https://github.com/Michael-OvO/Yolov7-Flask
A Beautiful Flask Web API for Yolov7 (and custom) models
https://github.com/Michael-OvO/Yolov7-Flask
flask flask-web inference-api model-deployment object-detection pretrained-weights python pytorch torchhub yolov7
Last synced: 28 days ago
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A Beautiful Flask Web API for Yolov7 (and custom) models
- Host: GitHub
- URL: https://github.com/Michael-OvO/Yolov7-Flask
- Owner: Michael-OvO
- License: gpl-3.0
- Created: 2022-08-24T03:04:55.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-09-20T08:20:14.000Z (almost 4 years ago)
- Last Synced: 2026-04-19T21:27:50.910Z (3 months ago)
- Topics: flask, flask-web, inference-api, model-deployment, object-detection, pretrained-weights, python, pytorch, torchhub, yolov7
- Language: Python
- Homepage:
- Size: 1.59 MB
- Stars: 201
- Watchers: 1
- Forks: 11
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
Yolov7 Flask
A Beautiful Flask Framework for Implementing the Latest Yolov7 Model
I 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:
https://github.com/Michael-OvO/Burn-Detection-Classification
## Installation & Usage:
To run this, please make sure you follow the following steps:
a trained Yolov7 model (or you can also use the official pretrained yolov7 models), they can be downloaded [here](https://github.com/WongKinYiu/yolov7/releases).
Once 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.
Make sure you have met the following requirements:
\- PyTorch >= 1.6
\- flask
\- 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)
then, to launch the app, run the following command:
```bash
$ FLASK_ENV=development FLASK_APP=app.py flask run
```
then, visit http://localhost:5000/ in your browser.
choose some pictures that the model has been trained on and test it out!
## Demonstration:
I will be using ```yolov7-e6e.pt``` for this demo and I am currently working with a RTX 3070Ti.
My directory setup is like this:
Then running the ```app.py``` yields the following output:
(if it is first time running, it may take a while to download the original repo
```bash
Using cache found in C:[PATH/To/Your/Cache].cache\torch\hub\WongKinYiu_yolov7_main
from n params module arguments
0 -1 1 0 models.common.ReOrg []
1 -1 1 8800 models.common.Conv [12, 80, 3, 1]
2 -1 1 70880 models.common.DownC [80, 160, 1]
3 -1 1 10368 models.common.Conv [160, 64, 1, 1]
4 -2 1 10368 models.common.Conv [160, 64, 1, 1]
5 -1 1 36992 models.common.Conv [64, 64, 3, 1]
6 -1 1 36992 models.common.Conv [64, 64, 3, 1]
7 -1 1 36992 models.common.Conv [64, 64, 3, 1]
8 -1 1 36992 models.common.Conv [64, 64, 3, 1]
9 -1 1 36992 models.common.Conv [64, 64, 3, 1]
10 -1 1 36992 models.common.Conv [64, 64, 3, 1]
11[-1, -3, -5, -7, -8] 1 0 models.common.Concat [1]
12 -1 1 51520 models.common.Conv [320, 160, 1, 1]
13 -11 1 10368 models.common.Conv [160, 64, 1, 1]
14 -12 1 10368 models.common.Conv [160, 64, 1, 1]
15 -1 1 36992 models.common.Conv [64, 64, 3, 1]
16 -1 1 36992 models.common.Conv [64, 64, 3, 1]
17 -1 1 36992 models.common.Conv [64, 64, 3, 1]
18 -1 1 36992 models.common.Conv [64, 64, 3, 1]
19 -1 1 36992 models.common.Conv [64, 64, 3, 1]
20 -1 1 36992 models.common.Conv [64, 64, 3, 1]
21[-1, -3, -5, -7, -8] 1 0 models.common.Concat [1]
22 -1 1 51520 models.common.Conv [320, 160, 1, 1]
23 [-1, -11] 1 0 models.common.Shortcut [1]
