------------------------------- 1 | 10 0.071828 0.377425 0.143656 0.197531 2 | 1 0.191891 0.513228 0.167343 0.155203 3 | 18 0.362188 0.344797 0.106312 0.093474 4 | 0 0.430110 0.236332 0.277593 0.261023 5 | 0 0.470469 0.510582 0.232312 0.252205 6 | 3 0.598437 0.194004 0.145687 0.141093 7 | 11 0.682109 0.305996 0.198843 0.195767 8 | 15 0.728375 0.424162 0.189000 0.164021 9 | 7 0.927218 0.438272 0.098437 0.121693 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/84.txt: -------------------------------------------------------------------------------- 1 | 19 0.061985 0.394180 0.123969 0.164021 2 | 4 0.153501 0.265432 0.267749 0.266314 3 | 4 0.812046 0.447090 0.281531 0.269841 4 | 13 0.272610 0.513228 0.198843 0.194004 5 | 14 0.497047 0.607584 0.198843 0.202822 6 | 6 0.393688 0.231922 0.102375 0.121693 7 | 23 0.572844 0.440035 0.114187 0.104056 8 | 0 0.705734 0.292769 0.281531 0.264550 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/85.txt: -------------------------------------------------------------------------------- 1 | 0 0.479328 0.621693 0.159468 0.149912 2 | 12 0.077735 0.344797 0.155469 0.199295 3 | 20 0.259813 0.322751 0.114187 0.102293 4 | 20 0.894734 0.351852 0.194906 0.174603 5 | 5 0.349391 0.495591 0.104344 0.123457 6 | 5 0.381875 0.235450 0.173250 0.206349 7 | 6 0.657500 0.232804 0.098437 0.119929 8 | 16 0.596469 0.414462 0.189000 0.165785 9 | 1 0.766765 0.519400 0.120094 0.146384 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/86.txt: -------------------------------------------------------------------------------- 1 | 1 0.156454 0.342152 0.151593 0.151675 2 | 1 0.340532 0.340388 0.279562 0.268078 3 | 0 0.055094 0.440035 0.110188 0.156966 4 | 8 0.278516 0.465608 0.100406 0.119929 5 | 19 0.402547 0.594356 0.183093 0.162257 6 | 21 0.573828 0.592593 0.108281 0.098765 7 | 23 0.736250 0.259259 0.110250 0.102293 8 | 23 0.934109 0.412698 0.112219 0.098765 9 | 14 0.663406 0.485891 0.204750 0.188713 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/87.txt: -------------------------------------------------------------------------------- 1 | 14 0.638797 0.437390 0.198843 0.201058 2 | 10 0.020641 0.421517 0.041282 0.088183 3 | 8 0.278516 0.526455 0.108281 0.121693 4 | 18 0.495078 0.608466 0.183093 0.165785 5 | 15 0.982312 0.370370 0.035376 0.141093 6 | 23 0.802203 0.260141 0.187031 0.171076 7 | 4 0.322813 0.326279 0.255937 0.250441 8 | 4 0.502953 0.133157 0.269718 0.259259 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/88.txt: -------------------------------------------------------------------------------- 1 | 13 0.157438 0.470899 0.196875 0.194004 2 | 13 0.460625 0.270723 0.118125 0.114638 3 | 21 0.257844 0.261023 0.196875 0.176367 4 | 18 0.348407 0.358025 0.177187 0.165785 5 | 1 0.618125 0.511464 0.252000 0.264550 6 | 22 0.759874 0.402116 0.189000 0.179894 7 | 22 0.868156 0.307760 0.192937 0.178131 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/89.txt: -------------------------------------------------------------------------------- 1 | 1 0.084625 0.404762 0.169250 0.273369 2 | 12 0.229297 0.379189 0.112219 0.116402 3 | 7 0.369078 0.542328 0.171281 0.202822 4 | 16 0.499016 0.430335 0.187031 0.162257 5 | 16 0.973453 0.389771 0.053094 0.098765 6 | 5 0.547250 0.146384 0.173250 0.201058 7 | 5 0.830749 0.299824 0.173250 0.204586 8 | 10 0.641750 0.321869 0.114187 0.114638 9 | 4 0.776609 0.514991 0.155531 0.155203 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/9.txt: -------------------------------------------------------------------------------- 1 | 4 0.061000 0.455908 0.122000 0.149912 2 | 23 0.218469 0.336861 0.114187 0.102293 3 | 8 0.414360 0.233686 0.100406 0.118166 4 | 19 0.330688 0.513228 0.102375 0.095238 5 | 11 0.574812 0.291887 0.204750 0.195767 6 | 0 0.673250 0.470899 0.157500 0.158730 7 | 5 0.767749 0.351852 0.173250 0.206349 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/90.txt: -------------------------------------------------------------------------------- 1 | 11 0.017688 0.390653 0.035376 0.089947 2 | 11 0.594500 0.571429 0.196875 0.197531 3 | 12 0.223391 0.515873 0.127969 0.114638 4 | 2 0.159407 0.305996 0.161437 0.149912 5 | 23 0.350375 0.279541 0.114187 0.104056 6 | 19 0.403531 0.422399 0.106312 0.093474 7 | 9 0.660453 0.469136 0.100406 0.119929 8 | 20 0.535437 0.141093 0.192937 0.176367 9 | 0 0.770703 0.312169 0.250031 0.257496 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/91.txt: -------------------------------------------------------------------------------- 1 | 10 0.199766 0.483245 0.202781 0.197531 2 | 23 0.303125 0.331570 0.189000 0.169312 3 | 22 0.424203 0.216931 0.190968 0.172840 4 | 15 0.529531 0.584656 0.106312 0.093474 5 | 8 0.568906 0.313051 0.086625 0.114638 6 | 4 0.679156 0.367725 0.259874 0.262787 7 | 7 0.743140 0.489418 0.171281 0.206349 8 | 2 0.910453 0.426808 0.179094 0.268078 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/92.txt: -------------------------------------------------------------------------------- 1 | 4 0.045250 0.361552 0.090500 0.250441 2 | 19 0.181063 0.492063 0.102375 0.095238 3 | 23 0.284422 0.309524 0.190968 0.174603 4 | 23 0.690968 0.285714 0.192937 0.169312 5 | 11 0.616156 0.192240 0.192937 0.190476 6 | 6 0.494094 0.434744 0.165375 0.202822 7 | 1 0.358250 0.531746 0.141750 0.146384 8 | 16 0.419281 0.600529 0.106312 0.093474 9 | 17 0.927187 0.412698 0.145626 0.162257 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/93.txt: -------------------------------------------------------------------------------- 1 | 8 0.158423 0.281305 0.179156 0.202822 2 | 23 0.383844 0.461199 0.192937 0.178131 3 | 6 0.298204 0.568783 0.100406 0.121693 4 | 2 0.500984 0.316578 0.222468 0.241623 5 | 4 0.598437 0.211640 0.133875 0.148148 6 | 18 0.712625 0.370370 0.181125 0.162257 7 | 20 0.798265 0.285714 0.108281 0.098765 8 | 19 0.905562 0.376543 0.106312 0.097002 9 | 9 0.965578 0.442681 0.068844 0.116402 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/94.txt: -------------------------------------------------------------------------------- 1 | 9 0.806140 0.463845 0.104344 0.123457 2 | 11 0.061954 0.382716 0.108281 0.109347 3 | 23 0.236188 0.453263 0.196875 0.179894 4 | 3 0.295250 0.259259 0.271687 0.264550 5 | 15 0.508859 0.324515 0.108281 0.095238 6 | 24 0.646672 0.379189 0.190968 0.176367 7 | 19 0.452750 0.584656 0.181125 0.164021 8 | 21 0.585640 0.551146 0.116156 0.104056 9 | 18 0.718531 0.512346 0.106312 0.097002 10 | 9 0.960656 0.437390 0.078688 0.119929 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/95.txt: -------------------------------------------------------------------------------- 1 | 12 0.119048 0.305115 0.198843 0.201058 2 | 2 0.348407 0.276896 0.153562 0.144621 3 | 16 0.280485 0.426808 0.108281 0.098765 4 | 16 0.450781 0.571429 0.181125 0.162257 5 | 1 0.391719 0.524691 0.157500 0.153439 6 | 17 0.626000 0.388889 0.106312 0.093474 7 | 15 0.708687 0.453263 0.181125 0.165785 8 | 6 0.912452 0.367725 0.096469 0.114638 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/96.txt: -------------------------------------------------------------------------------- 1 | 15 0.041313 0.434744 0.082626 0.097002 2 | 15 0.559062 0.353616 0.189000 0.160494 3 | 18 0.225360 0.447972 0.187031 0.165785 4 | 20 0.116094 0.292769 0.192937 0.172840 5 | 7 0.286391 0.284832 0.112219 0.121693 6 | 22 0.459641 0.212522 0.112219 0.104056 7 | 10 0.397625 0.601411 0.114187 0.116402 8 | 5 0.660453 0.211640 0.167343 0.204586 9 | 5 0.763812 0.484127 0.165375 0.202822 10 | 11 0.946875 0.376543 0.106250 0.114638 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/97.txt: -------------------------------------------------------------------------------- 1 | 11 0.347422 0.412698 0.198843 0.197531 2 | 22 0.213547 0.500882 0.108281 0.102293 3 | 17 0.435031 0.642857 0.106312 0.093474 4 | 7 0.574812 0.215168 0.102375 0.123457 5 | 23 0.728375 0.352734 0.189000 0.176367 6 | 3 0.794328 0.246032 0.253968 0.252205 7 | 14 0.668328 0.532628 0.206718 0.194004 8 | 12 0.862249 0.479718 0.118125 0.116402 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/98.txt: -------------------------------------------------------------------------------- 1 | 0 0.216501 0.285714 0.283499 0.268078 2 | 1 0.149563 0.424162 0.133875 0.142857 3 | 9 0.311985 