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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
asked Mar 28 at 8:42
aleio1aleio1
11
$endgroup$
add a comment |
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
asked Mar 28 at 8:42
aleio1aleio1
11
$endgroup$
add a comment |
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
asked Mar 28 at 8:42
aleio1aleio1
11
$endgroup$
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
asked Mar 28 at 8:42
aleio1aleio1
11
asked Mar 28 at 8:42
aleio1aleio1
11
asked Mar 28 at 8:42
aleio1aleio1
11
asked Mar 28 at 8:42
aleio1aleio1
11
asked Mar 28 at 8:42
aleio1aleio1
11
11
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