24 -1 1 282560 models.common.DownC [160, 320, 1]
25 -1 1 41216 models.common.Conv [320, 128, 1, 1]
26 -2 1 41216 models.common.Conv [320, 128, 1, 1]
27 -1 1 147712 models.common.Conv [128, 128, 3, 1]
28 -1 1 147712 models.common.Conv [128, 128, 3, 1]
29 -1 1 147712 models.common.Conv [128, 128, 3, 1]
30 -1 1 147712 models.common.Conv [128, 128, 3, 1]
31 -1 1 147712 models.common.Conv [128, 128, 3, 1]
32 -1 1 147712 models.common.Conv [128, 128, 3, 1]
33[-1, -3, -5, -7, -8] 1 0 models.common.Concat [1]
34 -1 1 205440 models.common.Conv [640, 320, 1, 1]
35 -11 1 41216 models.common.Conv [320, 128, 1, 1]
36 -12 1 41216 models.common.Conv [320, 128, 1, 1]
37 -1 1 147712 models.common.Conv [128, 128, 3, 1]
38 -1 1 147712 models.common.Conv [128, 128, 3, 1]
39 -1 1 147712 models.common.Conv [128, 128, 3, 1]
40 -1 1 147712 models.common.Conv [128, 128, 3, 1]
41 -1 1 147712 models.common.Conv [128, 128, 3, 1]
42 -1 1 147712 models.common.Conv [128, 128, 3, 1]
43[-1, -3, -5, -7, -8] 1 0 models.common.Concat [1]
44 -1 1 205440 models.common.Conv [640, 320, 1, 1]
45 [-1, -11] 1 0 models.common.Shortcut [1]
46 -1 1 1128320 models.common.DownC [320, 640, 1]
47 -1 1 164352 models.common.Conv [640, 256, 1, 1]
48 -2 1 164352 models.common.Conv [640, 256, 1, 1]
49 -1 1 590336 models.common.Conv [256, 256, 3, 1]
50 -1 1 590336 models.common.Conv [256, 256, 3, 1]
51 -1 1 590336 models.common.Conv [256, 256, 3, 1]
52 -1 1 590336 models.common.Conv [256, 256, 3, 1]
53 -1 1 590336 models.common.Conv [256, 256, 3, 1]
54 -1 1 590336 models.common.Conv [256, 256, 3, 1]
55[-1, -3, -5, -7, -8] 1 0 models.common.Concat [1]
56 -1 1 820480 models.common.Conv [1280, 640, 1, 1]
57 -11 1 164352 models.common.Conv [640, 256, 1, 1]
58 -12 1 164352 models.common.Conv [640, 256, 1, 1]
59 -1 1 590336 models.common.Conv [256, 256, 3, 1]
60 -1 1 590336 models.common.Conv [256, 256, 3, 1]
61 -1 1 590336 models.common.Conv [256, 256, 3, 1]
62 -1 1 590336 models.common.Conv [256, 256, 3, 1]
63 -1 1 590336 models.common.Conv [256, 256, 3, 1]
64 -1 1 590336 models.common.Conv [256, 256, 3, 1]
65[-1, -3, -5, -7, -8] 1 0 models.common.Concat [1]
66 -1 1 820480 models.common.Conv [1280, 640, 1, 1]
67 [-1, -11] 1 0 models.common.Shortcut [1]
68 -1 1 3484800 models.common.DownC [640, 960, 1]
69 -1 1 369408 models.common.Conv [960, 384, 1, 1]
70 -2 1 369408 models.common.Conv [960, 384, 1, 1]
71 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
72 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
73 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
74 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
75 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
76 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
77[-1, -3, -5, -7, -8] 1 0 models.common.Concat [1]
78 -1 1 1845120 models.common.Conv [1920, 960, 1, 1]
79 -11 1 369408 models.common.Conv [960, 384, 1, 1]
80 -12 1 369408 models.common.Conv [960, 384, 1, 1]
81 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
82 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
83 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
84 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
85 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
86 -1 1 1327872 models.common.Conv [384, 384, 3, 1]
87[-1, -3, -5, -7, -8] 1 0 models.common.Concat [1]