0.457672 0.167343 0.202822 4 | 9 0.777593 0.418871 0.169312 0.199295 5 | 21 0.421250 0.359788 0.192937 0.176367 6 | 11 0.522641 0.580247 0.116156 0.116402 7 | 16 0.660453 0.517637 0.104344 0.089947 8 | 19 0.541344 0.159612 0.181125 0.167549 9 | 17 0.708687 0.301587 0.181125 0.162257 -------------------------------------------------------------------------------- /datasets/geetest/labels/train2017/99.txt: -------------------------------------------------------------------------------- 1 | 17 0.689000 0.216049 0.189000 0.167549 2 | 2 0.328719 0.384480 0.255937 0.253968 3 | 2 0.800234 0.437390 0.273656 0.261023 4 | 11 0.106251 0.320106 0.196875 0.195767 5 | 11 0.495078 0.586420 0.194906 0.195767 6 | 21 0.425188 0.260141 0.192937 0.174603 7 | 24 0.953765 0.355379 0.092470 0.174603 8 | 15 0.576781 0.479718 0.173250 0.165785 -------------------------------------------------------------------------------- /img/1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/img/1.jpg -------------------------------------------------------------------------------- /img/2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/img/2.jpg -------------------------------------------------------------------------------- /img/3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/img/3.jpg -------------------------------------------------------------------------------- /img/4.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/img/4.jpg -------------------------------------------------------------------------------- /img/5.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/img/5.jpg -------------------------------------------------------------------------------- /yolov5/.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | # Define hooks for code formations 2 | # Will be applied on any updated commit files if a user has installed and linked commit hook 3 | 4 | default_language_version: 5 | python: python3.8 6 | 7 | # Define bot property if installed via https://github.com/marketplace/pre-commit-ci 8 | ci: 9 | autofix_prs: true 10 | autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions' 11 | autoupdate_schedule: monthly 12 | # submodules: true 13 | 14 | repos: 15 | - repo: https://github.com/pre-commit/pre-commit-hooks 16 | rev: v4.3.0 17 | hooks: 18 | # - id: end-of-file-fixer 19 | - id: trailing-whitespace 20 | - id: check-case-conflict 21 | - id: check-yaml 22 | - id: check-toml 23 | - id: pretty-format-json 24 | - id: check-docstring-first 25 | 26 | - repo: https://github.com/asottile/pyupgrade 27 | rev: v2.38.2 28 | hooks: 29 | - id: pyupgrade 30 | name: Upgrade code 31 | args: [ --py37-plus ] 32 | 33 | - repo: https://github.com/PyCQA/isort 34 | rev: 5.10.1 35 | hooks: 36 | - id: isort 37 | name: Sort imports 38 | 39 | - repo: https://github.com/pre-commit/mirrors-yapf 40 | rev: v0.32.0 41 | hooks: 42 | - id: yapf 43 | name: YAPF formatting 44 | 45 | - repo: https://github.com/executablebooks/mdformat 46 | rev: 0.7.16 47 | hooks: 48 | - id: mdformat 49 | name: MD formatting 50 | additional_dependencies: 51 | - mdformat-gfm 52 | - mdformat-black 53 | exclude: "README.md|README_cn.md" 54 | 55 | - repo: https://github.com/asottile/yesqa 56 | rev: v1.4.0 57 | hooks: 58 | - id: yesqa 59 | 60 | - repo: https://github.com/PyCQA/flake8 61 | rev: 5.0.4 62 | hooks: 63 | - id: flake8 64 | name: PEP8 65 | -------------------------------------------------------------------------------- /yolov5/best.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/best.pt -------------------------------------------------------------------------------- /yolov5/data/geetest.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics 3 | # Example usage: python train.py --data coco128.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── coco128 ← downloads here (7 MB) 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/geetest # dataset root dir 12 | train: images/train2017 # train images (relative to 'path') 128 images 13 | val: images/train2017 # val images (relative to 'path') 128 images 14 | test: # test images (optional) 15 | 16 | # Classes 17 | names: 18 | 0: RedBlock 19 | 1: YellowBlock 20 | 2: BlueBlock 21 | 3: GreenBlock 22 | 4: GreyBlock 23 | 5: RedCylinder 24 | 6: YellowCylinder 25 | 7: BlueCylinder 26 | 8: GreenCylinder 27 | 9: GreyCylinder 28 | 10: RedCone 29 | 11: YellowCone 30 | 12: BlueCone 31 | 13: GreenCone 32 | 14: GreyCone 33 | 15: RedPolyhedron 34 | 16: YellowPolyhedron 35 | 17: BluePolyhedron 36 | 18: GreenPolyhedron 37 | 19: GreyPolyhedron 38 | 20: RedBall 39 | 21: YellowBall 40 | 22: BlueBall 41 | 23: GreenBall 42 | 24: GreyBall 43 | -------------------------------------------------------------------------------- /yolov5/data/hyps/hyp.Objects365.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for Objects365 training 3 | # python train.py --weights yolov5m.pt --data Objects365.yaml --evolve 4 | # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.00258 7 | lrf: 0.17 8 | momentum: 0.779 9 | weight_decay: 0.00058 10 | warmup_epochs: 1.33 11 | warmup_momentum: 0.86 12 | warmup_bias_lr: 0.0711 13 | box: 0.0539 14 | cls: 0.299 15 | cls_pw: 0.825 16 | obj: 0.632 17 | obj_pw: 1.0 18 | iou_t: 0.2 19 | anchor_t: 3.44 20 | anchors: 3.2 21 | fl_gamma: 0.0 22 | hsv_h: 0.0188 23 | hsv_s: 0.704 24 | hsv_v: 0.36 25 | degrees: 0.0 26 | translate: 0.0902 27 | scale: 0.491 28 | shear: 0.0 29 | perspective: 0.0 30 | flipud: 0.0 31 | fliplr: 0.5 32 | mosaic: 1.0 33 | mixup: 0.0 34 | copy_paste: 0.0 35 | -------------------------------------------------------------------------------- /yolov5/data/hyps/hyp.VOC.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for VOC training 3 | # python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve 4 | # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials 5 | 6 | # YOLOv5 Hyperparameter Evolution Results 7 | # Best generation: 467 8 | # Last generation: 996 9 | # metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss 10 | # 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865 11 | 12 | lr0: 0.00334 13 | lrf: 0.15135 14 | momentum: 0.74832 15 | weight_decay: 0.00025 16 | warmup_epochs: 3.3835 17 | warmup_momentum: 0.59462 18 | warmup_bias_lr: 0.18657 19 | box: 0.02 20 | cls: 0.21638 21 | cls_pw: 0.5 22 | obj: 0.51728 23 | obj_pw: 0.67198 24 | iou_t: 0.2 25 | anchor_t: 3.3744 26 | fl_gamma: 0.0 27 | hsv_h: 0.01041 28 | hsv_s: 0.54703 29 | hsv_v: 0.27739 30 | degrees: 0.0 31 | translate: 0.04591 32 | scale: 0.75544 33 | shear: 0.0 34 | perspective: 0.0 35 | flipud: 0.0 36 | fliplr: 0.5 37 | mosaic: 0.85834 38 | mixup: 0.04266 39 | copy_paste: 0.0 40 | anchors: 3.412 41 | -------------------------------------------------------------------------------- /yolov5/data/hyps/hyp.scratch-high.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for high-augmentation COCO training from scratch 3 | # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 4 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) 8 | momentum: 0.937 # SGD momentum/Adam beta1 9 | weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | warmup_momentum: 0.8 # warmup initial momentum 12 | warmup_bias_lr: 0.1 # warmup initial bias lr 13 | box: 0.05 # box loss gain 14 | cls: 0.3 # cls loss gain 15 | cls_pw: 1.0 # cls BCELoss positive_weight 16 | obj: 0.7 # obj loss gain (scale with pixels) 17 | obj_pw: 1.0 # obj BCELoss positive_weight 18 | iou_t: 0.20 # IoU training threshold 19 | anchor_t: 4.0 # anchor-multiple threshold 20 | # anchors: 3 # anchors per output layer (0 to ignore) 21 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | degrees: 0.0 # image rotation (+/- deg) 26 | translate: 0.1 # image translation (+/- fraction) 27 | scale: 0.9 # image scale (+/- gain) 28 | shear: 0.0 # image shear (+/- deg) 29 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | flipud: 0.0 # image flip up-down (probability) 31 | fliplr: 0.5 # image flip left-right (probability) 32 | mosaic: 1.0 # image mosaic (probability) 33 | mixup: 0.1 # image mixup (probability) 34 | copy_paste: 0.1 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /yolov5/data/hyps/hyp.scratch-low.