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Yolo-v3 tiny *.weights file contains less weights then expected



2019 Community Moderator ElectionVisualizing ConvNet filters using my own fine-tuned network resulting in a “NoneType” when running: K.gradients(loss, model.input)[0]How to extract all the information from a midi file (monophonic and polyphonic) and then vectorize them to feed into a Neural Network?YOLO v3 complete architecture










0












$begingroup$


I have builded a Yolo V3 Tiny model in Tensorflow and I would like to load the weights provided by Yolo itself. I found here and reading the official Yolo code, that I can read yolov3-tiny.weights discarding the first 16 bytes and then reading the remaining bytes converting them in float32.



Now, yolov3-tiny.weights has 35.434.956 bytes, so (35.434.956-16)/4=8.858.735 float32 numbers and so I should have 8.858.735 weights.



Anyway the summary of my yolov3-tiny network is the following:



>>> model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to 
==================================================================================================
Input (InputLayer) [(None, 416, 416, 3) 0 
__________________________________________________________________________________________________
conv_1 (Conv2D) (None, 416, 416, 16) 448 Input[0][0] 
__________________________________________________________________________________________________
norm_1 (BatchNormalizationV1) (None, 416, 416, 16) 64 conv_1[0][0] 
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 208, 208, 16) 0 norm_1[0][0] 
__________________________________________________________________________________________________
conv_2 (Conv2D) (None, 208, 208, 32) 4640 max_pooling2d[0][0] 
__________________________________________________________________________________________________
norm_2 (BatchNormalizationV1) (None, 208, 208, 32) 128 conv_2[0][0] 
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 104, 104, 32) 0 norm_2[0][0] 
__________________________________________________________________________________________________
conv_3 (Conv2D) (None, 104, 104, 64) 18496 max_pooling2d_1[0][0] 
__________________________________________________________________________________________________
norm_3 (BatchNormalizationV1) (None, 104, 104, 64) 256 conv_3[0][0] 
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 52, 52, 64) 0 norm_3[0][0] 
__________________________________________________________________________________________________
conv_4 (Conv2D) (None, 52, 52, 128) 73856 max_pooling2d_2[0][0] 
__________________________________________________________________________________________________
norm_4 (BatchNormalizationV1) (None, 52, 52, 128) 512 conv_4[0][0] 
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 26, 26, 128) 0 norm_4[0][0] 
__________________________________________________________________________________________________
conv_5 (Conv2D) (None, 26, 26, 256) 295168 max_pooling2d_3[0][0] 
__________________________________________________________________________________________________
norm_5 (BatchNormalizationV1) (None, 26, 26, 256) 1024 conv_5[0][0] 
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 13, 13, 256) 0 norm_5[0][0] 
__________________________________________________________________________________________________
conv_6 (Conv2D) (None, 13, 13, 512) 1180160 max_pooling2d_4[0][0] 
__________________________________________________________________________________________________
norm_6 (BatchNormalizationV1) (None, 13, 13, 512) 2048 conv_6[0][0] 
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D) (None, 13, 13, 512) 0 norm_6[0][0] 
__________________________________________________________________________________________________
conv_7 (Conv2D) (None, 13, 13, 1024) 4719616 max_pooling2d_5[0][0] 
__________________________________________________________________________________________________
norm_7 (BatchNormalizationV1) (None, 13, 13, 1024) 4096 conv_7[0][0] 
__________________________________________________________________________________________________
conv_8 (Conv2D) (None, 13, 13, 256) 262400 norm_7[0][0] 
__________________________________________________________________________________________________
norm_8 (BatchNormalizationV1) (None, 13, 13, 256) 1024 conv_8[0][0] 
__________________________________________________________________________________________________
conv_11 (Conv2D) (None, 13, 13, 128) 32896 norm_8[0][0] 
__________________________________________________________________________________________________
norm_10 (BatchNormalizationV1) (None, 13, 13, 128) 512 conv_11[0][0] 
__________________________________________________________________________________________________
lambda_1 (Lambda) (None, 26, 26, 128) 0 norm_10[0][0] 
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 26, 26, 384) 0 lambda_1[0][0] 
 norm_5[0][0] 
__________________________________________________________________________________________________
conv_9 (Conv2D) (None, 13, 13, 512) 1180160 norm_8[0][0] 
__________________________________________________________________________________________________
conv_12 (Conv2D) (None, 26, 26, 256) 884992 concatenate[0][0] 
__________________________________________________________________________________________________
norm_9 (BatchNormalizationV1) (None, 13, 13, 512) 2048 conv_9[0][0] 
__________________________________________________________________________________________________
norm_11 (BatchNormalizationV1) (None, 26, 26, 256) 1024 conv_12[0][0] 
__________________________________________________________________________________________________
conv_10 (Conv2D) (None, 13, 13, 255) 130815 norm_9[0][0] 
__________________________________________________________________________________________________
conv_13 (Conv2D) (None, 26, 26, 255) 65535 norm_11[0][0] 
__________________________________________________________________________________________________
lambda (Lambda) (None, 507, 85) 0 conv_10[0][0] 
__________________________________________________________________________________________________
lambda_2 (Lambda) (None, 2028, 85) 0 conv_13[0][0] 
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 2535, 85) 0 lambda[0][0] 
 lambda_2[0][0] 
==================================================================================================
Total params: 8,861,918
Trainable params: 8,855,550
Non-trainable params: 6,368
__________________________________________________________________________________________________