88 -1 1 1845120 models.common.Conv [1920, 960, 1, 1]
89 [-1, -11] 1 0 models.common.Shortcut [1]
90 -1 1 7070080 models.common.DownC [960, 1280, 1]
91 -1 1 656384 models.common.Conv [1280, 512, 1, 1]
92 -2 1 656384 models.common.Conv [1280, 512, 1, 1]
93 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
94 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
95 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
96 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
97 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
98 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
99[-1, -3, -5, -7, -8] 1 0 models.common.Concat [1]
100 -1 1 3279360 models.common.Conv [2560, 1280, 1, 1]
101 -11 1 656384 models.common.Conv [1280, 512, 1, 1]
102 -12 1 656384 models.common.Conv [1280, 512, 1, 1]
103 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
104 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
105 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
106 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
107 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
108 -1 1 2360320 models.common.Conv [512, 512, 3, 1]
109[-1, -3, -5, -7, -8] 1 0 models.common.Concat [1]
110 -1 1 3279360 models.common.Conv [2560, 1280, 1, 1]
111 [-1, -11] 1 0 models.common.Shortcut [1]
112 -1 1 11887360 models.common.SPPCSPC [1280, 640, 1]
113 -1 1 308160 models.common.Conv [640, 480, 1, 1]
114 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
115 89 1 461760 models.common.Conv [960, 480, 1, 1]
116 [-1, -2] 1 0 models.common.Concat [1]
117 -1 1 369408 models.common.Conv [960, 384, 1, 1]
118 -2 1 369408 models.common.Conv [960, 384, 1, 1]
119 -1 1 663936 models.common.Conv [384, 192, 3, 1]
120 -1 1 332160 models.common.Conv [192, 192, 3, 1]
121 -1 1 332160 models.common.Conv [192, 192, 3, 1]
122 -1 1 332160 models.common.Conv [192, 192, 3, 1]
123 -1 1 332160 models.common.Conv [192, 192, 3, 1]
124 -1 1 332160 models.common.Conv [192, 192, 3, 1]
125[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
126 -1 1 922560 models.common.Conv [1920, 480, 1, 1]
127 -11 1 369408 models.common.Conv [960, 384, 1, 1]
128 -12 1 369408 models.common.Conv [960, 384, 1, 1]
129 -1 1 663936 models.common.Conv [384, 192, 3, 1]
130 -1 1 332160 models.common.Conv [192, 192, 3, 1]
131 -1 1 332160 models.common.Conv [192, 192, 3, 1]
132 -1 1 332160 models.common.Conv [192, 192, 3, 1]
133 -1 1 332160 models.common.Conv [192, 192, 3, 1]
134 -1 1 332160 models.common.Conv [192, 192, 3, 1]
135[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
136 -1 1 922560 models.common.Conv [1920, 480, 1, 1]
137 [-1, -11] 1 0 models.common.Shortcut [1]
138 -1 1 154240 models.common.Conv [480, 320, 1, 1]
139 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
140 67 1 205440 models.common.Conv [640, 320, 1, 1]
141 [-1, -2] 1 0 models.common.Concat [1]
142 -1 1 164352 models.common.Conv [640, 256, 1, 1]
143 -2 1 164352 models.common.Conv [640, 256, 1, 1]
144 -1 1 295168 models.common.Conv [256, 128, 3, 1]
145 -1 1 147712 models.common.Conv [128, 128, 3, 1]
146 -1 1 147712 models.common.Conv [128, 128, 3, 1]
147 -1 1 147712 models.common.Conv [128, 128, 3, 1]
148 -1 1 147712 models.common.Conv [128, 128, 3, 1]
149 -1 1 147712 models.common.Conv [128, 128, 3, 1]
150[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
151 -1 1 410240 models.common.Conv [1280, 320, 1, 1]