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for low-augmentation COCO training from scratch 3 | # python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear 4 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) 8 | momentum: 0.937 # SGD momentum/Adam beta1 9 | weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | warmup_momentum: 0.8 # warmup initial momentum 12 | warmup_bias_lr: 0.1 # warmup initial bias lr 13 | box: 0.05 # box loss gain 14 | cls: 0.5 # cls loss gain 15 | cls_pw: 1.0 # cls BCELoss positive_weight 16 | obj: 1.0 # obj loss gain (scale with pixels) 17 | obj_pw: 1.0 # obj BCELoss positive_weight 18 | iou_t: 0.20 # IoU training threshold 19 | anchor_t: 4.0 # anchor-multiple threshold 20 | # anchors: 3 # anchors per output layer (0 to ignore) 21 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | degrees: 0.0 # image rotation (+/- deg) 26 | translate: 0.1 # image translation (+/- fraction) 27 | scale: 0.5 # image scale (+/- gain) 28 | shear: 0.0 # image shear (+/- deg) 29 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | flipud: 0.0 # image flip up-down (probability) 31 | fliplr: 0.5 # image flip left-right (probability) 32 | mosaic: 1.0 # image mosaic (probability) 33 | mixup: 0.0 # image mixup (probability) 34 | copy_paste: 0.0 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /yolov5/data/hyps/hyp.scratch-med.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for medium-augmentation COCO training from scratch 3 | # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 4 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) 8 | momentum: 0.937 # SGD momentum/Adam beta1 9 | weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | warmup_momentum: 0.8 # warmup initial momentum 12 | warmup_bias_lr: 0.1 # warmup initial bias lr 13 | box: 0.05 # box loss gain 14 | cls: 0.3 # cls loss gain 15 | cls_pw: 1.0 # cls BCELoss positive_weight 16 | obj: 0.7 # obj loss gain (scale with pixels) 17 | obj_pw: 1.0 # obj BCELoss positive_weight 18 | iou_t: 0.20 # IoU training threshold 19 | anchor_t: 4.0 # anchor-multiple threshold 20 | # anchors: 3 # anchors per output layer (0 to ignore) 21 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | degrees: 0.0 # image rotation (+/- deg) 26 | translate: 0.1 # image translation (+/- fraction) 27 | scale: 0.9 # image scale (+/- gain) 28 | shear: 0.0 # image shear (+/- deg) 29 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | flipud: 0.0 # image flip up-down (probability) 31 | fliplr: 0.5 # image flip left-right (probability) 32 | mosaic: 1.0 # image mosaic (probability) 33 | mixup: 0.1 # image mixup (probability) 34 | copy_paste: 0.0 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /yolov5/data/images/6.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/data/images/6.jpg -------------------------------------------------------------------------------- /yolov5/data/scripts/download_weights.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 3 | # Download latest models from https://github.com/ultralytics/yolov5/releases 4 | # Example usage: bash data/scripts/download_weights.sh 5 | # parent 6 | # └── yolov5 7 | # ├── yolov5s.pt ← downloads here 8 | # ├── yolov5m.pt 9 | # └── ... 10 | 11 | python - <=3.2.2 6 | numpy>=1.18.5 7 | opencv-python>=4.1.1 8 | Pillow>=7.1.2 9 | PyYAML>=5.3.1 10 | requests>=2.23.0 11 | scipy>=1.4.1 12 | torch>=1.7.0 # see https://pytorch.org/get-started/locally/ (recommended) 13 | torchvision>=0.8.1 14 | tqdm>=4.64.0 15 | # protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012 16 | 17 | # Logging ------------------------------------- 18 | tensorboard>=2.4.1 19 | # clearml>=1.2.0 20 | # comet 21 | 22 | # Plotting ------------------------------------ 23 | pandas>=1.1.4 24 | seaborn>=0.11.0 25 | 26 | # Export -------------------------------------- 27 | # coremltools>=6.0 # CoreML export 28 | # onnx>=1.9.0 # ONNX export 29 | # onnx-simplifier>=0.4.1 # ONNX simplifier 30 | # nvidia-pyindex # TensorRT export 31 | # nvidia-tensorrt # TensorRT export 32 | # scikit-learn<=1.1.2 # CoreML quantization 33 | # tensorflow>=2.4.1 # TF exports (-cpu, -aarch64, -macos) 34 | # tensorflowjs>=3.9.0 # TF.js export 35 | # openvino-dev # OpenVINO export 36 | 37 | # Deploy -------------------------------------- 38 | # tritonclient[all]~=2.24.0 39 | 40 | # Extras -------------------------------------- 41 | ipython # interactive notebook 42 | psutil # system utilization 43 | thop>=0.1.1 # FLOPs computation 44 | # mss # screenshots 45 | # albumentations>=1.0.3 46 | # pycocotools>=2.0 # COCO mAP 47 | # roboflow 48 | -------------------------------------------------------------------------------- /yolov5/setup.cfg: -------------------------------------------------------------------------------- 1 | # Project-wide configuration file, can be used for package metadata and other toll configurations 2 | # Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments 3 | # Local usage: pip install pre-commit, pre-commit run --all-files 4 | 5 | [metadata] 6 | license_file = LICENSE 7 | description_file = README.md 8 | 9 | 10 | [tool:pytest] 11 | norecursedirs = 12 | .git 13 | dist 14 | build 15 | addopts = 16 | --doctest-modules 17 | --durations=25 18 | --color=yes 19 | 20 | 21 | [flake8] 22 | max-line-length = 120 23 | exclude = .tox,*.egg,build,temp 24 | select = E,W,F 25 | doctests = True 26 | verbose = 2 27 | # https://pep8.readthedocs.io/en/latest/intro.html#error-codes 28 | format = pylint 29 | # see: https://www.flake8rules.com/ 30 | ignore = 31 | E731 # Do not assign a lambda expression, use a def 32 | F405 # name may be undefined, or defined from star imports: module 33 | E402 # module level import not at top of file 34 | F401 # module imported but unused 35 | W504 # line break after binary operator 36 | E127 # continuation line over-indented for visual indent 37 | E231 # missing whitespace after ‘,’, ‘;’, or ‘:’ 38 | E501 # line too long 39 | F403 # ‘from module import *’ used; unable to detect undefined names 40 | 41 | 42 | [isort] 43 | # https://pycqa.github.io/isort/docs/configuration/options.html 44 | line_length = 120 45 | # see: https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html 46 | multi_line_output = 0 47 | 48 | 49 | [yapf] 50 | based_on_style = pep8 51 | spaces_before_comment = 2 52 | COLUMN_LIMIT = 120 53 | COALESCE_BRACKETS = True 54 | SPACES_AROUND_POWER_OPERATOR = True 55 | SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False 56 | SPLIT_BEFORE_CLOSING_BRACKET = False 57 | SPLIT_BEFORE_FIRST_ARGUMENT = False 58 | # EACH_DICT_ENTRY_ON_SEPARATE_LINE = False 59 | -------------------------------------------------------------------------------- /yolov5/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | utils/initialization 4 | """ 5 | 6 | import contextlib 7 | import platform 8 | import threading 9 | 10 | 11 | def emojis(str=''): 12 | # Return platform-dependent emoji-safe version of string 13 | return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str 14 | 15 | 16 | class TryExcept(contextlib.ContextDecorator): 17 | # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager 18 | def __init__(self, msg=''): 19 | self.msg = msg 20 | 21 | def __enter__(self): 22 | pass 23 | 24 | def __exit__(self, exc_type, value, traceback): 25 | if value: 26 | print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}")) 27 | return True 28 | 29 | 30 | def threaded(func): 31 | # Multi-threads a target function and returns thread. Usage: @threaded decorator 32 | def wrapper(*args, **kwargs): 33 | thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True) 34 | thread.start() 35 | return thread 36 | 37 | return wrapper 38 | 39 | 40 | def notebook_init(verbose=True): 41 | # Check system software and hardware 42 | print('Checking setup...') 43 | 44 | import os 45 | import shutil 46 | 47 | from utils.general import check_font, check_requirements, is_colab 48 | from utils.torch_utils import select_device # imports 49 | 50 | check_requirements(('psutil', 'IPython')) 51 | check_font() 52 | 53 | import psutil 54 | from IPython import display # to display images and clear console output 55 | 56 | if is_colab(): 57 | shutil.rmtree('/content/sample_data', ignore_errors=True) # remove colab /sample_data directory 58 | 59 | # System info 60 | if verbose: 61 | gb = 1 << 30 # bytes to GiB (1024 ** 3) 62 | ram = psutil.virtual_memory().total 63 | total, used, free = shutil.disk_usage("/") 64 | display.clear_output() 65 | s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)' 66 | else: 67 | s = '' 68 | 69 | select_device(newline=False) 70 | print(emojis(f'Setup complete ✅ {s}')) 71 | return display 72 | -------------------------------------------------------------------------------- /yolov5/utils/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/utils/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /yolov5/utils/__pycache__/augmentations.