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@Kumar-laxmi
Kumar-laxmi committed Mar 7, 2024
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from n params module arguments
0 -1 1 928 ultralytics.nn.modules.Conv [3, 32, 3, 2]
1 -1 1 18560 ultralytics.nn.modules.Conv [32, 64, 3, 2]
2 -1 1 29056 ultralytics.nn.modules.C2f [64, 64, 1, True]
3 -1 1 73984 ultralytics.nn.modules.Conv [64, 128, 3, 2]
4 -1 2 197632 ultralytics.nn.modules.C2f [128, 128, 2, True]
5 -1 1 295424 ultralytics.nn.modules.Conv [128, 256, 3, 2]
6 -1 2 788480 ultralytics.nn.modules.C2f [256, 256, 2, True]
7 -1 1 1180672 ultralytics.nn.modules.Conv [256, 512, 3, 2]
8 -1 1 1838080 ultralytics.nn.modules.C2f [512, 512, 1, True]
9 -1 1 656896 ultralytics.nn.modules.SPPF [512, 512, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.Concat [1]
12 -1 1 591360 ultralytics.nn.modules.C2f [768, 256, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.Concat [1]
15 -1 1 148224 ultralytics.nn.modules.C2f [384, 128, 1]
16 -1 1 147712 ultralytics.nn.modules.Conv [128, 128, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.Concat [1]
18 -1 1 493056 ultralytics.nn.modules.C2f [384, 256, 1]
19 -1 1 590336 ultralytics.nn.modules.Conv [256, 256, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.Concat [1]
21 -1 1 1969152 ultralytics.nn.modules.C2f [768, 512, 1]
22 [15, 18, 21] 1 2117596 ultralytics.nn.modules.Detect [4, [128, 256, 512]]
Model summary: 225 layers, 11137148 parameters, 11137132 gradients, 28.7 GFLOPs
Table 1. Training & Validation Performance of Model for all Classes
Model summary: 168 layers, 11127132 parameters, 0 gradients, 28.4 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100% 11/11 [00:30<00:00, 2.80s/it]
all 347 5594 0.991 0.986 0.993 0.874
Bicycle 347 410 0.984 0.976 0.993 0.618
Car 347 3632 0.998 0.992 0.995 0.937
Bus 347 323 0.999 0.994 0.995 0.989
Lorry 347 1229 0.983 0.982 0.988 0.951

Class Images Instances Box(P R mAP50 mAP50-95): 100% 22/22 [00:31<00:00, 1.44s/it]
all 347 5594 0.99 0.978 0.992 0.862
Bicycle 347 410 0.969 0.949 0.985 0.578
Car 347 3632 0.997 0.989 0.995 0.936
Bus 347 323 0.998 0.994 0.995 0.987
Lorry 347 1229 0.995 0.982 0.995 0.946

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