152 -11 1 164352 models.common.Conv [640, 256, 1, 1]
153 -12 1 164352 models.common.Conv [640, 256, 1, 1]
154 -1 1 295168 models.common.Conv [256, 128, 3, 1]
155 -1 1 147712 models.common.Conv [128, 128, 3, 1]
156 -1 1 147712 models.common.Conv [128, 128, 3, 1]
157 -1 1 147712 models.common.Conv [128, 128, 3, 1]
158 -1 1 147712 models.common.Conv [128, 128, 3, 1]
159 -1 1 147712 models.common.Conv [128, 128, 3, 1]
160[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
161 -1 1 410240 models.common.Conv [1280, 320, 1, 1]
162 [-1, -11] 1 0 models.common.Shortcut [1]
163 -1 1 51520 models.common.Conv [320, 160, 1, 1]
164 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
165 45 1 51520 models.common.Conv [320, 160, 1, 1]
166 [-1, -2] 1 0 models.common.Concat [1]
167 -1 1 41216 models.common.Conv [320, 128, 1, 1]
168 -2 1 41216 models.common.Conv [320, 128, 1, 1]
169 -1 1 73856 models.common.Conv [128, 64, 3, 1]
170 -1 1 36992 models.common.Conv [64, 64, 3, 1]
171 -1 1 36992 models.common.Conv [64, 64, 3, 1]
172 -1 1 36992 models.common.Conv [64, 64, 3, 1]
173 -1 1 36992 models.common.Conv [64, 64, 3, 1]
174 -1 1 36992 models.common.Conv [64, 64, 3, 1]
175[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
176 -1 1 102720 models.common.Conv [640, 160, 1, 1]
177 -11 1 41216 models.common.Conv [320, 128, 1, 1]
178 -12 1 41216 models.common.Conv [320, 128, 1, 1]
179 -1 1 73856 models.common.Conv [128, 64, 3, 1]
180 -1 1 36992 models.common.Conv [64, 64, 3, 1]
181 -1 1 36992 models.common.Conv [64, 64, 3, 1]
182 -1 1 36992 models.common.Conv [64, 64, 3, 1]
183 -1 1 36992 models.common.Conv [64, 64, 3, 1]
184 -1 1 36992 models.common.Conv [64, 64, 3, 1]
185[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
186 -1 1 102720 models.common.Conv [640, 160, 1, 1]
187 [-1, -11] 1 0 models.common.Shortcut [1]
188 -1 1 282560 models.common.DownC [160, 320, 1]
189 [-1, 162] 1 0 models.common.Concat [1]
190 -1 1 164352 models.common.Conv [640, 256, 1, 1]
191 -2 1 164352 models.common.Conv [640, 256, 1, 1]
192 -1 1 295168 models.common.Conv [256, 128, 3, 1]
193 -1 1 147712 models.common.Conv [128, 128, 3, 1]
194 -1 1 147712 models.common.Conv [128, 128, 3, 1]
195 -1 1 147712 models.common.Conv [128, 128, 3, 1]
196 -1 1 147712 models.common.Conv [128, 128, 3, 1]
197 -1 1 147712 models.common.Conv [128, 128, 3, 1]
198[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
199 -1 1 410240 models.common.Conv [1280, 320, 1, 1]
200 -11 1 164352 models.common.Conv [640, 256, 1, 1]
201 -12 1 164352 models.common.Conv [640, 256, 1, 1]
202 -1 1 295168 models.common.Conv [256, 128, 3, 1]
203 -1 1 147712 models.common.Conv [128, 128, 3, 1]
204 -1 1 147712 models.common.Conv [128, 128, 3, 1]
205 -1 1 147712 models.common.Conv [128, 128, 3, 1]
206 -1 1 147712 models.common.Conv [128, 128, 3, 1]
207 -1 1 147712 models.common.Conv [128, 128, 3, 1]
208[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
209 -1 1 410240 models.common.Conv [1280, 320, 1, 1]
210 [-1, -11] 1 0 models.common.Shortcut [1]
211 -1 1 872000 models.common.DownC [320, 480, 1]
212 [-1, 137] 1 0 models.common.Concat [1]
213 -1 1 369408 models.common.Conv [960, 384, 1, 1]
214 -2 1 369408 models.common.Conv [960, 384, 1, 1]
215 -1 1 663936 models.common.Conv [384, 192, 3, 1]
216 -1 1 332160 models.common.Conv [192, 192, 3, 1]
