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Yolov3-keras-style.txt
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Yolov3-keras-style.txt
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Model Yolov3 summary
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 32, 512, 512] 864
BatchNorm2d-2 [-1, 32, 512, 512] 64
LeakyReLU-3 [-1, 32, 512, 512] 0
Conv2d-4 [-1, 64, 256, 256] 18,432
BatchNorm2d-5 [-1, 64, 256, 256] 128
LeakyReLU-6 [-1, 64, 256, 256] 0
Conv2d-7 [-1, 32, 256, 256] 2,048
BatchNorm2d-8 [-1, 32, 256, 256] 64
LeakyReLU-9 [-1, 32, 256, 256] 0
Conv2d-10 [-1, 64, 256, 256] 18,432
BatchNorm2d-11 [-1, 64, 256, 256] 128
LeakyReLU-12 [-1, 64, 256, 256] 0
WeightedFeatureFusion-13 [-1, 64, 256, 256] 0
Conv2d-14 [-1, 128, 128, 128] 73,728
BatchNorm2d-15 [-1, 128, 128, 128] 256
LeakyReLU-16 [-1, 128, 128, 128] 0
Conv2d-17 [-1, 64, 128, 128] 8,192
BatchNorm2d-18 [-1, 64, 128, 128] 128
LeakyReLU-19 [-1, 64, 128, 128] 0
Conv2d-20 [-1, 128, 128, 128] 73,728
BatchNorm2d-21 [-1, 128, 128, 128] 256
LeakyReLU-22 [-1, 128, 128, 128] 0
WeightedFeatureFusion-23 [-1, 128, 128, 128] 0
Conv2d-24 [-1, 64, 128, 128] 8,192
BatchNorm2d-25 [-1, 64, 128, 128] 128
LeakyReLU-26 [-1, 64, 128, 128] 0
Conv2d-27 [-1, 128, 128, 128] 73,728
BatchNorm2d-28 [-1, 128, 128, 128] 256
LeakyReLU-29 [-1, 128, 128, 128] 0
WeightedFeatureFusion-30 [-1, 128, 128, 128] 0
Conv2d-31 [-1, 256, 64, 64] 294,912
BatchNorm2d-32 [-1, 256, 64, 64] 512
LeakyReLU-33 [-1, 256, 64, 64] 0
Conv2d-34 [-1, 128, 64, 64] 32,768
BatchNorm2d-35 [-1, 128, 64, 64] 256
LeakyReLU-36 [-1, 128, 64, 64] 0
Conv2d-37 [-1, 256, 64, 64] 294,912
BatchNorm2d-38 [-1, 256, 64, 64] 512
LeakyReLU-39 [-1, 256, 64, 64] 0
WeightedFeatureFusion-40 [-1, 256, 64, 64] 0
Conv2d-41 [-1, 128, 64, 64] 32,768
BatchNorm2d-42 [-1, 128, 64, 64] 256
LeakyReLU-43 [-1, 128, 64, 64] 0
Conv2d-44 [-1, 256, 64, 64] 294,912
BatchNorm2d-45 [-1, 256, 64, 64] 512
LeakyReLU-46 [-1, 256, 64, 64] 0
WeightedFeatureFusion-47 [-1, 256, 64, 64] 0
Conv2d-48 [-1, 128, 64, 64] 32,768
BatchNorm2d-49 [-1, 128, 64, 64] 256
LeakyReLU-50 [-1, 128, 64, 64] 0
Conv2d-51 [-1, 256, 64, 64] 294,912
BatchNorm2d-52 [-1, 256, 64, 64] 512
LeakyReLU-53 [-1, 256, 64, 64] 0
WeightedFeatureFusion-54 [-1, 256, 64, 64] 0
Conv2d-55 [-1, 128, 64, 64] 32,768
BatchNorm2d-56 [-1, 128, 64, 64] 256
LeakyReLU-57 [-1, 128, 64, 64] 0
Conv2d-58 [-1, 256, 64, 64] 294,912
BatchNorm2d-59 [-1, 256, 64, 64] 512
LeakyReLU-60 [-1, 256, 64, 64] 0
WeightedFeatureFusion-61 [-1, 256, 64, 64] 0
