{"id":50718188,"url":"https://github.com/Michael-OvO/Yolov7-Flask","last_synced_at":"2026-06-26T22:00:39.515Z","repository":{"id":58616829,"uuid":"528245351","full_name":"Michael-OvO/Yolov7-Flask","owner":"Michael-OvO","description":"A Beautiful Flask Web API for Yolov7 (and custom) 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for AI"],"sub_categories":["Model Serving \u0026 Inference"],"readme":"\u003cH1 align=\"center\"\u003e\nYolov7 Flask \u003c/H1\u003e\n\u003ch4 align = \"center\"\u003e\nA Beautiful Flask Framework for Implementing the Latest Yolov7 Model \u003c/h4\u003e\n\u003cp align = \"center\"\u003e\n  \u003cimg alt=\"GitHub Repo stars\" src=\"https://img.shields.io/github/stars/Michael-OvO/Yolov7-Flask?label=Please%20Support%20by%20Giving%20a%20Star\u0026logoColor=blue\u0026style=social\"\u003e\n  \u003cimg alt=\"GitHub followers\" src=\"https://img.shields.io/github/followers/Michael-OvO?logoColor=blue\u0026style=social\"\u003e\n  \u003cimg alt=\"GitHub watchers\" src=\"https://img.shields.io/github/watchers/Michael-OvO/Yolov7-Flask?logoColor=blue\u0026style=social\"\u003e\n  \u003cimg alt=\"GitHub forks\" src=\"https://img.shields.io/github/forks/Michael-OvO/Yolov7-Flask?logoColor=blue\u0026style=social\"\u003e\n\u003c/p\u003e\n\u003cdiv align = \"center\"\u003e\n  \u003cimg src=\"http://ForTheBadge.com/images/badges/made-with-python.svg\"\u003e\n  \u003cimg src = \"http://ForTheBadge.com/images/badges/built-with-love.svg\"\u003e \u003cbr\u003e\n\u003c/div\u003e \n\n\n\u003cdiv align=\"center\"\u003e\u003cimg src=\"https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge\u0026logo=PyTorch\u0026logoColor=white\"\u003e\n  \u003cimg src = \"https://img.shields.io/badge/Flask-000000?style=for-the-badge\u0026logo=flask\u0026logoColor=white\"\u003e\n  \u003cbr\u003e\n  \u003cimg src = \"https://repobeats.axiom.co/api/embed/a081b19ca4f52df2a34a35438450f603046c73fb.svg\"\u003e\n  \n\u003c/div\u003e\nI developed this API for the purpose of deploying my own Yolov7 model, which is a very accurate skin burn detector. For more information about that project please check out this repo:\n\nhttps://github.com/Michael-OvO/Burn-Detection-Classification\n\n\n\n## Installation \u0026 Usage: \n\n![](./figures/Flask_webapp_view3.png)\n\n![](./figures/Flask_webapp_view2.png)\n\nTo run this, please make sure you follow the following steps:\n\na trained Yolov7 model (or you can also use the official pretrained yolov7 models), they can be downloaded [here](https://github.com/WongKinYiu/yolov7/releases). \n\nOnce you have downloaded files, proceed to the next step. The feature of this web app is that it does not require a specific model name, as I have written code to directly search for the model file that is inside this directory (so you do not need to modify anything and just run it). **But do note that please just put one model file into your directory a single time, or else the code will not run properly.**  - the green bar on top of the page will display which model is currently being inferenced on your machine. \n\nMake sure you have met the following requirements:\n\n\\- PyTorch \u003e= 1.6\n\n\\- flask\n\n\\- and dependencies required by Yolov7 (if you git cloned the original yolov7 repo then simply run ``` pip install -r requirements.txt``` inside the yolov7 repo)  \n\nthen, to launch the app, run the following command:\n\n```bash\n$ FLASK_ENV=development FLASK_APP=app.py flask run\n```\n\nthen, visit http://localhost:5000/ in your browser.\n\nchoose some pictures that the model has been trained on and test it out!\n\n## Demonstration: \n\nI will be using ```yolov7-e6e.pt``` for this demo and I am currently working with a RTX 3070Ti. \n\nMy directory setup is like this:\n\n![](./figures/setup.png)\n\nThen running the ```app.py``` yields the following output:\n\n(if it is first time running, it may take a while to download the original repo\n\n```bash\nUsing cache found in C:[PATH/To/Your/Cache].cache\\torch\\hub\\WongKinYiu_yolov7_main\n\n                 from  n    params  module                                  arguments\n  0                -1  1         0  models.common.ReOrg                     []\n  1                -1  1      8800  models.common.Conv                      [12, 80, 3, 1]\n  2                -1  1     70880  models.common.DownC                     [80, 160, 1]\n  3                -1  1     10368  models.common.Conv                      [160, 64, 1, 1]\n  4                -2  1     10368  models.common.Conv                      [160, 64, 1, 1]\n  5                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n  6                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n  7                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n  8                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n  9                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n 10                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n 11[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]\n 12                -1  1     51520  models.common.Conv                      [320, 160, 1, 1]\n 13               -11  1     10368  models.common.Conv                      [160, 64, 1, 1]\n 14               -12  1     10368  models.common.Conv                      [160, 64, 1, 1]\n 15                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n 16                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n 17                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n 18                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n 19                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n 20                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n 21[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]\n 22                -1  1     51520  models.common.Conv                      [320, 160, 1, 1]\n 23         [-1, -11]  1         0  models.common.Shortcut                  [1]\n 24                -1  1    282560  models.common.DownC                     [160, 320, 1]\n 25                -1  1     41216  models.common.Conv                      [320, 128, 1, 1]\n 26                -2  1     41216  models.common.Conv                      [320, 128, 1, 1]\n 27                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 28                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 29                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 30                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 31                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 32                