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mAP always zero #1108

Closed
DarylWM opened this issue Sep 22, 2016 · 25 comments
Closed

mAP always zero #1108

DarylWM opened this issue Sep 22, 2016 · 25 comments
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object-detection

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@DarylWM
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DarylWM commented Sep 22, 2016

I can't figure out why my model training mAP (val) doesn't get above zero. I'm trying to use the same approach and the SpaceNet_DetectNet_Train_Val.prototxt from this article.

My label files 000n.txt look like this:
p 0.0 0 0.0 0 0 24 118 0 0 0 0 0 0 0 0

My images are 1280x1280, and I'm using these custom classes:
dontcare,p

image

Where am I going wrong?
@lukeyeager
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If you're using your own dataset, you can't expect to match Jon's results exactly. Hopefully that's obvious.

With these settings the trained network began detecting buildings after just one epoch and we trained for a total of 200 epochs.

My first thought would be that you may just need to wait longer.

@DarylWM
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DarylWM commented Sep 22, 2016 •
edited

Thanks Luke. I trained it for 200 epochs and mAP, precision, and recall were 0 at the end. I'd expect it to nudge up above zero if it's learning anything. I suspect the label file or the custom class names, but I can't see what's wrong.

@lukeyeager
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Got it - I thought from your screenshot that you might just need to wait a little longer.

I'm having a similar issue trying to adapt DetectNet to another dataset right now - I train for several dozen epochs and never get non-zero precision or recall. I'll let you know if I find anything helpful.

@sherifshehata
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I had the same problem, and it was because the data augmentation transformation didn't suit my dataset. When i disabled them it worked.

@DarylWM
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DarylWM commented Sep 23, 2016

Thanks Sherif. How did you disable the transformation? I'm guessing I need to change something here.

@sherifshehata
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just set the " detectnet_augmentation_param" probabilities to zero, except the cropping

@DarylWM
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DarylWM commented Sep 24, 2016

OK, like this?

detectnet_augmentation_param {
crop_prob: 1.0
shift_x: 32
shift_y: 32
scale_prob: 0.0
scale_min: 0.8
scale_max: 1.2
flip_prob: 0.0
rotation_prob: 0.0
max_rotate_degree: 5.0
hue_rotation_prob: 0.0
hue_rotation: 30.0
desaturation_prob: 0.0
desaturation_max: 0.8
}

I'll let it train overnight. It's done 5 epochs so far and mAP remains 0.

@DarylWM
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DarylWM commented Sep 24, 2016

Should I enter anything in the "Python Layers" field or leave it blank when I create the model? In this article it says "DetectNet also uses the “Python Layers” interface to calculate and output a simplified mean Average Precision (mAP) score for the final set of output bounding boxes."

@gheinrich
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Should I enter anything in the "Python Layers" field or leave it blank when I create the model?

You can leave it blank because the caffe.layers.detectnet.mean_ap Python module is in the Caffe tree and therefore automatically added to the Python search path by DIGITS.

@DarylWM
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DarylWM commented Sep 24, 2016

Thanks @gheinrich.

The model has done nearly 185 epochs now. Interestingly, mAP shot up from 0 to 28.7 on the 121st epoch and has been slowly increasing since then. Thankfully it's learning.

I appreciate your tip @sherifshehata.

@DarylWM DarylWM closed this as completed Sep 24, 2016
@rtrahms rtrahms mentioned this issue Oct 16, 2016
Open
@Ali-Maher Ali-Maher mentioned this issue Nov 17, 2016
Closed
@shreyasramesh
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Hi, can someone please help me understand why my model is not learning anything?

The loss curve seems to suggest that the model has learnt to be almost perfect within a single epoch of training, but the mAP curve seems to suggest otherwise.

I tried running the model for 140 epoch without any luck. As mentioned above, I have set the data augmentation probabilities to 0 except the crop_prob.