Conv2d-62 [-1, 128, 64, 64] 32,768
BatchNorm2d-63 [-1, 128, 64, 64] 256
LeakyReLU-64 [-1, 128, 64, 64] 0
Conv2d-65 [-1, 256, 64, 64] 294,912
BatchNorm2d-66 [-1, 256, 64, 64] 512
LeakyReLU-67 [-1, 256, 64, 64] 0
WeightedFeatureFusion-68 [-1, 256, 64, 64] 0
Conv2d-69 [-1, 128, 64, 64] 32,768
BatchNorm2d-70 [-1, 128, 64, 64] 256
LeakyReLU-71 [-1, 128, 64, 64] 0
Conv2d-72 [-1, 256, 64, 64] 294,912
BatchNorm2d-73 [-1, 256, 64, 64] 512
LeakyReLU-74 [-1, 256, 64, 64] 0
WeightedFeatureFusion-75 [-1, 256, 64, 64] 0
Conv2d-76 [-1, 128, 64, 64] 32,768
BatchNorm2d-77 [-1, 128, 64, 64] 256
LeakyReLU-78 [-1, 128, 64, 64] 0
Conv2d-79 [-1, 256, 64, 64] 294,912
BatchNorm2d-80 [-1, 256, 64, 64] 512
LeakyReLU-81 [-1, 256, 64, 64] 0
WeightedFeatureFusion-82 [-1, 256, 64, 64] 0
Conv2d-83 [-1, 128, 64, 64] 32,768
BatchNorm2d-84 [-1, 128, 64, 64] 256
LeakyReLU-85 [-1, 128, 64, 64] 0
Conv2d-86 [-1, 256, 64, 64] 294,912
BatchNorm2d-87 [-1, 256, 64, 64] 512
LeakyReLU-88 [-1, 256, 64, 64] 0
WeightedFeatureFusion-89 [-1, 256, 64, 64] 0
Conv2d-90 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-91 [-1, 512, 32, 32] 1,024
LeakyReLU-92 [-1, 512, 32, 32] 0
Conv2d-93 [-1, 256, 32, 32] 131,072
BatchNorm2d-94 [-1, 256, 32, 32] 512
LeakyReLU-95 [-1, 256, 32, 32] 0
Conv2d-96 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-97 [-1, 512, 32, 32] 1,024
LeakyReLU-98 [-1, 512, 32, 32] 0
WeightedFeatureFusion-99 [-1, 512, 32, 32] 0
Conv2d-100 [-1, 256, 32, 32] 131,072
BatchNorm2d-101 [-1, 256, 32, 32] 512
LeakyReLU-102 [-1, 256, 32, 32] 0
Conv2d-103 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-104 [-1, 512, 32, 32] 1,024
LeakyReLU-105 [-1, 512, 32, 32] 0
WeightedFeatureFusion-106 [-1, 512, 32, 32] 0
Conv2d-107 [-1, 256, 32, 32] 131,072
BatchNorm2d-108 [-1, 256, 32, 32] 512
LeakyReLU-109 [-1, 256, 32, 32] 0
Conv2d-110 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-111 [-1, 512, 32, 32] 1,024
LeakyReLU-112 [-1, 512, 32, 32] 0
WeightedFeatureFusion-113 [-1, 512, 32, 32] 0
Conv2d-114 [-1, 256, 32, 32] 131,072
BatchNorm2d-115 [-1, 256, 32, 32] 512
LeakyReLU-116 [-1, 256, 32, 32] 0
Conv2d-117 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-118 [-1, 512, 32, 32] 1,024
LeakyReLU-119 [-1, 512, 32, 32] 0
WeightedFeatureFusion-120 [-1, 512, 32, 32] 0
Conv2d-121 [-1, 256, 32, 32] 131,072
BatchNorm2d-122 [-1, 256, 32, 32] 512
LeakyReLU-123 [-1, 256, 32, 32] 0
Conv2d-124 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-125 [-1, 512, 32, 32] 1,024
LeakyReLU-126 [-1, 512, 32, 32] 0
WeightedFeatureFusion-127 [-1, 512, 32, 32] 0