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 33[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]\n 34                -1  1    205440  models.common.Conv                      [640, 320, 1, 1]\n 35               -11  1     41216  models.common.Conv                      [320, 128, 1, 1]\n 36               -12  1     41216  models.common.Conv                      [320, 128, 1, 1]\n 37                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 38                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 39                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 40                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 41                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 42                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n 43[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]\n 44                -1  1    205440  models.common.Conv                      [640, 320, 1, 1]\n 45         [-1, -11]  1         0  models.common.Shortcut                  [1]\n 46                -1  1   1128320  models.common.DownC                     [320, 640, 1]\n 47                -1  1    164352  models.common.Conv                      [640, 256, 1, 1]\n 48                -2  1    164352  models.common.Conv                      [640, 256, 1, 1]\n 49                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 50                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 51                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 52                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 53                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 54                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 55[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]\n 56                -1  1    820480  models.common.Conv                      [1280, 640, 1, 1]\n 57               -11  1    164352  models.common.Conv                      [640, 256, 1, 1]\n 58               -12  1    164352  models.common.Conv                      [640, 256, 1, 1]\n 59                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 60                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 61                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 62                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 63                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 64                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n 65[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]\n 66                -1  1    820480  models.common.Conv                      [1280, 640, 1, 1]\n 67         [-1, -11]  1         0  models.common.Shortcut                  [1]\n 68                -1  1   3484800  models.common.DownC                     [640, 960, 1]\n 69                -1  1    369408  models.common.Conv                      [960, 384, 1, 1]\n 70                -2  1    369408  models.common.Conv                      [960, 384, 1, 1]\n 71                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 72                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 73                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 74                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 75                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 76                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 77[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]\n 78                -1  1   1845120  models.common.Conv                      [1920, 960, 1, 1]\n 79               -11  1    369408  models.common.Conv                      [960, 384, 1, 1]\n 80               -12  1    369408  models.common.Conv                      [960, 384, 1, 1]\n 81                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 82                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 83                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 84                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 85                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 86                -1  1   1327872  models.common.Conv                      [384, 384, 3, 1]\n 87[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]\n 88                -1  1   1845120  models.common.Conv                      [1920, 960, 1, 1]\n 89         [-1, -11]  1         0  models.common.Shortcut                  [1]\n 90                -1  1   7070080  models.common.DownC                     [960, 1280, 1]\n 91                -1  1    656384  models.common.Conv                      [1280, 512, 1, 1]\n 92                -2  1    656384  models.common.Conv                      [1280, 512, 1, 1]\n 93                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n 94                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n 95                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n 96                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n 97                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n 98                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n 99[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]\n100                -1  1   3279360  models.common.Conv                      [2560, 1280, 1, 1]\n101               -11  1    656384  models.common.Conv                      [1280, 512, 1, 1]\n102               -12  1    656384  models.common.Conv                      [1280, 512, 1, 1]\n103                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n104                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n105                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n106                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n107                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n108                -1  1   2360320  models.common.Conv                      [512, 512, 3, 1]\n109[-1, -3, -5, -7, -8]  1         0  models.common.Concat                    [1]\n110                -1  1   3279360  models.common.Conv                      [2560, 1280, 1, 1]\n111         [-1, -11]  1         0  models.common.Shortcut                  [1]\n112                -1  1  11887360  models.common.SPPCSPC                   [1280, 640, 1]\n113                -1  1    308160  models.common.Conv                      [640, 480, 1, 1]\n114                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']\n115                89  1    461760  models.common.Conv                      [960, 480, 1, 1]\n116          [-1, -2]  1         0  models.common.Concat                    [1]\n117                -1  1    369408  models.common.Conv                      [960, 384, 1, 1]\n118                -2  1    369408  models.common.Conv                      [960, 384, 1, 1]\n119                -1  1    663936  models.common.Conv                      [384, 192, 3, 1]\n120                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n121                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n122                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n123                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n124                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n125[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n126                -1  1    922560  models.common.Conv                      [1920, 480, 1, 1]\n127               -11  1    369408  models.common.Conv                      [960, 384, 1, 1]\n128               -12  1    369408  models.common.Conv                      [960, 384, 1, 1]\n129                -1  1    663936  models.common.Conv                      [384, 192, 3, 1]\n130                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n131                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n132                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n133                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n134                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n135[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n136                -1  1    922560  models.common.Conv                      [1920, 480, 1, 1]\n137         [-1, -11]  1         0  models.common.Shortcut                  [1]\n138                -1  1    154240  models.common.Conv                      [480, 320, 1, 1]\n139                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']\n140                67  1    205440  models.common.Conv                      [640, 320, 1, 1]\n141          [-1, -2]  1         0  models.common.Concat                    [1]\n142                -1  1    164352  models.common.Conv                      [640, 256, 1, 1]\n143                -2  1    164352  models.common.Conv                      [640, 256, 1, 1]\n144                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]\n145                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n146                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n147                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n148                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n149                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n150[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n151                -1  1    410240  models.common.Conv                      [1280, 320, 1, 1]\n152               -11  1    164352  models.common.Conv                      [640, 256, 1, 1]\n153               -12  1    164352  models.common.Conv                      [640, 256, 1, 1]\n154                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]\n155                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n156                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n157                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n158                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n159                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n160[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n161                -1  1    410240  models.common.Conv                      [1280, 320, 1, 1]\n162         [-1, -11]  1         0  models.common.Shortcut                  [1]\n163                -1  1     51520  models.common.Conv                      [320, 160, 1, 1]\n164                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']\n165                45  1     51520  models.common.Conv                      [320, 160, 1, 1]\n166          [-1, -2]  1         0  models.common.Concat                    [1]\n167                -1  1     41216  models.common.Conv                      [320, 128, 1, 1]\n168                -2  1     41216  models.common.Conv                      [320, 128, 1, 1]\n169                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]\n170                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n171                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n172                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n173                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n174                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n175[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n176                -1  1    102720  models.common.Conv                      [640, 160, 1, 1]\n177               -11  1     41216  models.common.Conv                      [320, 128, 1, 1]\n178               -12  1     41216  models.common.Conv                      [320, 128, 1, 1]\n179                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]\n180                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n181                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n182                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n183                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n184                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]\n185[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n186                -1  1    102720  models.common.Conv                      [640, 160, 1, 1]\n187         [-1, -11]  1         0  models.common.Shortcut                  [1]\n188                -1  1    282560  models.common.DownC                     [160, 320, 1]\n189         [-1, 162]  1         0  models.common.Concat                    [1]\n190                -1  1    164352  models.common.Conv                      [640, 256, 1, 1]\n191                -2  1    164352  models.common.Conv                      [640, 256, 1, 1]\n192                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]\n193                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n194                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n195                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n196                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n197                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n198[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n199                -1  1    410240  models.common.Conv                      [1280, 320, 