My data set also has the KITTI format mentioned above. What could I be doing wrong here?
capture

@sulthanashafi
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iam also in the similar situation i tried changing augmentation as mentioned but haven't worked .Please someone help solve it.i may attach the few lkstlines of log file here.I0413 06:08:56.441606 22206 net.cpp:159] Memory required for data: 5099532756
I0413 06:08:56.441609 22206 net.cpp:222] mAP does not need backward computation.
I0413 06:08:56.441613 22206 net.cpp:222] score does not need backward computation.
I0413 06:08:56.441617 22206 net.cpp:222] cluster_gt does not need backward computation.
I0413 06:08:56.441622 22206 net.cpp:222] cluster does not need backward computation.
I0413 06:08:56.441649 22206 net.cpp:220] coverage_loss needs backward computation.
I0413 06:08:56.441654 22206 net.cpp:220] bbox_loss needs backward computation.
I0413 06:08:56.441659 22206 net.cpp:220] bbox-obj-norm needs backward computation.
I0413 06:08:56.441663 22206 net.cpp:220] bbox-norm needs backward computation.
I0413 06:08:56.441668 22206 net.cpp:220] bbox_mask needs backward computation.
I0413 06:08:56.441673 22206 net.cpp:220] bboxes_bbox/regressor_0_split needs backward computation.
I0413 06:08:56.441678 22206 net.cpp:220] bbox/regressor needs backward computation.
I0413 06:08:56.441682 22206 net.cpp:220] coverage_coverage/sig_0_split needs backward computation.
I0413 06:08:56.441686 22206 net.cpp:220] coverage/sig needs backward computation.
I0413 06:08:56.441690 22206 net.cpp:220] cvg/classifier needs backward computation.
I0413 06:08:56.441695 22206 net.cpp:220] pool5/drop_s1_pool5/drop_s1_0_split needs backward computation.
I0413 06:08:56.441699 22206 net.cpp:220] pool5/drop_s1 needs backward computation.
I0413 06:08:56.441704 22206 net.cpp:220] inception_5b/output needs backward computation.
I0413 06:08:56.441709 22206 net.cpp:220] inception_5b/relu_pool_proj needs backward computation.
I0413 06:08:56.441712 22206 net.cpp:220] inception_5b/pool_proj needs backward computation.
I0413 06:08:56.441716 22206 net.cpp:220] inception_5b/pool needs backward computation.
I0413 06:08:56.441721 22206 net.cpp:220] inception_5b/relu_5x5 needs backward computation.
I0413 06:08:56.441725 22206 net.cpp:220] inception_5b/5x5 needs backward computation.
I0413 06:08:56.441730 22206 net.cpp:220] inception_5b/relu_5x5_reduce needs backward computation.
I0413 06:08:56.441733 22206 net.cpp:220] inception_5b/5x5_reduce needs backward computation.
I0413 06:08:56.441737 22206 net.cpp:220] inception_5b/relu_3x3 needs backward computation.
I0413 06:08:56.441741 22206 net.cpp:220] inception_5b/3x3 needs backward computation.
I0413 06:08:56.441745 22206 net.cpp:220] inception_5b/relu_3x3_reduce needs backward computation.
I0413 06:08:56.441750 22206 net.cpp:220] inception_5b/3x3_reduce needs backward computation.
I0413 06:08:56.441753 22206 net.cpp:220] inception_5b/relu_1x1 needs backward computation.
I0413 06:08:56.441756 22206 net.cpp:220] inception_5b/1x1 needs backward computation.
I0413 06:08:56.441761 22206 net.cpp:220] inception_5a/output_inception_5a/output_0_split needs backward computation.
I0413 06:08:56.441766 22206 net.cpp:220] inception_5a/output needs backward computation.
I0413 06:08:56.441771 22206 net.cpp:220] inception_5a/relu_pool_proj needs backward computation.
I0413 06:08:56.441774 22206 net.cpp:220] inception_5a/pool_proj needs backward computation.
I0413 06:08:56.441778 22206 net.cpp:220] inception_5a/pool needs backward computation.
I0413 06:08:56.441782 22206 net.cpp:220] inception_5a/relu_5x5 needs backward computation.
I0413 06:08:56.441787 22206 net.cpp:220] inception_5a/5x5 needs backward computation.
I0413 06:08:56.441790 22206 net.cpp:220] inception_5a/relu_5x5_reduce needs backward computation.
I0413 06:08:56.441794 22206 net.cpp:220] inception_5a/5x5_reduce needs backward computation.
