A newer version of the Gradio SDK is available:
6.5.1
metadata
title: ERA SESSION13
emoji: 🔥
colorFrom: indigo
colorTo: indigo
sdk: gradio
sdk_version: 3.40.1
app_file: app.py
pinned: false
license: mit
ERA-SESSION13 YoloV3 with Pytorch Lightning & Gradio
HF Link: https://huggingface.co/spaces/Navyabhat/Session13
Achieved:
- Training Loss: 3.680
- Validation Loss: 4.940
- Class accuracy: 81.601883%
- No obj accuracy: 97.991463%
- Obj accuracy: 75.976616%
- MAP: 0.4366795
Tasks:
- :heavy_check_mark: Move the code to PytorchLightning
- :heavy_check_mark: Train the model to reach such that all of these are true:
- Class accuracy is more than 75%
- No Obj accuracy of more than 95%
- Object Accuracy of more than 70% (assuming you had to reduce the kernel numbers, else 80/98/78)
- Ideally trained till 40 epochs
- :heavy_check_mark: Add these training features:
- Add multi-resolution training - the code shared trains only on one resolution 416
- Add Implement Mosaic Augmentation only 75% of the times
- Train on float16
- GradCam must be implemented.
- :heavy_check_mark: Things that are allowed due to HW constraints:
- Change of batch size
- Change of resolution
- Change of OCP parameters
- :heavy_check_mark: Once done:
- Move the app to HuggingFace Spaces
- Allow custom upload of images
- Share some samples from the existing dataset
- Show the GradCAM output for the image that the user uploads as well as for the samples.
- :heavy_check_mark: Mention things like:
- classes that your model support
- link to the actual model
- :heavy_check_mark: Assignment:
- Share HuggingFace App Link
- Share LightningCode Link on Github
- Share notebook link (with logs) on GitHub
Results
Gradio App
Model Summary
| Name | Type | Params
-------------------------------------------------------------------
0 | loss_fn | YoloLoss | 0
1 | loss_fn.mse | MSELoss | 0
2 | loss_fn.bce | BCEWithLogitsLoss | 0
3 | loss_fn.entropy | CrossEntropyLoss | 0
4 | loss_fn.sigmoid | Sigmoid | 0
5 | layers | ModuleList | 61.6 M
6 | layers.0 | CNNBlock | 928
7 | layers.0.conv | Conv2d | 864
8 | layers.0.bn | BatchNorm2d | 64
9 | layers.0.leaky | LeakyReLU | 0
10 | layers.1 | CNNBlock | 18.6 K
11 | layers.1.conv | Conv2d | 18.4 K
12 | layers.1.bn | BatchNorm2d | 128
13 | layers.1.leaky | LeakyReLU | 0
14 | layers.2 | ResidualBlock | 20.7 K
15 | layers.2.layers | ModuleList | 20.7 K
16 | layers.2.layers.0 | Sequential | 20.7 K
17 | layers.2.layers.0.0 | CNNBlock | 2.1 K
18 | layers.2.layers.0.0.conv | Conv2d | 2.0 K
19 | layers.2.layers.0.0.bn | BatchNorm2d | 64
20 | layers.2.layers.0.0.leaky | LeakyReLU | 0
21 | layers.2.layers.0.1 | CNNBlock | 18.6 K
22 | layers.2.layers.0.1.conv | Conv2d | 18.4 K
23 | layers.2.layers.0.1.bn | BatchNorm2d | 128