and has 8.861.918 weights. There are (8.861.918-8.858.735)=3183 parameters more then those contained in yolov3-tiny.weights. Had I make any error building the network or am I missing something?



Thank you.






python convnet yolo







share|improve this question









$endgroup$
















    0












    $begingroup$


    I have builded a Yolo V3 Tiny model in Tensorflow and I would like to load the weights provided by Yolo itself. I found here and reading the official Yolo code, that I can read yolov3-tiny.weights discarding the first 16 bytes and then reading the remaining bytes converting them in float32.



    Now, yolov3-tiny.weights has 35.434.956 bytes, so (35.434.956-16)/4=8.858.735 float32 numbers and so I should have 8.858.735 weights.



    Anyway the summary of my yolov3-tiny network is the following:



    >>> model.summary()
    Model: "model"
    __________________________________________________________________________________________________
    Layer (type) Output Shape Param # Connected to 
    ==================================================================================================
    Input (InputLayer) [(None, 416, 416, 3) 0 
    __________________________________________________________________________________________________
    conv_1 (Conv2D) (None, 416, 416, 16) 448 Input[0][0] 
    __________________________________________________________________________________________________
    norm_1 (BatchNormalizationV1) (None, 416, 416, 16) 64 conv_1[0][0] 
    __________________________________________________________________________________________________
    max_pooling2d (MaxPooling2D) (None, 208, 208, 16) 0 norm_1[0][0] 
    __________________________________________________________________________________________________
    conv_2 (Conv2D) (None, 208, 208, 32) 4640 max_pooling2d[0][0] 
    __________________________________________________________________________________________________
    norm_2 (BatchNormalizationV1) (None, 208, 208, 32) 128 conv_2[0][0] 
    __________________________________________________________________________________________________
    max_pooling2d_1 (MaxPooling2D) (None, 104, 104, 32) 0 norm_2[0][0] 
    __________________________________________________________________________________________________
    conv_3 (Conv2D) (None, 104, 104, 64) 18496 max_pooling2d_1[0][0] 
    __________________________________________________________________________________________________
    norm_3 (BatchNormalizationV1) (None, 104, 104, 64) 256 conv_3[0][0] 
    __________________________________________________________________________________________________
    max_pooling2d_2 (MaxPooling2D) (None, 52, 52, 64) 0 norm_3[0][0] 
    __________________________________________________________________________________________________
    conv_4 (Conv2D) (None, 52, 52, 128) 73856 max_pooling2d_2[0][0] 
    __________________________________________________________________________________________________
    norm_4 (BatchNormalizationV1) (None, 52, 52, 128) 512 conv_4[0][0] 
    __________________________________________________________________________________________________
    max_pooling2d_3 (MaxPooling2D) (None, 26, 26, 128) 0 norm_4[0][0] 
    __________________________________________________________________________________________________
    conv_5 (Conv2D) (None, 26, 26, 256) 295168 max_pooling2d_3[0][0] 
    __________________________________________________________________________________________________
    norm_5 (BatchNormalizationV1) (None, 26, 26, 256) 1024 conv_5[0][0] 
    __________________________________________________________________________________________________
    max_pooling2d_4 (MaxPooling2D) (None, 13, 13, 256) 0 norm_5[0][0] 
    __________________________________________________________________________________________________