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/utils/__pycache__/augmentations.cpython-37.pyc 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""" 5 | 6 | import torch 7 | import torch.nn as nn 8 | import torch.nn.functional as F 9 | 10 | 11 | class SiLU(nn.Module): 12 | # SiLU activation https://arxiv.org/pdf/1606.08415.pdf 13 | @staticmethod 14 | def forward(x): 15 | return x * torch.sigmoid(x) 16 | 17 | 18 | class Hardswish(nn.Module): 19 | # Hard-SiLU activation 20 | @staticmethod 21 | def forward(x): 22 | # return x * F.hardsigmoid(x) # for TorchScript and CoreML 23 | return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX 24 | 25 | 26 | class Mish(nn.Module): 27 | # Mish activation https://github.com/digantamisra98/Mish 28 | @staticmethod 29 | def forward(x): 30 | return x * F.softplus(x).tanh() 31 | 32 | 33 | class MemoryEfficientMish(nn.Module): 34 | # Mish activation memory-efficient 35 | class F(torch.autograd.Function): 36 | 37 | @staticmethod 38 | def forward(ctx, x): 39 | ctx.save_for_backward(x) 40 | return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) 41 | 42 | @staticmethod 43 | def backward(ctx, grad_output): 44 | x = ctx.saved_tensors[0] 45 | sx = torch.sigmoid(x) 46 | fx = F.softplus(x).tanh() 47 | return grad_output * (fx + x * sx * (1 - fx * fx)) 48 | 49 | def forward(self, x): 50 | return self.F.apply(x) 51 | 52 | 53 | class FReLU(nn.Module): 54 | # FReLU activation https://arxiv.org/abs/2007.11824 55 | def __init__(self, c1, k=3): # ch_in, kernel 56 | super().__init__() 57 | self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) 58 | self.bn = nn.BatchNorm2d(c1) 59 | 60 | def forward(self, x): 61 | return torch.max(x, self.bn(self.conv(x))) 62 | 63 | 64 | class AconC(nn.Module): 65 | r""" ACON activation (activate or not) 66 | AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter 67 | according to "Activate or Not: Learning Customized Activation" . 68 | """ 69 | 70 | def __init__(self, c1): 71 | super().__init__() 72 | self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) 73 | self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) 74 | self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) 75 | 76 | def forward(self, x): 77 | dpx = (self.p1 - self.p2) * x 78 | return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x 79 | 80 | 81 | class MetaAconC(nn.Module): 82 | r""" ACON activation (activate or not) 83 | MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network 84 | according to "Activate or Not: Learning Customized Activation" . 85 | """ 86 | 87 | def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r 88 | super().__init__() 89 | c2 = max(r, c1 // r) 90 | self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) 91 | self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) 92 | self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) 93 | self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) 94 | # self.bn1 = nn.BatchNorm2d(c2) 95 | # self.bn2 = nn.BatchNorm2d(c1) 96 | 97 | def forward(self, x): 98 | y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) 99 | # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 100 | # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable 101 | beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed 102 | dpx = (self.p1 - self.p2) * x 103 | return dpx * torch.sigmoid(beta * dpx) + self.p2 * x 104 | -------------------------------------------------------------------------------- /yolov5/utils/autobatch.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Auto-batch utils 4 | """ 5 | 6 | from copy import deepcopy 7 | 8 | import numpy as np 9 | import torch 10 | 11 | from utils.general import LOGGER, colorstr 12 | from utils.torch_utils import profile 13 | 14 | 15 | def check_train_batch_size(model, imgsz=640, amp=True): 16 | # Check YOLOv5 training batch size 17 | with torch.cuda.amp.autocast(amp): 18 | return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size 19 | 20 | 21 | def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): 22 | # Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory 23 | # Usage: 24 | # import torch 25 | # from utils.autobatch import autobatch 26 | # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) 27 | # print(autobatch(model)) 28 | 29 | # Check device 30 | prefix = colorstr('AutoBatch: ') 31 | LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}') 32 | device = next(model.parameters()).device # get model device 33 | if device.type == 'cpu': 34 | LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}') 35 | return batch_size 36 | if torch.backends.cudnn.benchmark: 37 | LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}') 38 | return batch_size 39 | 40 | # Inspect CUDA memory 41 | gb = 1 << 30 # bytes to GiB (1024 ** 3) 42 | d = str(device).upper() # 'CUDA:0' 43 | properties = torch.cuda.get_device_properties(device) # device properties 44 | t = properties.total_memory / gb # GiB total 45 | r = torch.cuda.memory_reserved(device) / gb # GiB reserved 46 | a = torch.cuda.memory_allocated(device) / gb # GiB allocated 47 | f = t - (r + a) # GiB free 48 | LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free') 49 | 50 | # Profile batch sizes 51 | batch_sizes = [1, 2, 4, 8, 16] 52 | try: 53 | img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes] 54 | results = profile(img, model, n=3, device=device) 55 | except Exception as e: 56 | LOGGER.warning(f'{prefix}{e}') 57 | 58 | # Fit a solution 59 | y = [x[2] for x in results if x] # memory [2] 60 | p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit 61 | b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) 62 | if None in results: # some sizes failed 63 | i = results.index(None) # first fail index 64 | if b >= batch_sizes[i]: # y intercept above failure point 65 | b = batch_sizes[max(i - 1, 0)] # select prior safe point 66 | if b < 1 or b > 1024: # b outside of safe range 67 | b = batch_size 68 | LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.') 69 | 70 | fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted 71 | LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅') 72 | return b 73 | -------------------------------------------------------------------------------- /yolov5/utils/aws/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/utils/aws/__init__.py -------------------------------------------------------------------------------- /yolov5/utils/aws/mime.sh: -------------------------------------------------------------------------------- 1 | # AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ 2 | # This script will run on every instance restart, not only on first start 3 | # --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- 4 | 5 | Content-Type: multipart/mixed; boundary="//" 6 | MIME-Version: 1.0 7 | 8 | --// 9 | Content-Type: text/cloud-config; charset="us-ascii" 10 | MIME-Version: 1.0 11 | Content-Transfer-Encoding: 7bit 12 | Content-Disposition: attachment; filename="cloud-config.txt" 13 | 14 | #cloud-config 15 | cloud_final_modules: 16 | - [scripts-user, always] 17 | 18 | --// 19 | Content-Type: text/x-shellscript; charset="us-ascii" 20 | MIME-Version: 1.0 21 | Content-Transfer-Encoding: 7bit 22 | Content-Disposition: attachment; filename="userdata.txt" 23 | 24 | #!