yolo-tf
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parse_darknet_yolo2.py
INFO:tensorflow:yolo2_darknet/conv20/BatchNorm/moving_variance: [1024]=1024
<class 'numpy.int32'>
INFO:tensorflow:yolo2_darknet/conv20/weights: [3, 3, 3072, 1024]=28311552
<class 'numpy.int32'>
Traceback (most recent call last):
File "parse_darknet_yolo2.py", line 137, in
I print the value of(4*cnt). It actually equals to 113246208, and the type of cnt is int32. And I do not know how to solve this problem..... Could anyone tell me about it ? Thank you !
@ruiminshen not really follow the real model YOLOv2 architecture
Hi, @gengmujian10 did you check your python version? The author stated that he used python 3 so that could be the problem. I had the same issue in a similar case while unpacking some binary values and it was my wrong version of python. By the way, if you want to give it a try there is the same code but by @huseinzol05 in its github!
@huseinzol05 could you please state why do you think this is wrong? I m trying to implement YOLOv2, I already made the forward as you did but I cannot find a valid training part. Are you working on it? Thanks, Andrea
based on .cfg from darknet
[route] layers=-9
[convolutional] batch_normalize=1 size=1 stride=1 pad=1 filters=64 activation=leaky
from inference.py,
_net = reorg(passthrough)
supposed,
passthrough = slim.layers.conv2d(passthrough, 64)
_net = reorg(passthrough)
so is there a solution for this problem?
I think I encountered this problem and I believe I got around the problem by downloading and using the yolo-full.weights
file stored in the Google Drive link.