    conv_6 (Conv2D) (None, 13, 13, 512) 1180160 max_pooling2d_4[0][0] 
    __________________________________________________________________________________________________
    norm_6 (BatchNormalizationV1) (None, 13, 13, 512) 2048 conv_6[0][0] 
    __________________________________________________________________________________________________
    max_pooling2d_5 (MaxPooling2D) (None, 13, 13, 512) 0 norm_6[0][0] 
    __________________________________________________________________________________________________
    conv_7 (Conv2D) (None, 13, 13, 1024) 4719616 max_pooling2d_5[0][0] 
    __________________________________________________________________________________________________
    norm_7 (BatchNormalizationV1) (None, 13, 13, 1024) 4096 conv_7[0][0] 
    __________________________________________________________________________________________________
    conv_8 (Conv2D) (None, 13, 13, 256) 262400 norm_7[0][0] 
    __________________________________________________________________________________________________
    norm_8 (BatchNormalizationV1) (None, 13, 13, 256) 1024 conv_8[0][0] 
    __________________________________________________________________________________________________
    conv_11 (Conv2D) (None, 13, 13, 128) 32896 norm_8[0][0] 
    __________________________________________________________________________________________________
    norm_10 (BatchNormalizationV1) (None, 13, 13, 128) 512 conv_11[0][0] 
    __________________________________________________________________________________________________
    lambda_1 (Lambda) (None, 26, 26, 128) 0 norm_10[0][0] 
    __________________________________________________________________________________________________
    concatenate (Concatenate) (None, 26, 26, 384) 0 lambda_1[0][0] 
     norm_5[0][0] 
    __________________________________________________________________________________________________
    conv_9 (Conv2D) (None, 13, 13, 512) 1180160 norm_8[0][0] 
    __________________________________________________________________________________________________
    conv_12 (Conv2D) (None, 26, 26, 256) 884992 concatenate[0][0] 
    __________________________________________________________________________________________________
    norm_9 (BatchNormalizationV1) (None, 13, 13, 512) 2048 conv_9[0][0] 
    __________________________________________________________________________________________________
    norm_11 (BatchNormalizationV1) (None, 26, 26, 256) 1024 conv_12[0][0] 
    __________________________________________________________________________________________________
    conv_10 (Conv2D) (None, 13, 13, 255) 130815 norm_9[0][0] 
    __________________________________________________________________________________________________
    conv_13 (Conv2D) (None, 26, 26, 255) 65535 norm_11[0][0] 
    __________________________________________________________________________________________________
    lambda (Lambda) (None, 507, 85) 0 conv_10[0][0] 
    __________________________________________________________________________________________________
    lambda_2 (Lambda) (None, 2028, 85) 0 conv_13[0][0] 
    __________________________________________________________________________________________________
    concatenate_1 (Concatenate) (None, 2535, 85) 0 lambda[0][0] 
     lambda_2[0][0] 
    ==================================================================================================
    Total params: 8,861,918
    Trainable params: 8,855,550
    Non-trainable params: 6,368
    __________________________________________________________________________________________________


    and has 8.861.918 weights. There are (8.861.918-8.858.735)=3183 parameters more then those contained in yolov3-tiny.weights. Had I make any error building the network or am I missing something?