Conv2d-128 [-1, 256, 32, 32] 131,072
BatchNorm2d-129 [-1, 256, 32, 32] 512
LeakyReLU-130 [-1, 256, 32, 32] 0
Conv2d-131 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-132 [-1, 512, 32, 32] 1,024
LeakyReLU-133 [-1, 512, 32, 32] 0
WeightedFeatureFusion-134 [-1, 512, 32, 32] 0
Conv2d-135 [-1, 256, 32, 32] 131,072
BatchNorm2d-136 [-1, 256, 32, 32] 512
LeakyReLU-137 [-1, 256, 32, 32] 0
Conv2d-138 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-139 [-1, 512, 32, 32] 1,024
LeakyReLU-140 [-1, 512, 32, 32] 0
WeightedFeatureFusion-141 [-1, 512, 32, 32] 0
Conv2d-142 [-1, 256, 32, 32] 131,072
BatchNorm2d-143 [-1, 256, 32, 32] 512
LeakyReLU-144 [-1, 256, 32, 32] 0
Conv2d-145 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-146 [-1, 512, 32, 32] 1,024
LeakyReLU-147 [-1, 512, 32, 32] 0
WeightedFeatureFusion-148 [-1, 512, 32, 32] 0
Conv2d-149 [-1, 1024, 16, 16] 4,718,592
BatchNorm2d-150 [-1, 1024, 16, 16] 2,048
LeakyReLU-151 [-1, 1024, 16, 16] 0
Conv2d-152 [-1, 512, 16, 16] 524,288
BatchNorm2d-153 [-1, 512, 16, 16] 1,024
LeakyReLU-154 [-1, 512, 16, 16] 0
Conv2d-155 [-1, 1024, 16, 16] 4,718,592
BatchNorm2d-156 [-1, 1024, 16, 16] 2,048
LeakyReLU-157 [-1, 1024, 16, 16] 0
WeightedFeatureFusion-158 [-1, 1024, 16, 16] 0
Conv2d-159 [-1, 512, 16, 16] 524,288
BatchNorm2d-160 [-1, 512, 16, 16] 1,024
LeakyReLU-161 [-1, 512, 16, 16] 0
Conv2d-162 [-1, 1024, 16, 16] 4,718,592
BatchNorm2d-163 [-1, 1024, 16, 16] 2,048
LeakyReLU-164 [-1, 1024, 16, 16] 0
WeightedFeatureFusion-165 [-1, 1024, 16, 16] 0
Conv2d-166 [-1, 512, 16, 16] 524,288
BatchNorm2d-167 [-1, 512, 16, 16] 1,024
LeakyReLU-168 [-1, 512, 16, 16] 0
Conv2d-169 [-1, 1024, 16, 16] 4,718,592
BatchNorm2d-170 [-1, 1024, 16, 16] 2,048
LeakyReLU-171 [-1, 1024, 16, 16] 0
WeightedFeatureFusion-172 [-1, 1024, 16, 16] 0
Conv2d-173 [-1, 512, 16, 16] 524,288
BatchNorm2d-174 [-1, 512, 16, 16] 1,024
LeakyReLU-175 [-1, 512, 16, 16] 0
Conv2d-176 [-1, 1024, 16, 16] 4,718,592
BatchNorm2d-177 [-1, 1024, 16, 16] 2,048
LeakyReLU-178 [-1, 1024, 16, 16] 0
WeightedFeatureFusion-179 [-1, 1024, 16, 16] 0
Conv2d-180 [-1, 512, 16, 16] 524,288
BatchNorm2d-181 [-1, 512, 16, 16] 1,024
LeakyReLU-182 [-1, 512, 16, 16] 0
Conv2d-183 [-1, 1024, 16, 16] 4,718,592
BatchNorm2d-184 [-1, 1024, 16, 16] 2,048
LeakyReLU-185 [-1, 1024, 16, 16] 0
Conv2d-186 [-1, 512, 16, 16] 524,288
BatchNorm2d-187 [-1, 512, 16, 16] 1,024
LeakyReLU-188 [-1, 512, 16, 16] 0
Conv2d-189 [-1, 1024, 16, 16] 4,718,592
BatchNorm2d-190 [-1, 1024, 16, 16] 2,048
LeakyReLU-191 [-1, 1024, 16, 16] 0