24 | layers.2.layers.0.1.leaky | LeakyReLU | 0
25 | layers.3 | CNNBlock | 74.0 K
26 | layers.3.conv | Conv2d | 73.7 K
27 | layers.3.bn | BatchNorm2d | 256
28 | layers.3.leaky | LeakyReLU | 0
29 | layers.4 | ResidualBlock | 164 K
30 | layers.4.layers | ModuleList | 164 K
31 | layers.4.layers.0 | Sequential | 82.3 K
32 | layers.4.layers.0.0 | CNNBlock | 8.3 K
33 | layers.4.layers.0.0.conv | Conv2d | 8.2 K
34 | layers.4.layers.0.0.bn | BatchNorm2d | 128
35 | layers.4.layers.0.0.leaky | LeakyReLU | 0
36 | layers.4.layers.0.1 | CNNBlock | 74.0 K
37 | layers.4.layers.0.1.conv | Conv2d | 73.7 K
38 | layers.4.layers.0.1.bn | BatchNorm2d | 256
39 | layers.4.layers.0.1.leaky | LeakyReLU | 0
40 | layers.4.layers.1 | Sequential | 82.3 K
41 | layers.4.layers.1.0 | CNNBlock | 8.3 K
42 | layers.4.layers.1.0.conv | Conv2d | 8.2 K
43 | layers.4.layers.1.0.bn | BatchNorm2d | 128
44 | layers.4.layers.1.0.leaky | LeakyReLU | 0
45 | layers.4.layers.1.1 | CNNBlock | 74.0 K
46 | layers.4.layers.1.1.conv | Conv2d | 73.7 K
47 | layers.4.layers.1.1.bn | BatchNorm2d | 256
48 | layers.4.layers.1.1.leaky | LeakyReLU | 0
49 | layers.5 | CNNBlock | 295 K
50 | layers.5.conv | Conv2d | 294 K
51 | layers.5.bn | BatchNorm2d | 512
52 | layers.5.leaky | LeakyReLU | 0
53 | layers.6 | ResidualBlock | 2.6 M
54 | layers.6.layers | ModuleList | 2.6 M
55 | layers.6.layers.0 | Sequential | 328 K
56 | layers.6.layers.0.0 | CNNBlock | 33.0 K
57 | layers.6.layers.0.0.conv | Conv2d | 32.8 K
58 | layers.6.layers.0.0.bn | BatchNorm2d | 256
59 | layers.6.layers.0.0.leaky | LeakyReLU | 0
60 | layers.6.layers.0.1 | CNNBlock | 295 K
61 | layers.6.layers.0.1.conv | Conv2d | 294 K
62 | layers.6.layers.0.1.bn | BatchNorm2d | 512
63 | layers.6.layers.0.1.leaky | LeakyReLU | 0
64 | layers.6.layers.1 | Sequential | 328 K
65 | layers.6.layers.1.0 | CNNBlock | 33.0 K
66 | layers.6.layers.1.0.conv | Conv2d | 32.8 K
67 | layers.6.layers.1.0.bn | BatchNorm2d | 256
68 | layers.6.layers.1.0.leaky | LeakyReLU | 0
69 | layers.6.layers.1.1 | CNNBlock | 295 K
70 | layers.6.layers.1.1.conv | Conv2d | 294 K
71 | layers.6.layers.1.1.bn | BatchNorm2d | 512
72 | layers.6.layers.1.1.leaky | LeakyReLU | 0
73 | layers.6.layers.2 | Sequential | 328 K
74 | layers.6.layers.2.0 | CNNBlock | 33.0 K
75 | layers.6.layers.2.0.conv | Conv2d | 32.8 K
76 | layers.6.layers.2.0.bn | BatchNorm2d | 256
77 | layers.6.layers.2.0.leaky | LeakyReLU | 0
78 | layers.6.layers.2.1 | CNNBlock | 295 K
79 | layers.6.layers.2.1.conv | Conv2d | 294 K
80 | layers.6.layers.2.1.bn | BatchNorm2d | 512
81 | layers.6.layers.2.1.leaky | LeakyReLU | 0
82 | layers.6.layers.3 | Sequential | 328 K
83 | layers.6.layers.3.0 | CNNBlock | 33.0 K
84 | layers.6.layers.3.0.conv | Conv2d | 32.8 K
