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 ---
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/GunaKoppula/Session13
### Achieved:
1. **Training Loss: 3.680**
2. **Validation Loss: 4.940**
3. **Class accuracy: 81.601883%**
4. **No obj accuracy: 97.991463%**
5. **Obj accuracy: 75.976616%**
6. **MAP: 0.4366795**
### Tasks:
1. :heavy_check_mark: Move the code to PytorchLightning
2. :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
3. :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.
4. :heavy_check_mark: Things that are allowed due to HW constraints:
- Change of batch size
- Change of resolution
- Change of OCP parameters
5. :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.
6. :heavy_check_mark: Mention things like:
- classes that your model support
- link to the actual model
7. :heavy_check_mark: Assignment:
- Share HuggingFace App Link
- Share LightningCode Link on Github
- Share notebook link (with logs) on GitHub
### Results
![image](https://github.com/GunaKoppula/ERAV1-Session-13/blob/main/yolo_results.png)
### Gradio App
![image](https://github.com/GunaKoppula/ERAV1-Session-13/assets/61241928/5304d1e4-a545-4b8c-951c-10cc5da09e00)
![image](https://github.com/GunaKoppula/ERAV1-Session-13/assets/61241928/8d558059-4477-4d54-a0c6-9f6bb424c77c)
### Model Summary
```python
| 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
![image](https://github.com/GunaKoppula/ERAV1-Session-13/assets/61241928/7ffabc81-f1d3-4379-bbfb-6ba7da277a02)
### Loss & Accuracy
**Training & Validation Loss:**
![image](https://github.com/GunaKoppula/ERAV1-Session-13/assets/61241928/332fda1e-acfb-4aec-979f-93984bc43e2d)
**Testing Accuracy:**
```python
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
```python
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:** ![image](https://github.com/GunaKoppula/ERAV1-Session-13/assets/61241928/3b4fb334-5b2a-45b0-a892-5222c147160a)
**Validation Loss vs Steps:**
(Info: Validation loss calculated every 10 epochs to save time, that's why the straight line)
![image](https://github.com/GunaKoppula/ERAV1-Session-13/assets/61241928/8b5f2b66-41d9-40bc-80f9-94589d5ffb59)
### GradCAM Representations
EigenCAM is used to generate CAM representation, since the usual gradient-based method won't work with detection models like Yolo, FRCNN, etc.
![image](https://github.com/GunaKoppula/ERAV1-Session-13/assets/61241928/0f679781-fe30-41f8-9625-8fa312ae7f38)