    Thank you.






    python convnet yolo







    share|improve this question









    $endgroup$














      0












      0








      0





      $begingroup$


      I have builded a Yolo V3 Tiny model in Tensorflow and I would like to load the weights provided by Yolo itself. I found here and reading the official Yolo code, that I can read yolov3-tiny.weights discarding the first 16 bytes and then reading the remaining bytes converting them in float32.



      Now, yolov3-tiny.weights has 35.434.956 bytes, so (35.434.956-16)/4=8.858.735 float32 numbers and so I should have 8.858.735 weights.



      Anyway the summary of my yolov3-tiny network is the following:



      >>> model.summary()
      Model: "model"
      __________________________________________________________________________________________________
      Layer (type) Output Shape Param # Connected to 
      ==================================================================================================
      Input (InputLayer) [(None, 416, 416, 3) 0 
      __________________________________________________________________________________________________
      conv_1 (Conv2D) (None, 416, 416, 16) 448 Input[0][0] 
      __________________________________________________________________________________________________
      norm_1 (BatchNormalizationV1) (None, 416, 416, 16) 64 conv_1[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d (MaxPooling2D) (None, 208, 208, 16) 0 norm_1[0][0] 
      __________________________________________________________________________________________________
      conv_2 (Conv2D) (None, 208, 208, 32) 4640 max_pooling2d[0][0] 
      __________________________________________________________________________________________________
      norm_2 (BatchNormalizationV1) (None, 208, 208, 32) 128 conv_2[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d_1 (MaxPooling2D) (None, 104, 104, 32) 0 norm_2[0][0] 
      __________________________________________________________________________________________________
      conv_3 (Conv2D) (None, 104, 104, 64) 18496 max_pooling2d_1[0][0] 
      __________________________________________________________________________________________________
      norm_3 (BatchNormalizationV1) (None, 104, 104, 64) 256 conv_3[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d_2 (MaxPooling2D) (None, 52, 52, 64) 0 norm_3[0][0] 
      __________________________________________________________________________________________________
      conv_4 (Conv2D) (None, 52, 52, 128) 73856 max_pooling2d_2[0][0] 
      __________________________________________________________________________________________________
      norm_4 (BatchNormalizationV1) (None, 52, 52, 128) 512 conv_4[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d_3 (MaxPooling2D) (None, 26, 26, 128) 0 norm_4[0][0] 
      __________________________________________________________________________________________________
      conv_5 (Conv2D) (None, 26, 26, 256) 295168 max_pooling2d_3[0][0] 
      __________________________________________________________________________________________________
      norm_5 (BatchNormalizationV1) (None, 26, 26, 256) 1024 conv_5[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d_4 (MaxPooling2D) (None, 13, 13, 256) 0 norm_5[0][0] 
      __________________________________________________________________________________________________
      conv_6 (Conv2D) (None, 13, 13, 512) 1180160 max_pooling2d_4[0][0] 
      __________________________________________________________________________________________________
      norm_6 (BatchNormalizationV1) (None, 13, 13, 512) 2048 conv_6[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d_5 (MaxPooling2D) (None, 13, 13, 512) 0 norm_6[0][0] 
      __________________________________________________________________________________________________
      conv_7 (Conv2D) (None, 13, 13, 1024) 4719616 max_pooling2d_5[0][0] 
      __________________________________________________________________________________________________
      norm_7 (BatchNormalizationV1) (None, 13, 13, 1024) 4096 conv_7[0][0] 
      __________________________________________________________________________________________________
      conv_8 (Conv2D) (None, 13, 13, 256) 262400 norm_7[0][0] 
      __________________________________________________________________________________________________
      norm_8 (BatchNormalizationV1) (None, 13, 13, 256) 1024 conv_8[0][0] 
      __________________________________________________________________________________________________
      conv_11 (Conv2D) (None, 13, 13, 128) 32896 norm_8[0][0] 
      __________________________________________________________________________________________________
      norm_10 (BatchNormalizationV1) (None, 13, 13, 128) 512 conv_11[0][0] 
      __________________________________________________________________________________________________
      lambda_1 (Lambda) (None, 26, 26, 128) 0 norm_10[0][0] 
      __________________________________________________________________________________________________
      concatenate (Concatenate) (None, 26, 26, 384) 0 lambda_1[0][0] 
       norm_5[0][0] 
      __________________________________________________________________________________________________
      conv_9 (Conv2D) (None, 13, 13, 512) 1180160 norm_8[0][0] 
      __________________________________________________________________________________________________
      conv_12 (Conv2D) (None, 26, 26, 256) 884992 concatenate[0][0] 
      __________________________________________________________________________________________________
      norm_9 (BatchNormalizationV1) (None, 13, 13, 512) 2048 conv_9[0][0] 
      __________________________________________________________________________________________________
      norm_11 (BatchNormalizationV1) (None, 26, 26, 256) 1024 conv_12[0][0] 
      __________________________________________________________________________________________________
      conv_10 (Conv2D) (None, 13, 13, 255) 130815 norm_9[0][0] 
      __________________________________________________________________________________________________
      conv_13 (Conv2D) (None, 26, 26, 255) 65535 norm_11[0][0] 
      __________________________________________________________________________________________________
      lambda (Lambda) (None, 507, 85) 0 conv_10[0][0] 
      __________________________________________________________________________________________________
      lambda_2 (Lambda) (None, 2028, 85) 0 conv_13[0][0] 
      __________________________________________________________________________________________________
      concatenate_1 (Concatenate) (None, 2535, 85) 0 lambda[0][0] 
       lambda_2[0][0] 
      ==================================================================================================
      Total params: 8,861,918
      Trainable params: 8,855,550
      Non-trainable params: 6,368
      __________________________________________________________________________________________________