Conv2d-192 [-1, 512, 16, 16] 524,288
BatchNorm2d-193 [-1, 512, 16, 16] 1,024
LeakyReLU-194 [-1, 512, 16, 16] 0
Conv2d-195 [-1, 1024, 16, 16] 4,718,592
BatchNorm2d-196 [-1, 1024, 16, 16] 2,048
LeakyReLU-197 [-1, 1024, 16, 16] 0
Conv2d-198 [-1, 255, 16, 16] 261,375
YOLOLayer-199 [-1, 3, 16, 16, 85] 0
FeatureConcat-200 [-1, 512, 16, 16] 0
Conv2d-201 [-1, 256, 16, 16] 131,072
BatchNorm2d-202 [-1, 256, 16, 16] 512
LeakyReLU-203 [-1, 256, 16, 16] 0
Upsample-204 [-1, 256, 32, 32] 0
FeatureConcat-205 [-1, 768, 32, 32] 0
Conv2d-206 [-1, 256, 32, 32] 196,608
BatchNorm2d-207 [-1, 256, 32, 32] 512
LeakyReLU-208 [-1, 256, 32, 32] 0
Conv2d-209 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-210 [-1, 512, 32, 32] 1,024
LeakyReLU-211 [-1, 512, 32, 32] 0
Conv2d-212 [-1, 256, 32, 32] 131,072
BatchNorm2d-213 [-1, 256, 32, 32] 512
LeakyReLU-214 [-1, 256, 32, 32] 0
Conv2d-215 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-216 [-1, 512, 32, 32] 1,024
LeakyReLU-217 [-1, 512, 32, 32] 0
Conv2d-218 [-1, 256, 32, 32] 131,072
BatchNorm2d-219 [-1, 256, 32, 32] 512
LeakyReLU-220 [-1, 256, 32, 32] 0
Conv2d-221 [-1, 512, 32, 32] 1,179,648
BatchNorm2d-222 [-1, 512, 32, 32] 1,024
LeakyReLU-223 [-1, 512, 32, 32] 0
Conv2d-224 [-1, 255, 32, 32] 130,815
YOLOLayer-225 [-1, 3, 32, 32, 85] 0
FeatureConcat-226 [-1, 256, 32, 32] 0
Conv2d-227 [-1, 128, 32, 32] 32,768
BatchNorm2d-228 [-1, 128, 32, 32] 256
LeakyReLU-229 [-1, 128, 32, 32] 0
Upsample-230 [-1, 128, 64, 64] 0
FeatureConcat-231 [-1, 384, 64, 64] 0
Conv2d-232 [-1, 128, 64, 64] 49,152
BatchNorm2d-233 [-1, 128, 64, 64] 256
LeakyReLU-234 [-1, 128, 64, 64] 0
Conv2d-235 [-1, 256, 64, 64] 294,912
BatchNorm2d-236 [-1, 256, 64, 64] 512
LeakyReLU-237 [-1, 256, 64, 64] 0
Conv2d-238 [-1, 128, 64, 64] 32,768
BatchNorm2d-239 [-1, 128, 64, 64] 256
LeakyReLU-240 [-1, 128, 64, 64] 0
Conv2d-241 [-1, 256, 64, 64] 294,912
BatchNorm2d-242 [-1, 256, 64, 64] 512
LeakyReLU-243 [-1, 256, 64, 64] 0
Conv2d-244 [-1, 128, 64, 64] 32,768
BatchNorm2d-245 [-1, 128, 64, 64] 256
LeakyReLU-246 [-1, 128, 64, 64] 0
Conv2d-247 [-1, 256, 64, 64] 294,912
BatchNorm2d-248 [-1, 256, 64, 64] 512
LeakyReLU-249 [-1, 256, 64, 64] 0
Conv2d-250 [-1, 255, 64, 64] 65,535
YOLOLayer-251 [-1, 3, 64, 64, 85] 0
================================================================
Total params: 61,949,149
Trainable params: 61,949,149
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 3.00
Forward/backward pass size (MB): 1543.42
Params size (MB): 236.32
Estimated Total Size (MB): 1782.74