217 -1 1 332160 models.common.Conv [192, 192, 3, 1]
218 -1 1 332160 models.common.Conv [192, 192, 3, 1]
219 -1 1 332160 models.common.Conv [192, 192, 3, 1]
220 -1 1 332160 models.common.Conv [192, 192, 3, 1]
221[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
222 -1 1 922560 models.common.Conv [1920, 480, 1, 1]
223 -11 1 369408 models.common.Conv [960, 384, 1, 1]
224 -12 1 369408 models.common.Conv [960, 384, 1, 1]
225 -1 1 663936 models.common.Conv [384, 192, 3, 1]
226 -1 1 332160 models.common.Conv [192, 192, 3, 1]
227 -1 1 332160 models.common.Conv [192, 192, 3, 1]
228 -1 1 332160 models.common.Conv [192, 192, 3, 1]
229 -1 1 332160 models.common.Conv [192, 192, 3, 1]
230 -1 1 332160 models.common.Conv [192, 192, 3, 1]
231[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
232 -1 1 922560 models.common.Conv [1920, 480, 1, 1]
233 [-1, -11] 1 0 models.common.Shortcut [1]
234 -1 1 1768640 models.common.DownC [480, 640, 1]
235 [-1, 112] 1 0 models.common.Concat [1]
236 -1 1 656384 models.common.Conv [1280, 512, 1, 1]
237 -2 1 656384 models.common.Conv [1280, 512, 1, 1]
238 -1 1 1180160 models.common.Conv [512, 256, 3, 1]
239 -1 1 590336 models.common.Conv [256, 256, 3, 1]
240 -1 1 590336 models.common.Conv [256, 256, 3, 1]
241 -1 1 590336 models.common.Conv [256, 256, 3, 1]
242 -1 1 590336 models.common.Conv [256, 256, 3, 1]
243 -1 1 590336 models.common.Conv [256, 256, 3, 1]
244[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
245 -1 1 1639680 models.common.Conv [2560, 640, 1, 1]
246 -11 1 656384 models.common.Conv [1280, 512, 1, 1]
247 -12 1 656384 models.common.Conv [1280, 512, 1, 1]
248 -1 1 1180160 models.common.Conv [512, 256, 3, 1]
249 -1 1 590336 models.common.Conv [256, 256, 3, 1]
250 -1 1 590336 models.common.Conv [256, 256, 3, 1]
251 -1 1 590336 models.common.Conv [256, 256, 3, 1]
252 -1 1 590336 models.common.Conv [256, 256, 3, 1]
253 -1 1 590336 models.common.Conv [256, 256, 3, 1]
254[-1, -2, -3, -4, -5, -6, -7, -8] 1 0 models.common.Concat [1]
255 -1 1 1639680 models.common.Conv [2560, 640, 1, 1]
256 [-1, -11] 1 0 models.common.Shortcut [1]
257 187 1 461440 models.common.Conv [160, 320, 3, 1]
258 210 1 1844480 models.common.Conv [320, 640, 3, 1]
259 233 1 4149120 models.common.Conv [480, 960, 3, 1]
260 256 1 7375360 models.common.Conv [640, 1280, 3, 1]
261[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]]
C: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.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Model Summary: 1032 layers, 151757244 parameters, 151757244 gradients, 211.6 GFLOPS
Adding autoShape...
YOLOR 2022-8-24 torch 1.10.2 CUDA:0 (NVIDIA GeForce RTX 3070 Ti, 8191.375MB)
* Debug mode: off
WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
* Running on http://YOUR_IP_ADDRESS:5000
Press CTRL+C to quit
```
Then, simply control + click on the address will bring you to the flask app.
The original yolov7 pretrained weights was trained on MS COCO dataset, so it could recognize a dog:
Have fun using this framework!
## Todos:
- [x] Basic Functionalities and CSS layout
- [x] Model Indicator & Automatically search for model weights
- [ ] Support for video
- [ ] Support for webcam
(If there are requests to add these 2 features please let me know. I will consider adding it)
## Acknowledgment:
This framework was rewritten from this repo:
https://github.com/robmarkcole/yolov5-flask