/bin/bash 25 | # --- paste contents of userdata.sh here --- 26 | --// 27 | -------------------------------------------------------------------------------- /yolov5/utils/aws/resume.py: -------------------------------------------------------------------------------- 1 | # Resume all interrupted trainings in yolov5/ dir including DDP trainings 2 | # Usage: $ python utils/aws/resume.py 3 | 4 | import os 5 | import sys 6 | from pathlib import Path 7 | 8 | import torch 9 | import yaml 10 | 11 | FILE = Path(__file__).resolve() 12 | ROOT = FILE.parents[2] # YOLOv5 root directory 13 | if str(ROOT) not in sys.path: 14 | sys.path.append(str(ROOT)) # add ROOT to PATH 15 | 16 | port = 0 # --master_port 17 | path = Path('').resolve() 18 | for last in path.rglob('*/**/last.pt'): 19 | ckpt = torch.load(last) 20 | if ckpt['optimizer'] is None: 21 | continue 22 | 23 | # Load opt.yaml 24 | with open(last.parent.parent / 'opt.yaml', errors='ignore') as f: 25 | opt = yaml.safe_load(f) 26 | 27 | # Get device count 28 | d = opt['device'].split(',') # devices 29 | nd = len(d) # number of devices 30 | ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel 31 | 32 | if ddp: # multi-GPU 33 | port += 1 34 | cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}' 35 | else: # single-GPU 36 | cmd = f'python train.py --resume {last}' 37 | 38 | cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread 39 | print(cmd) 40 | os.system(cmd) 41 | -------------------------------------------------------------------------------- /yolov5/utils/aws/userdata.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html 3 | # This script will run only once on first instance start (for a re-start script see mime.sh) 4 | # /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir 5 | # Use >300 GB SSD 6 | 7 | cd home/ubuntu 8 | if [ ! -d yolov5 ]; then 9 | echo "Running first-time script." # install dependencies, download COCO, pull Docker 10 | git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 11 | cd yolov5 12 | bash data/scripts/get_coco.sh && echo "COCO done." & 13 | sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & 14 | python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & 15 | wait && echo "All tasks done." # finish background tasks 16 | else 17 | echo "Running re-start script." # resume interrupted runs 18 | i=0 19 | list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' 20 | while IFS= read -r id; do 21 | ((i++)) 22 | echo "restarting container $i: $id" 23 | sudo docker start $id 24 | # sudo docker exec -it $id python train.py --resume # single-GPU 25 | sudo docker exec -d $id python utils/aws/resume.py # multi-scenario 26 | done <<<"$list" 27 | fi 28 | -------------------------------------------------------------------------------- /yolov5/utils/callbacks.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Callback utils 4 | """ 5 | 6 | import threading 7 | 8 | 9 | class Callbacks: 10 | """" 11 | Handles all registered callbacks for YOLOv5 Hooks 12 | """ 13 | 14 | def __init__(self): 15 | # Define the available callbacks 16 | self._callbacks = { 17 | 'on_pretrain_routine_start': [], 18 | 'on_pretrain_routine_end': [], 19 | 'on_train_start': [], 20 | 'on_train_epoch_start': [], 21 | 'on_train_batch_start': [], 22 | 'optimizer_step': [], 23 | 'on_before_zero_grad': [], 24 | 'on_train_batch_end': [], 25 | 'on_train_epoch_end': [], 26 | 'on_val_start': [], 27 | 'on_val_batch_start': [], 28 | 'on_val_image_end': [], 29 | 'on_val_batch_end': [], 30 | 'on_val_end': [], 31 | 'on_fit_epoch_end': [], # fit = train + val 32 | 'on_model_save': [], 33 | 'on_train_end': [], 34 | 'on_params_update': [], 35 | 'teardown': [],} 36 | self.stop_training = False # set True to interrupt training 37 | 38 | def register_action(self, hook, name='', callback=None): 39 | """ 40 | Register a new action to a callback hook 41 | 42 | Args: 43 | hook: The callback hook name to register the action to 44 | name: The name of the action for later reference 45 | callback: The callback to fire 46 | """ 47 | assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" 48 | assert callable(callback), f"callback '{callback}' is not callable" 49 | self._callbacks[hook].append({'name': name, 'callback': callback}) 50 | 51 | def get_registered_actions(self, hook=None): 52 | """" 53 | Returns all the registered actions by callback hook 54 | 55 | Args: 56 | hook: The name of the hook to check, defaults to all 57 | """ 58 | return self._callbacks[hook] if hook else self._callbacks 59 | 60 | def run(self, hook, *args, thread=False, **kwargs): 61 | """ 62 | Loop through the registered actions and fire all callbacks on main thread 63 | 64 | Args: 65 | hook: The name of the hook to check, defaults to all 66 | args: Arguments to receive from YOLOv5 67 | thread: (boolean) Run callbacks in daemon thread 68 | kwargs: Keyword Arguments to receive from YOLOv5 69 | """ 70 | 71 | assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" 72 | for logger in self._callbacks[hook]: 73 | if thread: 74 | threading.Thread(target=logger['callback'], args=args, kwargs=kwargs, daemon=True).start() 75 | else: 76 | logger['callback'](*args, **kwargs) 77 | -------------------------------------------------------------------------------- /yolov5/utils/docker/Dockerfile: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 3 | # Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference 4 | 5 | # Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch 6 | FROM nvcr.io/nvidia/pytorch:22.09-py3 7 | RUN rm -rf /opt/pytorch # remove 1.2GB dir 8 | 9 | # Downloads to user config dir 10 | ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ 11 | 12 | # Install linux packages 13 | RUN apt update && apt install --no-install-recommends -y zip htop screen libgl1-mesa-glx 14 | 15 | # Install pip packages 16 | COPY requirements.txt . 17 | RUN python -m pip install --upgrade pip wheel 18 | RUN pip uninstall -y Pillow torchtext torch torchvision 19 | RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook Pillow>=9.1.0 \ 20 | 'opencv-python<4.6.0.66' \ 21 | --extra-index-url https://download.pytorch.org/whl/cu113 22 | 23 | # Create working directory 24 | RUN mkdir -p /usr/src/app 25 | WORKDIR /usr/src/app 26 | 27 | # Copy contents 28 | # COPY . /usr/src/app (issues as not a .git directory) 29 | RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app 30 | 31 | # Set environment variables 32 | ENV OMP_NUM_THREADS=8 33 | 34 | 35 | # Usage Examples ------------------------------------------------------------------------------------------------------- 36 | 37 | # Build and Push 38 | # t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t 39 | 40 | # Pull and Run 41 | # t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t 42 | 43 | # Pull and Run with local directory access 44 | # t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t 45 | 46 | # Kill all 47 | # sudo docker kill $(sudo docker ps -q) 48 | 49 | # Kill all image-based 50 | # sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) 51 | 52 | # DockerHub tag update 53 | # t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew 54 | 55 | # Clean up 56 | # docker system prune -a --volumes 57 | 58 | # Update Ubuntu drivers 59 | # https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ 60 | 61 | # DDP test 62 | # python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 63 | 64 | # GCP VM from Image 65 | # docker.io/ultralytics/yolov5:latest 66 | -------------------------------------------------------------------------------- /yolov5/utils/docker/Dockerfile-arm64: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 3 | # Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi 4 | 5 | # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu 6 | FROM arm64v8/ubuntu:20.04 7 | 8 | # Downloads to user config dir 9 | ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ 10 | 11 | # Install linux packages 12 | RUN apt update 13 | RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt install -y tzdata 14 | RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev 15 | # RUN alias python=python3 16 | 17 | # Install pip packages 18 | COPY requirements.txt . 