85 | layers.6.layers.3.0.bn | BatchNorm2d | 256
86 | layers.6.layers.3.0.leaky | LeakyReLU | 0
87 | layers.6.layers.3.1 | CNNBlock | 295 K
88 | layers.6.layers.3.1.conv | Conv2d | 294 K
89 | layers.6.layers.3.1.bn | BatchNorm2d | 512
90 | layers.6.layers.3.1.leaky | LeakyReLU | 0
91 | layers.6.layers.4 | Sequential | 328 K
92 | layers.6.layers.4.0 | CNNBlock | 33.0 K
93 | layers.6.layers.4.0.conv | Conv2d | 32.8 K
94 | layers.6.layers.4.0.bn | BatchNorm2d | 256
95 | layers.6.layers.4.0.leaky | LeakyReLU | 0
96 | layers.6.layers.4.1 | CNNBlock | 295 K
97 | layers.6.layers.4.1.conv | Conv2d | 294 K
98 | layers.6.layers.4.1.bn | BatchNorm2d | 512
99 | layers.6.layers.4.1.leaky | LeakyReLU | 0
100 | layers.6.layers.5 | Sequential | 328 K
101 | layers.6.layers.5.0 | CNNBlock | 33.0 K
102 | layers.6.layers.5.0.conv | Conv2d | 32.8 K
103 | layers.6.layers.5.0.bn | BatchNorm2d | 256
104 | layers.6.layers.5.0.leaky | LeakyReLU | 0
105 | layers.6.layers.5.1 | CNNBlock | 295 K
106 | layers.6.layers.5.1.conv | Conv2d | 294 K
107 | layers.6.layers.5.1.bn | BatchNorm2d | 512
108 | layers.6.layers.5.1.leaky | LeakyReLU | 0
109 | layers.6.layers.6 | Sequential | 328 K
110 | layers.6.layers.6.0 | CNNBlock | 33.0 K
111 | layers.6.layers.6.0.conv | Conv2d | 32.8 K
112 | layers.6.layers.6.0.bn | BatchNorm2d | 256
113 | layers.6.layers.6.0.leaky | LeakyReLU | 0
114 | layers.6.layers.6.1 | CNNBlock | 295 K
115 | layers.6.layers.6.1.conv | Conv2d | 294 K
116 | layers.6.layers.6.1.bn | BatchNorm2d | 512
117 | layers.6.layers.6.1.leaky | LeakyReLU | 0
118 | layers.6.layers.7 | Sequential | 328 K
119 | layers.6.layers.7.0 | CNNBlock | 33.0 K
120 | layers.6.layers.7.0.conv | Conv2d | 32.8 K
121 | layers.6.layers.7.0.bn | BatchNorm2d | 256
122 | layers.6.layers.7.0.leaky | LeakyReLU | 0
123 | layers.6.layers.7.1 | CNNBlock | 295 K
124 | layers.6.layers.7.1.conv | Conv2d | 294 K
125 | layers.6.layers.7.1.bn | BatchNorm2d | 512
126 | layers.6.layers.7.1.leaky | LeakyReLU | 0
127 | layers.7 | CNNBlock | 1.2 M
128 | layers.7.conv | Conv2d | 1.2 M
129 | layers.7.bn | BatchNorm2d | 1.0 K
130 | layers.7.leaky | LeakyReLU | 0
131 | layers.8 | ResidualBlock | 10.5 M
132 | layers.8.layers | ModuleList | 10.5 M
133 | layers.8.layers.0 | Sequential | 1.3 M
134 | layers.8.layers.0.0 | CNNBlock | 131 K
135 | layers.8.layers.0.0.conv | Conv2d | 131 K
136 | layers.8.layers.0.0.bn | BatchNorm2d | 512
137 | layers.8.layers.0.0.leaky | LeakyReLU | 0
138 | layers.8.layers.0.1 | CNNBlock | 1.2 M
139 | layers.8.layers.0.1.conv | Conv2d | 1.2 M
140 | layers.8.layers.0.1.bn | BatchNorm2d | 1.0 K
141 | layers.8.layers.0.1.leaky | LeakyReLU | 0
142 | layers.8.layers.1 | Sequential | 1.3 M