I0413 06:08:56.441798 22206 net.cpp:220] inception_5a/relu_3x3 needs backward computation.
I0413 06:08:56.441802 22206 net.cpp:220] inception_5a/3x3 needs backward computation.
I0413 06:08:56.441805 22206 net.cpp:220] inception_5a/relu_3x3_reduce needs backward computation.
I0413 06:08:56.441809 22206 net.cpp:220] inception_5a/3x3_reduce needs backward computation.
I0413 06:08:56.441813 22206 net.cpp:220] inception_5a/relu_1x1 needs backward computation.
I0413 06:08:56.441817 22206 net.cpp:220] inception_5a/1x1 needs backward computation.
I0413 06:08:56.441820 22206 net.cpp:220] inception_4e/output_inception_4e/output_0_split needs backward computation.
I0413 06:08:56.441825 22206 net.cpp:220] inception_4e/output needs backward computation.
I0413 06:08:56.441830 22206 net.cpp:220] inception_4e/relu_pool_proj needs backward computation.
I0413 06:08:56.441834 22206 net.cpp:220] inception_4e/pool_proj needs backward computation.
I0413 06:08:56.441844 22206 net.cpp:220] inception_4e/pool needs backward computation.
I0413 06:08:56.441848 22206 net.cpp:220] inception_4e/relu_5x5 needs backward computation.
I0413 06:08:56.441853 22206 net.cpp:220] inception_4e/5x5 needs backward computation.
I0413 06:08:56.441856 22206 net.cpp:220] inception_4e/relu_5x5_reduce needs backward computation.
I0413 06:08:56.441860 22206 net.cpp:220] inception_4e/5x5_reduce needs backward computation.
I0413 06:08:56.441864 22206 net.cpp:220] inception_4e/relu_3x3 needs backward computation.
I0413 06:08:56.441867 22206 net.cpp:220] inception_4e/3x3 needs backward computation.
I0413 06:08:56.441871 22206 net.cpp:220] inception_4e/relu_3x3_reduce needs backward computation.
I0413 06:08:56.441875 22206 net.cpp:220] inception_4e/3x3_reduce needs backward computation.
I0413 06:08:56.441879 22206 net.cpp:220] inception_4e/relu_1x1 needs backward computation.
I0413 06:08:56.441884 22206 net.cpp:220] inception_4e/1x1 needs backward computation.
I0413 06:08:56.441887 22206 net.cpp:220] inception_4d/output_inception_4d/output_0_split needs backward computation.
I0413 06:08:56.441891 22206 net.cpp:220] inception_4d/output needs backward computation.
I0413 06:08:56.441896 22206 net.cpp:220] inception_4d/relu_pool_proj needs backward computation.
I0413 06:08:56.441900 22206 net.cpp:220] inception_4d/pool_proj needs backward computation.
I0413 06:08:56.441905 22206 net.cpp:220] inception_4d/pool needs backward computation.
I0413 06:08:56.441908 22206 net.cpp:220] inception_4d/relu_5x5 needs backward computation.
I0413 06:08:56.441912 22206 net.cpp:220] inception_4d/5x5 needs backward computation.
I0413 06:08:56.441916 22206 net.cpp:220] inception_4d/relu_5x5_reduce needs backward computation.
I0413 06:08:56.441920 22206 net.cpp:220] inception_4d/5x5_reduce needs backward computation.
I0413 06:08:56.441925 22206 net.cpp:220] inception_4d/relu_3x3 needs backward computation.
I0413 06:08:56.441927 22206 net.cpp:220] inception_4d/3x3 needs backward computation.
I0413 06:08:56.441931 22206 net.cpp:220] inception_4d/relu_3x3_reduce needs backward computation.
I0413 06:08:56.441934 22206 net.cpp:220] inception_4d/3x3_reduce needs backward computation.
I0413 06:08:56.441938 22206 net.cpp:220] inception_4d/relu_1x1 needs backward computation.
I0413 06:08:56.441942 22206 net.cpp:220] inception_4d/1x1 needs backward computation.
I0413 06:08:56.441946 22206 net.cpp:220] inception_4c/output_inception_4c/output_0_split needs backward computation.
I0413 06:08:56.441951 22206 net.cpp:220] inception_4c/output needs backward computation.
I0413 06:08:56.441956 22206 net.cpp:220] inception_4c/relu_pool_proj needs backward computation.
I0413 06:08:56.441958 22206 net.cpp:220] inception_4c/pool_proj needs backward computation.
I0413 06:08:56.441962 22206 net.cpp:220] inception_4c/pool needs backward computation.
I0413 06:08:56.441967 22206 net.cpp:220] inception_4c/relu_5x5 needs backward computation.
I0413 06:08:56.441969 22206 net.cpp:220] inception_4c/5x5 needs backward computation.