19 | RUN python3 -m pip install --upgrade pip wheel 20 | RUN pip install --no-cache -r requirements.txt gsutil notebook \ 21 | tensorflow-aarch64 22 | # tensorflowjs \ 23 | # onnx onnx-simplifier onnxruntime \ 24 | # coremltools openvino-dev \ 25 | 26 | # Create working directory 27 | RUN mkdir -p /usr/src/app 28 | WORKDIR /usr/src/app 29 | 30 | # Copy contents 31 | # COPY . /usr/src/app (issues as not a .git directory) 32 | RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app 33 | 34 | 35 | # Usage Examples ------------------------------------------------------------------------------------------------------- 36 | 37 | # Build and Push 38 | # t=ultralytics/yolov5:latest-M1 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t 39 | 40 | # Pull and Run 41 | # t=ultralytics/yolov5:latest-M1 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t 42 | -------------------------------------------------------------------------------- /yolov5/utils/docker/Dockerfile-cpu: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 3 | # Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments 4 | 5 | # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu 6 | FROM ubuntu:20.04 7 | 8 | # Downloads to user config dir 9 | ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ 10 | 11 | # Install linux packages 12 | RUN apt update 13 | RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt install -y tzdata 14 | RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg 15 | # RUN alias python=python3 16 | 17 | # Install pip packages 18 | COPY requirements.txt . 19 | RUN python3 -m pip install --upgrade pip wheel 20 | RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ 21 | coremltools onnx onnx-simplifier onnxruntime tensorflow-cpu tensorflowjs \ 22 | # openvino-dev \ 23 | --extra-index-url https://download.pytorch.org/whl/cpu 24 | 25 | # Create working directory 26 | RUN mkdir -p /usr/src/app 27 | WORKDIR /usr/src/app 28 | 29 | # Copy contents 30 | # COPY . /usr/src/app (issues as not a .git directory) 31 | RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app 32 | 33 | 34 | # Usage Examples ------------------------------------------------------------------------------------------------------- 35 | 36 | # Build and Push 37 | # t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t 38 | 39 | # Pull and Run 40 | # t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t 41 | -------------------------------------------------------------------------------- /yolov5/utils/flask_rest_api/README.md: -------------------------------------------------------------------------------- 1 | # Flask REST API 2 | 3 | [REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are 4 | commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API 5 | created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/). 6 | 7 | ## Requirements 8 | 9 | [Flask](https://palletsprojects.com/p/flask/) is required. Install with: 10 | 11 | ```shell 12 | $ pip install Flask 13 | ``` 14 | 15 | ## Run 16 | 17 | After Flask installation run: 18 | 19 | ```shell 20 | $ python3 restapi.py --port 5000 21 | ``` 22 | 23 | Then use [curl](https://curl.se/) to perform a request: 24 | 25 | ```shell 26 | $ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s' 27 | ``` 28 | 29 | The model inference results are returned as a JSON response: 30 | 31 | ```json 32 | [ 33 | { 34 | "class": 0, 35 | "confidence": 0.8900438547, 36 | "height": 0.9318675399, 37 | "name": "person", 38 | "width": 0.3264600933, 39 | "xcenter": 0.7438579798, 40 | "ycenter": 0.5207948685 41 | }, 42 | { 43 | "class": 0, 44 | "confidence": 0.8440024257, 45 | "height": 0.7155083418, 46 | "name": "person", 47 | "width": 0.6546785235, 48 | "xcenter": 0.427829951, 49 | "ycenter": 0.6334488392 50 | }, 51 | { 52 | "class": 27, 53 | "confidence": 0.3771208823, 54 | "height": 0.3902671337, 55 | "name": "tie", 56 | "width": 0.0696444362, 57 | "xcenter": 0.3675483763, 58 | "ycenter": 0.7991207838 59 | }, 60 | { 61 | "class": 27, 62 | "confidence": 0.3527112305, 63 | "height": 0.1540903747, 64 | "name": "tie", 65 | "width": 0.0336618312, 66 | "xcenter": 0.7814827561, 67 | "ycenter": 0.5065554976 68 | } 69 | ] 70 | ``` 71 | 72 | An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given 73 | in `example_request.py` 74 | -------------------------------------------------------------------------------- /yolov5/utils/flask_rest_api/example_request.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Perform test request 4 | """ 5 | 6 | import pprint 7 | 8 | import requests 9 | 10 | DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" 11 | IMAGE = "zidane.jpg" 12 | 13 | # Read image 14 | with open(IMAGE, "rb") as f: 15 | image_data = f.read() 16 | 17 | response = requests.post(DETECTION_URL, files={"image": image_data}).json() 18 | 19 | pprint.pprint(response) 20 | -------------------------------------------------------------------------------- /yolov5/utils/flask_rest_api/restapi.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Run a Flask REST API exposing one or more YOLOv5s models 4 | """ 5 | 6 | import argparse 7 | import io 8 | 9 | import torch 10 | from flask import Flask, request 11 | from PIL import Image 12 | 13 | app = Flask(__name__) 14 | models = {} 15 | 16 | DETECTION_URL = "/v1/object-detection/" 17 | 18 | 19 | @app.route(DETECTION_URL, methods=["POST"]) 20 | def predict(model): 21 | if request.method != "POST": 22 | return 23 | 24 | if request.files.get("image"): 25 | # Method 1 26 | # with request.files["image"] as f: 27 | # im = Image.open(io.BytesIO(f.read())) 28 | 29 | # Method 2 30 | im_file = request.files["image"] 31 | im_bytes = im_file.read() 32 | im = Image.open(io.BytesIO(im_bytes)) 33 | 34 | if model in models: 35 | results = models[model](im, size=640) # reduce size=320 for faster inference 36 | return results.pandas().xyxy[0].to_json(orient="records") 37 | 38 | 39 | if __name__ == "__main__": 40 | parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") 41 | parser.add_argument("--port", default=5000, type=int, help="port number") 42 | parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s') 43 | opt = parser.parse_args() 44 | 45 | for m in opt.model: 46 | models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True) 47 | 48 | app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat 49 | -------------------------------------------------------------------------------- /yolov5/utils/google_app_engine/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM gcr.io/google-appengine/python 2 | 3 | # Create a virtualenv for dependencies. This isolates these packages from 4 | # system-level packages. 5 | # Use -p python3 or -p python3.7 to select python version. Default is version 2. 6 | RUN virtualenv /env -p python3 7 | 8 | # Setting these environment variables are the same as running 9 | # source /env/bin/activate. 10 | ENV VIRTUAL_ENV /env 11 | ENV PATH /env/bin:$PATH 12 | 13 | RUN apt-get update && apt-get install -y python-opencv 14 | 15 | # Copy the application's requirements.txt and run pip to install all 16 | # dependencies into the virtualenv. 17 | ADD requirements.txt /app/requirements.txt 18 | RUN pip install -r /app/requirements.txt 19 | 20 | # Add the application source code. 21 | ADD . /app 22 | 23 | # Run a WSGI server to serve the application. gunicorn must be declared as 24 | # a dependency in requirements.txt. 25 | CMD gunicorn -b :$PORT main:app 26 | -------------------------------------------------------------------------------- /yolov5/utils/google_app_engine/additional_requirements.txt: -------------------------------------------------------------------------------- 1 | # add these requirements in your app on top of the existing ones 2 | pip==21.1 3 | Flask==1.0.2 4 | gunicorn==19.9.0 5 | -------------------------------------------------------------------------------- /yolov5/utils/google_app_engine/app.yaml: -------------------------------------------------------------------------------- 1 | runtime: custom 2 | env: flex 3 | 4 | service: yolov5app 5 | 6 | liveness_check: 7 | initial_delay_sec: 600 8 | 9 | manual_scaling: 10 | instances: 1 11 | resources: 12 | cpu: 1 13 | memory_gb: 4 14 | disk_size_gb: 20 15 | -------------------------------------------------------------------------------- /yolov5/utils/loggers/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/utils/loggers/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /yolov5/utils/loggers/comet/optimizer_config.json: -------------------------------------------------------------------------------- 1 | { 2 | "algorithm": "random", 3 | "parameters": { 4 | "anchor_t": { 5 | "type": "discrete", 6 | "values": [ 7 | 2, 8 | 8 9 | ] 10 | }, 11 | "batch_size": { 12 | "type": "discrete", 13 | "values": [ 14 | 16, 15 | 32, 16 | 64 