143 | layers.8.layers.1.0 | CNNBlock | 131 K
144 | layers.8.layers.1.0.conv | Conv2d | 131 K
145 | layers.8.layers.1.0.bn | BatchNorm2d | 512
146 | layers.8.layers.1.0.leaky | LeakyReLU | 0
147 | layers.8.layers.1.1 | CNNBlock | 1.2 M
148 | layers.8.layers.1.1.conv | Conv2d | 1.2 M
149 | layers.8.layers.1.1.bn | BatchNorm2d | 1.0 K
150 | layers.8.layers.1.1.leaky | LeakyReLU | 0
151 | layers.8.layers.2 | Sequential | 1.3 M
152 | layers.8.layers.2.0 | CNNBlock | 131 K
153 | layers.8.layers.2.0.conv | Conv2d | 131 K
154 | layers.8.layers.2.0.bn | BatchNorm2d | 512
155 | layers.8.layers.2.0.leaky | LeakyReLU | 0
156 | layers.8.layers.2.1 | CNNBlock | 1.2 M
157 | layers.8.layers.2.1.conv | Conv2d | 1.2 M
158 | layers.8.layers.2.1.bn | BatchNorm2d | 1.0 K
159 | layers.8.layers.2.1.leaky | LeakyReLU | 0
160 | layers.8.layers.3 | Sequential | 1.3 M
161 | layers.8.layers.3.0 | CNNBlock | 131 K
162 | layers.8.layers.3.0.conv | Conv2d | 131 K
163 | layers.8.layers.3.0.bn | BatchNorm2d | 512
164 | layers.8.layers.3.0.leaky | LeakyReLU | 0
165 | layers.8.layers.3.1 | CNNBlock | 1.2 M
166 | layers.8.layers.3.1.conv | Conv2d | 1.2 M
167 | layers.8.layers.3.1.bn | BatchNorm2d | 1.0 K
168 | layers.8.layers.3.1.leaky | LeakyReLU | 0
169 | layers.8.layers.4 | Sequential | 1.3 M
170 | layers.8.layers.4.0 | CNNBlock | 131 K
171 | layers.8.layers.4.0.conv | Conv2d | 131 K
172 | layers.8.layers.4.0.bn | BatchNorm2d | 512
173 | layers.8.layers.4.0.leaky | LeakyReLU | 0
174 | layers.8.layers.4.1 | CNNBlock | 1.2 M
175 | layers.8.layers.4.1.conv | Conv2d | 1.2 M
176 | layers.8.layers.4.1.bn | BatchNorm2d | 1.0 K
177 | layers.8.layers.4.1.leaky | LeakyReLU | 0
178 | layers.8.layers.5 | Sequential | 1.3 M
179 | layers.8.layers.5.0 | CNNBlock | 131 K
180 | layers.8.layers.5.0.conv | Conv2d | 131 K
181 | layers.8.layers.5.0.bn | BatchNorm2d | 512
182 | layers.8.layers.5.0.leaky | LeakyReLU | 0
183 | layers.8.layers.5.1 | CNNBlock | 1.2 M
184 | layers.8.layers.5.1.conv | Conv2d | 1.2 M
185 | layers.8.layers.5.1.bn | BatchNorm2d | 1.0 K
186 | layers.8.layers.5.1.leaky | LeakyReLU | 0
187 | layers.8.layers.6 | Sequential | 1.3 M
188 | layers.8.layers.6.0 | CNNBlock | 131 K
189 | layers.8.layers.6.0.conv | Conv2d | 131 K
190 | layers.8.layers.6.0.bn | BatchNorm2d | 512
191 | layers.8.layers.6.0.leaky | LeakyReLU | 0
192 | layers.8.layers.6.1 | CNNBlock | 1.2 M
193 | layers.8.layers.6.1.conv | Conv2d | 1.2 M
194 | layers.8.layers.6.1.bn | BatchNorm2d | 1.0 K
195 | layers.8.layers.6.1.leaky | LeakyReLU | 0
196 | layers.8.layers.7 | Sequential | 1.3 M
197 | layers.8.layers.7.0 | CNNBlock | 131 K
198 | layers.8.layers.7.0.conv | Conv2d | 131 K
199 | layers.8.layers.7.0.bn | BatchNorm2d | 512
200 | layers.8.layers.7.0.leaky | LeakyReLU | 0