1, 1]\n200               -11  1    164352  models.common.Conv                      [640, 256, 1, 1]\n201               -12  1    164352  models.common.Conv                      [640, 256, 1, 1]\n202                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]\n203                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n204                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n205                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n206                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n207                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]\n208[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n209                -1  1    410240  models.common.Conv                      [1280, 320, 1, 1]\n210         [-1, -11]  1         0  models.common.Shortcut                  [1]\n211                -1  1    872000  models.common.DownC                     [320, 480, 1]\n212         [-1, 137]  1         0  models.common.Concat                    [1]\n213                -1  1    369408  models.common.Conv                      [960, 384, 1, 1]\n214                -2  1    369408  models.common.Conv                      [960, 384, 1, 1]\n215                -1  1    663936  models.common.Conv                      [384, 192, 3, 1]\n216                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n217                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n218                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n219                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n220                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n221[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n222                -1  1    922560  models.common.Conv                      [1920, 480, 1, 1]\n223               -11  1    369408  models.common.Conv                      [960, 384, 1, 1]\n224               -12  1    369408  models.common.Conv                      [960, 384, 1, 1]\n225                -1  1    663936  models.common.Conv                      [384, 192, 3, 1]\n226                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n227                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n228                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n229                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n230                -1  1    332160  models.common.Conv                      [192, 192, 3, 1]\n231[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n232                -1  1    922560  models.common.Conv                      [1920, 480, 1, 1]\n233         [-1, -11]  1         0  models.common.Shortcut                  [1]\n234                -1  1   1768640  models.common.DownC                     [480, 640, 1]\n235         [-1, 112]  1         0  models.common.Concat                    [1]\n236                -1  1    656384  models.common.Conv                      [1280, 512, 1, 1]\n237                -2  1    656384  models.common.Conv                      [1280, 512, 1, 1]\n238                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]\n239                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n240                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n241                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n242                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n243                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n244[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n245                -1  1   1639680  models.common.Conv                      [2560, 640, 1, 1]\n246               -11  1    656384  models.common.Conv                      [1280, 512, 1, 1]\n247               -12  1    656384  models.common.Conv                      [1280, 512, 1, 1]\n248                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]\n249                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n250                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n251                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n252                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n253                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]\n254[-1, -2, -3, -4, -5, -6, -7, -8]  1         0  models.common.Concat                    [1]\n255                -1  1   1639680  models.common.Conv                      [2560, 640, 1, 1]\n256         [-1, -11]  1         0  models.common.Shortcut                  [1]\n257               187  1    461440  models.common.Conv                      [160, 320, 3, 1]\n258               210  1   1844480  models.common.Conv                      [320, 640, 3, 1]\n259               233  1   4149120  models.common.Conv                      [480, 960, 3, 1]\n260               256  1   7375360  models.common.Conv                      [640, 1280, 3, 1]\n261[257, 258, 259, 260]  1    817020  models.yolo.Detect                      [80, [[19, 27, 44, 40, 38, 94], [96, 68, 86, 152, 180, 137], [140, 301, 303, 264, 238, 542], [436, 615, 739, 380, 925, 792]], [320, 640, 960, 1280]]\nC:PATH\\TO\\YOUR\\anaconda3\\lib\\site-packages\\torch\\functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ..\\aten\\src\\ATen\\native\\TensorShape.cpp:2157.)\n  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\nModel Summary: 1032 layers, 151757244 parameters, 151757244 gradients, 211.6 GFLOPS\n\nAdding autoShape... \nYOLOR  2022-8-24 torch 1.10.2 CUDA:0 (NVIDIA GeForce RTX 3070 Ti, 8191.375MB)\n\n * Debug mode: off\nWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\n * Running on http://YOUR_IP_ADDRESS:5000\nPress CTRL+C to quit\n```\n\nThen, simply control + click on the address will bring you to the flask app.\n\nThe original yolov7 pretrained weights was trained on MS COCO dataset, so it could recognize a dog:\n\n![](./figures/Flask_webapp_view2.png)\n\nHave fun using this framework!\n\n## Todos:\n\n- [x] Basic Functionalities and CSS layout\n- [x] Model Indicator \u0026 Automatically search for model weights\n- [ ] Support for video\n- [ ] Support for webcam \n\n(If there are requests to add these 2 features please let me know. I will consider adding it)\n\n## Acknowledgment:\n\nThis framework was rewritten from this repo:\n\nhttps://github.com/robmarkcole/yolov5-flask\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMichael-OvO%2FYolov7-Flask","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FMichael-OvO%2FYolov7-Flask","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMichael-OvO%2FYolov7-Flask/lists"}