17 | ] 18 | }, 19 | "box": { 20 | "type": "discrete", 21 | "values": [ 22 | 0.02, 23 | 0.2 24 | ] 25 | }, 26 | "cls": { 27 | "type": "discrete", 28 | "values": [ 29 | 0.2 30 | ] 31 | }, 32 | "cls_pw": { 33 | "type": "discrete", 34 | "values": [ 35 | 0.5 36 | ] 37 | }, 38 | "copy_paste": { 39 | "type": "discrete", 40 | "values": [ 41 | 1 42 | ] 43 | }, 44 | "degrees": { 45 | "type": "discrete", 46 | "values": [ 47 | 0, 48 | 45 49 | ] 50 | }, 51 | "epochs": { 52 | "type": "discrete", 53 | "values": [ 54 | 5 55 | ] 56 | }, 57 | "fl_gamma": { 58 | "type": "discrete", 59 | "values": [ 60 | 0 61 | ] 62 | }, 63 | "fliplr": { 64 | "type": "discrete", 65 | "values": [ 66 | 0 67 | ] 68 | }, 69 | "flipud": { 70 | "type": "discrete", 71 | "values": [ 72 | 0 73 | ] 74 | }, 75 | "hsv_h": { 76 | "type": "discrete", 77 | "values": [ 78 | 0 79 | ] 80 | }, 81 | "hsv_s": { 82 | "type": "discrete", 83 | "values": [ 84 | 0 85 | ] 86 | }, 87 | "hsv_v": { 88 | "type": "discrete", 89 | "values": [ 90 | 0 91 | ] 92 | }, 93 | "iou_t": { 94 | "type": "discrete", 95 | "values": [ 96 | 0.7 97 | ] 98 | }, 99 | "lr0": { 100 | "type": "discrete", 101 | "values": [ 102 | 1e-05, 103 | 0.1 104 | ] 105 | }, 106 | "lrf": { 107 | "type": "discrete", 108 | "values": [ 109 | 0.01, 110 | 1 111 | ] 112 | }, 113 | "mixup": { 114 | "type": "discrete", 115 | "values": [ 116 | 1 117 | ] 118 | }, 119 | "momentum": { 120 | "type": "discrete", 121 | "values": [ 122 | 0.6 123 | ] 124 | }, 125 | "mosaic": { 126 | "type": "discrete", 127 | "values": [ 128 | 0 129 | ] 130 | }, 131 | "obj": { 132 | "type": "discrete", 133 | "values": [ 134 | 0.2 135 | ] 136 | }, 137 | "obj_pw": { 138 | "type": "discrete", 139 | "values": [ 140 | 0.5 141 | ] 142 | }, 143 | "optimizer": { 144 | "type": "categorical", 145 | "values": [ 146 | "SGD", 147 | "Adam", 148 | "AdamW" 149 | ] 150 | }, 151 | "perspective": { 152 | "type": "discrete", 153 | "values": [ 154 | 0 155 | ] 156 | }, 157 | "scale": { 158 | "type": "discrete", 159 | "values": [ 160 | 0 161 | ] 162 | }, 163 | "shear": { 164 | "type": "discrete", 165 | "values": [ 166 | 0 167 | ] 168 | }, 169 | "translate": { 170 | "type": "discrete", 171 | "values": [ 172 | 0 173 | ] 174 | }, 175 | "warmup_bias_lr": { 176 | "type": "discrete", 177 | "values": [ 178 | 0, 179 | 0.2 180 | ] 181 | }, 182 | "warmup_epochs": { 183 | "type": "discrete", 184 | "values": [ 185 | 5 186 | ] 187 | }, 188 | "warmup_momentum": { 189 | "type": "discrete", 190 | "values": [ 191 | 0, 192 | 0.95 193 | ] 194 | }, 195 | "weight_decay": { 196 | "type": "discrete", 197 | "values": [ 198 | 0, 199 | 0.001 200 | ] 201 | } 202 | }, 203 | "spec": { 204 | "maxCombo": 0, 205 | "metric": "metrics/mAP_0.5", 206 | "objective": "maximize" 207 | }, 208 | "trials": 1 209 | } 210 | -------------------------------------------------------------------------------- /yolov5/utils/loggers/wandb/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/utils/loggers/wandb/__init__.py -------------------------------------------------------------------------------- /yolov5/utils/loggers/wandb/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/utils/loggers/wandb/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /yolov5/utils/loggers/wandb/__pycache__/wandb_utils.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/utils/loggers/wandb/__pycache__/wandb_utils.cpython-37.pyc -------------------------------------------------------------------------------- /yolov5/utils/loggers/wandb/log_dataset.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | from wandb_utils import WandbLogger 4 | 5 | from utils.general import LOGGER 6 | 7 | WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' 8 | 9 | 10 | def create_dataset_artifact(opt): 11 | logger = WandbLogger(opt, None, job_type='Dataset Creation') # TODO: return value unused 12 | if not logger.wandb: 13 | LOGGER.info("install wandb using `pip install wandb` to log the dataset") 14 | 15 | 16 | if __name__ == '__main__': 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') 19 | parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') 20 | parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') 21 | parser.add_argument('--entity', default=None, help='W&B entity') 22 | parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run') 23 | 24 | opt = parser.parse_args() 25 | opt.resume = False # Explicitly disallow resume check for dataset upload job 26 | 27 | create_dataset_artifact(opt) 28 | -------------------------------------------------------------------------------- /yolov5/utils/loggers/wandb/sweep.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from pathlib import Path 3 | 4 | import wandb 5 | 6 | FILE = Path(__file__).resolve() 7 | ROOT = FILE.parents[3] # YOLOv5 root directory 8 | if str(ROOT) not in sys.path: 9 | sys.path.append(str(ROOT)) # add ROOT to PATH 10 | 11 | from train import parse_opt, train 12 | from utils.callbacks import Callbacks 13 | from utils.general import increment_path 14 | from utils.torch_utils import select_device 15 | 16 | 17 | def sweep(): 18 | wandb.init() 19 | # Get hyp dict from sweep agent. Copy because train() modifies parameters which confused wandb. 20 | hyp_dict = vars(wandb.config).get("_items").copy() 21 | 22 | # Workaround: get necessary opt args 23 | opt = parse_opt(known=True) 24 | opt.batch_size = hyp_dict.get("batch_size") 25 | opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) 26 | opt.epochs = hyp_dict.get("epochs") 27 | opt.nosave = True 28 | opt.data = hyp_dict.get("data") 29 | opt.weights = str(opt.weights) 30 | opt.cfg = str(opt.cfg) 31 | opt.data = str(opt.data) 32 | opt.hyp = str(opt.hyp) 33 | opt.project = str(opt.project) 34 | device = select_device(opt.device, batch_size=opt.batch_size) 35 | 36 | # train 37 | train(hyp_dict, opt, device, callbacks=Callbacks()) 38 | 39 | 40 | if __name__ == "__main__": 41 | sweep() 42 | -------------------------------------------------------------------------------- /yolov5/utils/loggers/wandb/sweep.yaml: -------------------------------------------------------------------------------- 1 | # Hyperparameters for training 2 | # To set range- 3 | # Provide min and max values as: 4 | # parameter: 5 | # 6 | # min: scalar 7 | # max: scalar 8 | # OR 9 | # 10 | # Set a specific list of search space- 11 | # parameter: 12 | # values: [scalar1, scalar2, scalar3...] 13 | # 14 | # You can use grid, bayesian and hyperopt search strategy 15 | # For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration 16 | 17 | program: utils/loggers/wandb/sweep.py 18 | method: random 19 | metric: 20 | name: metrics/mAP_0.5 21 | goal: maximize 22 | 23 | parameters: 24 | # hyperparameters: set either min, max range or values list 25 | data: 26 | value: "data/coco128.yaml" 27 | batch_size: 28 | values: [64] 29 | epochs: 30 | values: [10] 31 | 32 | lr0: 33 | distribution: uniform 34 | min: 1e-5 35 | max: 1e-1 36 | lrf: 37 | distribution: uniform 38 | min: 0.01 39 | max: 1.0 40 | momentum: 41 | distribution: uniform 42 | min: 0.6 43 | max: 0.98 44 | weight_decay: 45 | distribution: uniform 46 | min: 0.0 47 | max: 0.001 48 | warmup_epochs: 49 | distribution: uniform 50 | min: 0.0 51 | max: 5.0 52 | warmup_momentum: 53 | distribution: uniform 54 | min: 0.0 55 | max: 0.95 56 | warmup_bias_lr: 57 | distribution: uniform 58 | min: 0.0 59 | max: 0.2 60 | box: 61 | distribution: uniform 62 | min: 0.02 63 | max: 0.2 64 | cls: 65 | distribution: uniform 66 | min: 0.2 67 | max: 4.0 68 | cls_pw: 69 | distribution: uniform 70 | min: 0.5 71 | max: 2.0 72 | obj: 73 | distribution: uniform 74 | min: 0.2 75 | max: 4.0 76 | obj_pw: 77 | distribution: uniform 78 | min: 0.5 79 | max: 2.0 80 | iou_t: 81 | distribution: uniform 82 | min: 0.1 83 | max: 0.7 84 | anchor_t: 85 | distribution: uniform 86 | min: 2.0 87 | max: 8.0 88 | fl_gamma: 89 | distribution: uniform 90 | min: 0.0 91 | max: 4.0 92 | hsv_h: 93 | distribution: uniform 94 | min: 0.0 95 | max: 0.1 96 | hsv_s: 97 | distribution: uniform 98 | min: 0.0 99 | max: 0.9 100 | hsv_v: 101 | distribution: uniform 102 | min: 0.0 103 | max: 0.9 104 | degrees: 105 | distribution: uniform 106 | min: 0.0 107 | max: 45.0 108 | translate: 109 | distribution: uniform 110 | min: 0.0 111 | max: 0.9 112 | scale: 113 | distribution: uniform 114 | min: 0.0 115 | max: 0.9 116 | shear: 117 | distribution: uniform 118 | min: 0.0 119 | max: 10.0 120 | perspective: 121 | distribution: uniform 122 | min: 0.0 123 | max: 0.001 124 | flipud: 125 | distribution: uniform 126 | min: 0.0 127 | max: 1.0 128 | fliplr: 129 | distribution: uniform 130 | min: 0.0 131 | max: 1.0 132 | mosaic: 133 | distribution: uniform 134 | min: 0.0 135 | max: 1.0 136 | mixup: 137 | distribution: uniform 138 | min: 0.0 139 | max: 1.0 140 | copy_paste: 141 | distribution: uniform 142 | min: 0.0 143 | max: 1.0 144 | -------------------------------------------------------------------------------- /yolov5/utils/segment/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/utils/segment/__init__.py -------------------------------------------------------------------------------- /yolov5/utils/segment/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/utils/segment/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /yolov5/utils/segment/__pycache__/general.