201 | layers.8.layers.7.1 | CNNBlock | 1.2 M
202 | layers.8.layers.7.1.conv | Conv2d | 1.2 M
203 | layers.8.layers.7.1.bn | BatchNorm2d | 1.0 K
204 | layers.8.layers.7.1.leaky | LeakyReLU | 0
205 | layers.9 | CNNBlock | 4.7 M
206 | layers.9.conv | Conv2d | 4.7 M
207 | layers.9.bn | BatchNorm2d | 2.0 K
208 | layers.9.leaky | LeakyReLU | 0
209 | layers.10 | ResidualBlock | 21.0 M
210 | layers.10.layers | ModuleList | 21.0 M
211 | layers.10.layers.0 | Sequential | 5.2 M
212 | layers.10.layers.0.0 | CNNBlock | 525 K
213 | layers.10.layers.0.0.conv | Conv2d | 524 K
214 | layers.10.layers.0.0.bn | BatchNorm2d | 1.0 K
215 | layers.10.layers.0.0.leaky | LeakyReLU | 0
216 | layers.10.layers.0.1 | CNNBlock | 4.7 M
217 | layers.10.layers.0.1.conv | Conv2d | 4.7 M
218 | layers.10.layers.0.1.bn | BatchNorm2d | 2.0 K
219 | layers.10.layers.0.1.leaky | LeakyReLU | 0
220 | layers.10.layers.1 | Sequential | 5.2 M
221 | layers.10.layers.1.0 | CNNBlock | 525 K
222 | layers.10.layers.1.0.conv | Conv2d | 524 K
223 | layers.10.layers.1.0.bn | BatchNorm2d | 1.0 K
224 | layers.10.layers.1.0.leaky | LeakyReLU | 0
225 | layers.10.layers.1.1 | CNNBlock | 4.7 M
226 | layers.10.layers.1.1.conv | Conv2d | 4.7 M
227 | layers.10.layers.1.1.bn | BatchNorm2d | 2.0 K
228 | layers.10.layers.1.1.leaky | LeakyReLU | 0
229 | layers.10.layers.2 | Sequential | 5.2 M
230 | layers.10.layers.2.0 | CNNBlock | 525 K
231 | layers.10.layers.2.0.conv | Conv2d | 524 K
232 | layers.10.layers.2.0.bn | BatchNorm2d | 1.0 K
233 | layers.10.layers.2.0.leaky | LeakyReLU | 0
234 | layers.10.layers.2.1 | CNNBlock | 4.7 M
235 | layers.10.layers.2.1.conv | Conv2d | 4.7 M
236 | layers.10.layers.2.1.bn | BatchNorm2d | 2.0 K
237 | layers.10.layers.2.1.leaky | LeakyReLU | 0
238 | layers.10.layers.3 | Sequential | 5.2 M
239 | layers.10.layers.3.0 | CNNBlock | 525 K
240 | layers.10.layers.3.0.conv | Conv2d | 524 K
241 | layers.10.layers.3.0.bn | BatchNorm2d | 1.0 K
242 | layers.10.layers.3.0.leaky | LeakyReLU | 0
243 | layers.10.layers.3.1 | CNNBlock | 4.7 M
244 | layers.10.layers.3.1.conv | Conv2d | 4.7 M
245 | layers.10.layers.3.1.bn | BatchNorm2d | 2.0 K
246 | layers.10.layers.3.1.leaky | LeakyReLU | 0
247 | layers.11 | CNNBlock | 525 K
248 | layers.11.conv | Conv2d | 524 K
249 | layers.11.bn | BatchNorm2d | 1.0 K
250 | layers.11.leaky | LeakyReLU | 0
251 | layers.12 | CNNBlock | 4.7 M
252 | layers.12.conv | Conv2d | 4.7 M
253 | layers.12.bn | BatchNorm2d | 2.0 K
254 | layers.12.leaky | LeakyReLU | 0
255 | layers.13 | ResidualBlock | 5.2 M
256 | layers.13.layers | ModuleList | 5.2 M
257 | layers.13.layers.0 | Sequential | 5.2 M
258 | layers.13.layers.0.0 | CNNBlock | 525 K
259 | layers.13.layers.0.0.conv | Conv2d | 524 K