      and has 8.861.918 weights. There are (8.861.918-8.858.735)=3183 parameters more then those contained in yolov3-tiny.weights. Had I make any error building the network or am I missing something?



      Thank you.






      python convnet yolo







      share|improve this question









      $endgroup$




      I have builded a Yolo V3 Tiny model in Tensorflow and I would like to load the weights provided by Yolo itself. I found here and reading the official Yolo code, that I can read yolov3-tiny.weights discarding the first 16 bytes and then reading the remaining bytes converting them in float32.



      Now, yolov3-tiny.weights has 35.434.956 bytes, so (35.434.956-16)/4=8.858.735 float32 numbers and so I should have 8.858.735 weights.



      Anyway the summary of my yolov3-tiny network is the following:



      >>> model.summary()
      Model: "model"
      __________________________________________________________________________________________________
      Layer (type) Output Shape Param # Connected to 
      ==================================================================================================
      Input (InputLayer) [(None, 416, 416, 3) 0 
      __________________________________________________________________________________________________
      conv_1 (Conv2D) (None, 416, 416, 16) 448 Input[0][0] 
      __________________________________________________________________________________________________
      norm_1 (BatchNormalizationV1) (None, 416, 416, 16) 64 conv_1[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d (MaxPooling2D) (None, 208, 208, 16) 0 norm_1[0][0] 
      __________________________________________________________________________________________________
      conv_2 (Conv2D) (None, 208, 208, 32) 4640 max_pooling2d[0][0] 
      __________________________________________________________________________________________________
      norm_2 (BatchNormalizationV1) (None, 208, 208, 32) 128 conv_2[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d_1 (MaxPooling2D) (None, 104, 104, 32) 0 norm_2[0][0] 
      __________________________________________________________________________________________________
      conv_3 (Conv2D) (None, 104, 104, 64) 18496 max_pooling2d_1[0][0] 
      __________________________________________________________________________________________________
      norm_3 (BatchNormalizationV1) (None, 104, 104, 64) 256 conv_3[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d_2 (MaxPooling2D) (None, 52, 52, 64) 0 norm_3[0][0] 
      __________________________________________________________________________________________________
      conv_4 (Conv2D) (None, 52, 52, 128) 73856 max_pooling2d_2[0][0] 
      __________________________________________________________________________________________________
      norm_4 (BatchNormalizationV1) (None, 52, 52, 128) 512 conv_4[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d_3 (MaxPooling2D) (None, 26, 26, 128) 0 norm_4[0][0] 
      __________________________________________________________________________________________________
      conv_5 (Conv2D) (None, 26, 26, 256) 295168 max_pooling2d_3[0][0] 
      __________________________________________________________________________________________________
      norm_5 (BatchNormalizationV1) (None, 26, 26, 256) 1024 conv_5[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d_4 (MaxPooling2D) (None, 13, 13, 256) 0 norm_5[0][0] 
      __________________________________________________________________________________________________
      conv_6 (Conv2D) (None, 13, 13, 512) 1180160 max_pooling2d_4[0][0] 
      __________________________________________________________________________________________________
      norm_6 (BatchNormalizationV1) (None, 13, 13, 512) 2048 conv_6[0][0] 