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/utils/segment/__pycache__/general.cpython-37.pyc -------------------------------------------------------------------------------- /yolov5/utils/segment/augmentations.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Image augmentation functions 4 | """ 5 | 6 | import math 7 | import random 8 | 9 | import cv2 10 | import numpy as np 11 | 12 | from ..augmentations import box_candidates 13 | from ..general import resample_segments, segment2box 14 | 15 | 16 | def mixup(im, labels, segments, im2, labels2, segments2): 17 | # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf 18 | r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 19 | im = (im * r + im2 * (1 - r)).astype(np.uint8) 20 | labels = np.concatenate((labels, labels2), 0) 21 | segments = np.concatenate((segments, segments2), 0) 22 | return im, labels, segments 23 | 24 | 25 | def random_perspective(im, 26 | targets=(), 27 | segments=(), 28 | degrees=10, 29 | translate=.1, 30 | scale=.1, 31 | shear=10, 32 | perspective=0.0, 33 | border=(0, 0)): 34 | # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) 35 | # targets = [cls, xyxy] 36 | 37 | height = im.shape[0] + border[0] * 2 # shape(h,w,c) 38 | width = im.shape[1] + border[1] * 2 39 | 40 | # Center 41 | C = np.eye(3) 42 | C[0, 2] = -im.shape[1] / 2 # x translation (pixels) 43 | C[1, 2] = -im.shape[0] / 2 # y translation (pixels) 44 | 45 | # Perspective 46 | P = np.eye(3) 47 | P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) 48 | P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) 49 | 50 | # Rotation and Scale 51 | R = np.eye(3) 52 | a = random.uniform(-degrees, degrees) 53 | # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations 54 | s = random.uniform(1 - scale, 1 + scale) 55 | # s = 2 ** random.uniform(-scale, scale) 56 | R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) 57 | 58 | # Shear 59 | S = np.eye(3) 60 | S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) 61 | S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) 62 | 63 | # Translation 64 | T = np.eye(3) 65 | T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels) 66 | T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels) 67 | 68 | # Combined rotation matrix 69 | M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT 70 | if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed 71 | if perspective: 72 | im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) 73 | else: # affine 74 | im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) 75 | 76 | # Visualize 77 | # import matplotlib.pyplot as plt 78 | # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() 79 | # ax[0].imshow(im[:, :, ::-1]) # base 80 | # ax[1].imshow(im2[:, :, ::-1]) # warped 81 | 82 | # Transform label coordinates 83 | n = len(targets) 84 | new_segments = [] 85 | if n: 86 | new = np.zeros((n, 4)) 87 | segments = resample_segments(segments) # upsample 88 | for i, segment in enumerate(segments): 89 | xy = np.ones((len(segment), 3)) 90 | xy[:, :2] = segment 91 | xy = xy @ M.T # transform 92 | xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine 93 | 94 | # clip 95 | new[i] = segment2box(xy, width, height) 96 | new_segments.append(xy) 97 | 98 | # filter candidates 99 | i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01) 100 | targets = targets[i] 101 | targets[:, 1:5] = new[i] 102 | new_segments = np.array(new_segments)[i] 103 | 104 | return im, targets, new_segments 105 | -------------------------------------------------------------------------------- /yolov5/utils/triton.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ Utils to interact with the Triton Inference Server 3 | """ 4 | 5 | import typing 6 | from urllib.parse import urlparse 7 | 8 | import torch 9 | 10 | 11 | class TritonRemoteModel: 12 | """ A wrapper over a model served by the Triton Inference Server. It can 13 | be configured to communicate over GRPC or HTTP. It accepts Torch Tensors 14 | as input and returns them as outputs. 15 | """ 16 | 17 | def __init__(self, url: str): 18 | """ 19 | Keyword arguments: 20 | url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000 21 | """ 22 | 23 | parsed_url = urlparse(url) 24 | if parsed_url.scheme == "grpc": 25 | from tritonclient.grpc import InferenceServerClient, InferInput 26 | 27 | self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client 28 | model_repository = self.client.get_model_repository_index() 29 | self.model_name = model_repository.models[0].name 30 | self.metadata = self.client.get_model_metadata(self.model_name, as_json=True) 31 | 32 | def create_input_placeholders() -> typing.List[InferInput]: 33 | return [ 34 | InferInput(i['name'], [int(s) for s in i["shape"]], i['datatype']) for i in self.metadata['inputs']] 35 | 36 | else: 37 | from tritonclient.http import InferenceServerClient, InferInput 38 | 39 | self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client 40 | model_repository = self.client.get_model_repository_index() 41 | self.model_name = model_repository[0]['name'] 42 | self.metadata = self.client.get_model_metadata(self.model_name) 43 | 44 | def create_input_placeholders() -> typing.List[InferInput]: 45 | return [ 46 | InferInput(i['name'], [int(s) for s in i["shape"]], i['datatype']) for i in self.metadata['inputs']] 47 | 48 | self._create_input_placeholders_fn = create_input_placeholders 49 | 50 | @property 51 | def runtime(self): 52 | """Returns the model runtime""" 53 | return self.metadata.get("backend", self.metadata.get("platform")) 54 | 55 | def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]: 56 | """ Invokes the model. Parameters can be provided via args or kwargs. 57 | args, if provided, are assumed to match the order of inputs of the model. 58 | kwargs are matched with the model input names. 59 | """ 60 | inputs = self._create_inputs(*args, **kwargs) 61 | response = self.client.infer(model_name=self.model_name, inputs=inputs) 62 | result = [] 63 | for output in self.metadata['outputs']: 64 | tensor = torch.as_tensor(response.as_numpy(output['name'])) 65 | result.append(tensor) 66 | return result[0] if len(result) == 1 else result 67 | 68 | def _create_inputs(self, *args, **kwargs): 69 | args_len, kwargs_len = len(args), len(kwargs) 70 | if not args_len and not kwargs_len: 71 | raise RuntimeError("No inputs provided.") 72 | if args_len and kwargs_len: 73 | raise RuntimeError("Cannot specify args and kwargs at the same time") 74 | 75 | placeholders = self._create_input_placeholders_fn() 76 | if args_len: 77 | if args_len != len(placeholders): 78 | raise RuntimeError(f"Expected {len(placeholders)} inputs, got {args_len}.") 79 | for input, value in zip(placeholders, args): 80 | input.set_data_from_numpy(value.cpu().numpy()) 81 | else: 82 | for input in placeholders: 83 | value = kwargs[input.name] 84 | input.set_data_from_numpy(value.cpu().numpy()) 85 | return placeholders 86 | -------------------------------------------------------------------------------- /yolov5/yolov5s.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xiaoheimaoo/GeetestCrack/e1bc1b745fe8ed8538221e5e646d5995bf4a2131/yolov5/yolov5s.pt --------------------------------------------------------------------------------