260 | layers.13.layers.0.0.bn | BatchNorm2d | 1.0 K
261 | layers.13.layers.0.0.leaky | LeakyReLU | 0
262 | layers.13.layers.0.1 | CNNBlock | 4.7 M
263 | layers.13.layers.0.1.conv | Conv2d | 4.7 M
264 | layers.13.layers.0.1.bn | BatchNorm2d | 2.0 K
265 | layers.13.layers.0.1.leaky | LeakyReLU | 0
266 | layers.14 | CNNBlock | 525 K
267 | layers.14.conv | Conv2d | 524 K
268 | layers.14.bn | BatchNorm2d | 1.0 K
269 | layers.14.leaky | LeakyReLU | 0
270 | layers.15 | ScalePrediction | 4.8 M
271 | layers.15.pred | Sequential | 4.8 M
272 | layers.15.pred.0 | CNNBlock | 4.7 M
273 | layers.15.pred.0.conv | Conv2d | 4.7 M
274 | layers.15.pred.0.bn | BatchNorm2d | 2.0 K
275 | layers.15.pred.0.leaky | LeakyReLU | 0
276 | layers.15.pred.1 | CNNBlock | 77.0 K
277 | layers.15.pred.1.conv | Conv2d | 76.9 K
278 | layers.15.pred.1.bn | BatchNorm2d | 150
279 | layers.15.pred.1.leaky | LeakyReLU | 0
280 | layers.16 | CNNBlock | 131 K
281 | layers.16.conv | Conv2d | 131 K
282 | layers.16.bn | BatchNorm2d | 512
283 | layers.16.leaky | LeakyReLU | 0
284 | layers.17 | Upsample | 0
285 | layers.18 | CNNBlock | 197 K
286 | layers.18.conv | Conv2d | 196 K
287 | layers.18.bn | BatchNorm2d | 512
288 | layers.18.leaky | LeakyReLU | 0
289 | layers.19 | CNNBlock | 1.2 M
290 | layers.19.conv | Conv2d | 1.2 M
291 | layers.19.bn | BatchNorm2d | 1.0 K
292 | layers.19.leaky | LeakyReLU | 0
293 | layers.20 | ResidualBlock | 1.3 M
294 | layers.20.layers | ModuleList | 1.3 M
295 | layers.20.layers.0 | Sequential | 1.3 M
296 | layers.20.layers.0.0 | CNNBlock | 131 K
297 | layers.20.layers.0.0.conv | Conv2d | 131 K
298 | layers.20.layers.0.0.bn | BatchNorm2d | 512
299 | layers.20.layers.0.0.leaky | LeakyReLU | 0
300 | layers.20.layers.0.1 | CNNBlock | 1.2 M
301 | layers.20.layers.0.1.conv | Conv2d | 1.2 M
302 | layers.20.layers.0.1.bn | BatchNorm2d | 1.0 K
303 | layers.20.layers.0.1.leaky | LeakyReLU | 0
304 | layers.21 | CNNBlock | 131 K
305 | layers.21.conv | Conv2d | 131 K
306 | layers.21.bn | BatchNorm2d | 512
307 | layers.21.leaky | LeakyReLU | 0
308 | layers.22 | ScalePrediction | 1.2 M
309 | layers.22.pred | Sequential | 1.2 M
310 | layers.22.pred.0 | CNNBlock | 1.2 M
311 | layers.22.pred.0.conv | Conv2d | 1.2 M
312 | layers.22.pred.0.bn | BatchNorm2d | 1.0 K
313 | layers.22.pred.0.leaky | LeakyReLU | 0
314 | layers.22.pred.1 | CNNBlock | 38.6 K
315 | layers.22.pred.1.conv | Conv2d | 38.5 K
316 | layers.22.pred.1.bn | BatchNorm2d | 150
317 | layers.22.pred.1.leaky | LeakyReLU | 0
318 | layers.23 | CNNBlock | 33.0 K
319 | layers.23.conv | Conv2d | 32.8 K
320 | layers.23.bn | BatchNorm2d | 256
321 | layers.23.leaky | LeakyReLU | 0
322 | layers.24 | Upsample | 0
323 | layers.25 | CNNBlock | 49.4 K
324 | layers.25.conv | Conv2d | 49.2 K
