Yolov7 Flask Save
A Beautiful Flask Web API for Yolov7 (and custom) models
Project README
Yolov7 Flask
A Beautiful Flask Framework for Implementing the Latest Yolov7 Model
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.
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:
$ 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
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:
- Basic Functionalities and CSS layout
- 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:
Open Source Agenda is not affiliated with "Yolov7 Flask" Project. README Source: Michael-OvO/Yolov7-Flask
Stars
205
Open Issues
3
Last Commit
1 year ago
Repository
License
Open Source Agenda Badge