      __________________________________________________________________________________________________
      max_pooling2d_5 (MaxPooling2D) (None, 13, 13, 512) 0 norm_6[0][0] 
      __________________________________________________________________________________________________
      conv_7 (Conv2D) (None, 13, 13, 1024) 4719616 max_pooling2d_5[0][0] 
      __________________________________________________________________________________________________
      norm_7 (BatchNormalizationV1) (None, 13, 13, 1024) 4096 conv_7[0][0] 
      __________________________________________________________________________________________________
      conv_8 (Conv2D) (None, 13, 13, 256) 262400 norm_7[0][0] 
      __________________________________________________________________________________________________
      norm_8 (BatchNormalizationV1) (None, 13, 13, 256) 1024 conv_8[0][0] 
      __________________________________________________________________________________________________
      conv_11 (Conv2D) (None, 13, 13, 128) 32896 norm_8[0][0] 
      __________________________________________________________________________________________________
      norm_10 (BatchNormalizationV1) (None, 13, 13, 128) 512 conv_11[0][0] 
      __________________________________________________________________________________________________
      lambda_1 (Lambda) (None, 26, 26, 128) 0 norm_10[0][0] 
      __________________________________________________________________________________________________
      concatenate (Concatenate) (None, 26, 26, 384) 0 lambda_1[0][0] 
       norm_5[0][0] 
      __________________________________________________________________________________________________
      conv_9 (Conv2D) (None, 13, 13, 512) 1180160 norm_8[0][0] 
      __________________________________________________________________________________________________
      conv_12 (Conv2D) (None, 26, 26, 256) 884992 concatenate[0][0] 
      __________________________________________________________________________________________________
      norm_9 (BatchNormalizationV1) (None, 13, 13, 512) 2048 conv_9[0][0] 
      __________________________________________________________________________________________________
      norm_11 (BatchNormalizationV1) (None, 26, 26, 256) 1024 conv_12[0][0] 
      __________________________________________________________________________________________________
      conv_10 (Conv2D) (None, 13, 13, 255) 130815 norm_9[0][0] 
      __________________________________________________________________________________________________
      conv_13 (Conv2D) (None, 26, 26, 255) 65535 norm_11[0][0] 
      __________________________________________________________________________________________________
      lambda (Lambda) (None, 507, 85) 0 conv_10[0][0] 
      __________________________________________________________________________________________________
      lambda_2 (Lambda) (None, 2028, 85) 0 conv_13[0][0] 
      __________________________________________________________________________________________________
      concatenate_1 (Concatenate) (None, 2535, 85) 0 lambda[0][0] 
       lambda_2[0][0] 
      ==================================================================================================
      Total params: 8,861,918
      Trainable params: 8,855,550
      Non-trainable params: 6,368
      __________________________________________________________________________________________________


      and has 8.861.918 weights. There are (8.861.918-8.858.735)=3183 parameters more then those contained in yolov3-tiny.weights. Had I make any error building the network or am I missing something?



      Thank you.






      python convnet yolo




      python convnet yolo






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Mar 28 at 8:42









      aleio1aleio1

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