325 | layers.25.bn | BatchNorm2d | 256
326 | layers.25.leaky | LeakyReLU | 0
327 | layers.26 | CNNBlock | 295 K
328 | layers.26.conv | Conv2d | 294 K
329 | layers.26.bn | BatchNorm2d | 512
330 | layers.26.leaky | LeakyReLU | 0
331 | layers.27 | ResidualBlock | 328 K
332 | layers.27.layers | ModuleList | 328 K
333 | layers.27.layers.0 | Sequential | 328 K
334 | layers.27.layers.0.0 | CNNBlock | 33.0 K
335 | layers.27.layers.0.0.conv | Conv2d | 32.8 K
336 | layers.27.layers.0.0.bn | BatchNorm2d | 256
337 | layers.27.layers.0.0.leaky | LeakyReLU | 0
338 | layers.27.layers.0.1 | CNNBlock | 295 K
339 | layers.27.layers.0.1.conv | Conv2d | 294 K
340 | layers.27.layers.0.1.bn | BatchNorm2d | 512
341 | layers.27.layers.0.1.leaky | LeakyReLU | 0
342 | layers.28 | CNNBlock | 33.0 K
343 | layers.28.conv | Conv2d | 32.8 K
344 | layers.28.bn | BatchNorm2d | 256
345 | layers.28.leaky | LeakyReLU | 0
346 | layers.29 | ScalePrediction | 314 K
347 | layers.29.pred | Sequential | 314 K
348 | layers.29.pred.0 | CNNBlock | 295 K
349 | layers.29.pred.0.conv | Conv2d | 294 K
350 | layers.29.pred.0.bn | BatchNorm2d | 512
351 | layers.29.pred.0.leaky | LeakyReLU | 0
352 | layers.29.pred.1 | CNNBlock | 19.4 K
353 | layers.29.pred.1.conv | Conv2d | 19.3 K
354 | layers.29.pred.1.bn | BatchNorm2d | 150
355 | layers.29.pred.1.leaky | LeakyReLU | 0
-------------------------------------------------------------------
61.6 M Trainable params
0 Non-trainable params
61.6 M Total params
246.506 Total estimated model params size (MB)
LR Finder
Loss & Accuracy
Training & Validation Loss:
Testing Accuracy:
0%| | 0/39 [00:00<?, ?it/s]
3%|▎ | 1/39 [00:05<03:24, 5.37s/it]
5%|▌ | 2/39 [00:11<03:32, 5.75s/it]
8%|▊ | 3/39 [00:16<03:14, 5.41s/it]
10%|█ | 4/39 [00:21<03:06, 5.33s/it]
13%|█▎ | 5/39 [00:26<02:55, 5.17s/it]
15%|█▌ | 6/39 [00:31<02:50, 5.16s/it]
18%|█▊ | 7/39 [00:36<02:43, 5.11s/it]
21%|██ | 8/39 [00:42<02:48, 5.43s/it]
23%|██▎ | 9/39 [00:48<02:44, 5.47s/it]
26%|██▌ | 10/39 [00:54<02:41, 5.58s/it]
28%|██▊ | 11/39 [00:59<02:36, 5.59s/it]
31%|███ | 12/39 [01:05<02:35, 5.77s/it]
33%|███▎ | 13/39 [01:11<02:28, 5.70s/it]
36%|███▌ | 14/39 [01:16<02:15, 5.42s/it]
38%|███▊ | 15/39 [01:21<02:07, 5.30s/it]
41%|████ | 16/39 [01:26<02:02, 5.34s/it]
44%|████▎ | 17/39 [01:31<01:54, 5.23s/it]
46%|████▌ | 18/39 [01:36<01:49, 5.22s/it]
49%|████▊ | 19/39 [01:42<01:43, 5.20s/it]
51%|█████▏ | 20/39 [01:46<01:33, 4.94s/it]
54%|█████▍ | 21/39 [01:50<01:23, 4.64s/it]
56%|█████▋ | 22/39 [01:54<01:14, 4.41s/it]
59%|█████▉ | 23/39 [01:57<01:03, 3.96s/it]
62%|██████▏ | 24/39 [02:00<00:54, 3.66s/it]
64%|██████▍ | 25/39 [02:04<00:55, 3.94s/it]
67%|██████▋ | 26/39 [02:10<00:56, 4.38s/it]
69%|██████▉ | 27/39 [02:14<00:53, 4.47s/it]
72%|███████▏ | 28/39 [02:20<00:52, 4.77s/it]
74%|███████▍ | 29/39 [02:25<00:50, 5.04s/it]
77%|███████▋ | 30/39 [02:31<00:47, 5.25s/it]
79%|███████▉ | 31/39 [02:37<00:42, 5.36s/it]
82%|████████▏ | 32/39 [02:42<00:38, 5.43s/it]
85%|████████▍ | 33/39 [02:47<00:31, 5.24s/it]
87%|████████▋ | 34/39 [02:53<00:26, 5.29s/it]
90%|████████▉ | 35/39 [02:58<00:21, 5.32s/it]
92%|█████████▏| 36/39 [03:03<00:15, 5.23s/it]
95%|█████████▍| 37/39 [03:08<00:10, 5.26s/it]
97%|█████████▋| 38/39 [03:14<00:05, 5.32s/it]
100%|██████████| 39/39 [03:17<00:00, 5.07s/it]
Class accuracy is: 81.601883%
No obj accuracy is: 97.991463%
Obj accuracy is: 75.976616%
MAP Calculations
0%| | 0/39 [00:00<?, ?it/s]
3%|▎ | 1/39 [00:40<25:35, 40.40s/it]
5%|▌ | 2/39 [01:24<26:05, 42.31s/it]
8%|▊ | 3/39 [02:01<24:02, 40.07s/it]
10%|█ | 4/39 [02:40<23:04, 39.57s/it]
13%|█▎ | 5/39 [03:36<25:45, 45.46s/it]
15%|█▌ | 6/39 [04:20<24:45, 45.00s/it]
18%|█▊ | 7/39 [05:03<23:37, 44.29s/it]
21%|██ | 8/39 [05:47<22:55, 44.36s/it]
23%|██▎ | 9/39 [06:33<22:25, 44.84s/it]
26%|██▌ | 10/39 [07:06<19:54, 41.20s/it]
28%|██▊ | 11/39 [07:58<20:45, 44.49s/it]
31%|███ | 12/39 [08:36<19:10, 42.60s/it]
33%|███▎ | 13/39 [09:20<18:33, 42.81s/it]
36%|███▌ | 14/39 [10:01<17:43, 42.53s/it]
38%|███▊ | 15/39 [10:42<16:49, 42.04s/it]
41%|████ | 16/39 [11:25<16:10, 42.18s/it]
44%|████▎ | 17/39 [12:12<16:02, 43.73s/it]
46%|████▌ | 18/39 [12:56<15:20, 43.83s/it]
49%|████▊ | 19/39 [13:36<14:12, 42.64s/it]
51%|█████▏ | 20/39 [14:20<13:37, 43.04s/it]
54%|█████▍ | 21/39 [14:58<12:27, 41.54s/it]
56%|█████▋ | 22/39 [15:43<12:01, 42.45s/it]
59%|█████▉ | 23/39 [16:29<11:35, 43.49s/it]
62%|██████▏ | 24/39 [17:13<10:55, 43.69s/it]
64%|██████▍ | 25/39 [18:02<10:34, 45.29s/it]
67%|██████▋ | 26/39 [18:41<09:25, 43.53s/it]
69%|██████▉ | 27/39 [19:26<08:45, 43.77s/it]
72%|███████▏ | 28/39 [20:04<07:44, 42.22s/it]
74%|███████▍ | 29/39 [20:45<06:56, 41.65s/it]
77%|███████▋ | 30/39 [21:32<06:30, 43.44s/it]
79%|███████▉ | 31/39 [22:16<05:47, 43.46s/it]
82%|████████▏ | 32/39 [22:52<04:49, 41.32s/it]
85%|████████▍ | 33/39 [23:36<04:13, 42.19s/it]
87%|████████▋ | 34/39 [24:18<03:29, 41.99s/it]
90%|████████▉ | 35/39 [25:00<02:48, 42.17s/it]
92%|█████████▏| 36/39 [25:46<02:09, 43.24s/it]
95%|█████████▍| 37/39 [26:29<01:26, 43.24s/it]
97%|█████████▋| 38/39 [27:18<00:44, 44.74s/it]
100%|██████████| 39/39 [27:46<00:00, 42.74s/it]
MAP: 0.43667954206466675
Tensorboard Plots
Training Loss vs Steps:
Validation Loss vs Steps:
(Info: Validation loss calculated every 10 epochs to save time, thats why the straight line)
GradCAM Representations
EigenCAM is used to generate CAM representation, since usal gradient based method wont work with detection models like Yolo, FRCNN etc.