I0413 06:08:56.441973 22206 net.cpp:220] inception_4c/relu_5x5_reduce needs backward computation.
I0413 06:08:56.441977 22206 net.cpp:220] inception_4c/5x5_reduce needs backward computation.
I0413 06:08:56.441982 22206 net.cpp:220] inception_4c/relu_3x3 needs backward computation.
I0413 06:08:56.441985 22206 net.cpp:220] inception_4c/3x3 needs backward computation.
I0413 06:08:56.441988 22206 net.cpp:220] inception_4c/relu_3x3_reduce needs backward computation.
I0413 06:08:56.441992 22206 net.cpp:220] inception_4c/3x3_reduce needs backward computation.
I0413 06:08:56.441997 22206 net.cpp:220] inception_4c/relu_1x1 needs backward computation.
I0413 06:08:56.441999 22206 net.cpp:220] inception_4c/1x1 needs backward computation.
I0413 06:08:56.442003 22206 net.cpp:220] inception_4b/output_inception_4b/output_0_split needs backward computation.
I0413 06:08:56.442008 22206 net.cpp:220] inception_4b/output needs backward computation.
I0413 06:08:56.442013 22206 net.cpp:220] inception_4b/relu_pool_proj needs backward computation.
I0413 06:08:56.442020 22206 net.cpp:220] inception_4b/pool_proj needs backward computation.
I0413 06:08:56.442024 22206 net.cpp:220] inception_4b/pool needs backward computation.
I0413 06:08:56.442028 22206 net.cpp:220] inception_4b/relu_5x5 needs backward computation.
I0413 06:08:56.442032 22206 net.cpp:220] inception_4b/5x5 needs backward computation.
I0413 06:08:56.442036 22206 net.cpp:220] inception_4b/relu_5x5_reduce needs backward computation.
I0413 06:08:56.442039 22206 net.cpp:220] inception_4b/5x5_reduce needs backward computation.
I0413 06:08:56.442044 22206 net.cpp:220] inception_4b/relu_3x3 needs backward computation.
I0413 06:08:56.442047 22206 net.cpp:220] inception_4b/3x3 needs backward computation.
I0413 06:08:56.442050 22206 net.cpp:220] inception_4b/relu_3x3_reduce needs backward computation.
I0413 06:08:56.442054 22206 net.cpp:220] inception_4b/3x3_reduce needs backward computation.
I0413 06:08:56.442059 22206 net.cpp:220] inception_4b/relu_1x1 needs backward computation.
I0413 06:08:56.442062 22206 net.cpp:220] inception_4b/1x1 needs backward computation.
I0413 06:08:56.442066 22206 net.cpp:220] inception_4a/output_inception_4a/output_0_split needs backward computation.
I0413 06:08:56.442070 22206 net.cpp:220] inception_4a/output needs backward computation.
I0413 06:08:56.442075 22206 net.cpp:220] inception_4a/relu_pool_proj needs backward computation.
I0413 06:08:56.442078 22206 net.cpp:220] inception_4a/pool_proj needs backward computation.
I0413 06:08:56.442082 22206 net.cpp:220] inception_4a/pool needs backward computation.
I0413 06:08:56.442086 22206 net.cpp:220] inception_4a/relu_5x5 needs backward computation.
I0413 06:08:56.442090 22206 net.cpp:220] inception_4a/5x5 needs backward computation.
I0413 06:08:56.442095 22206 net.cpp:220] inception_4a/relu_5x5_reduce needs backward computation.
I0413 06:08:56.442097 22206 net.cpp:220] inception_4a/5x5_reduce needs backward computation.
I0413 06:08:56.442101 22206 net.cpp:220] inception_4a/relu_3x3 needs backward computation.
I0413 06:08:56.442106 22206 net.cpp:220] inception_4a/3x3 needs backward computation.
I0413 06:08:56.442109 22206 net.cpp:220] inception_4a/relu_3x3_reduce needs backward computation.
I0413 06:08:56.442112 22206 net.cpp:220] inception_4a/3x3_reduce needs backward computation.
I0413 06:08:56.442116 22206 net.cpp:220] inception_4a/relu_1x1 needs backward computation.
I0413 06:08:56.442121 22206 net.cpp:220] inception_4a/1x1 needs backward computation.
I0413 06:08:56.442124 22206 net.cpp:220] pool3/3x3_s2_pool3/3x3_s2_0_split needs backward computation.
I0413 06:08:56.442128 22206 net.cpp:220] pool3/3x3_s2 needs backward computation.
I0413 06:08:56.442132 22206 net.cpp:220] inception_3b/output needs backward computation.
I0413 06:08:56.442138 22206 net.cpp:220] inception_3b/relu_pool_proj needs backward computation.
I0413 06:08:56.442142 22206 net.cpp:220] inception_3b/pool_proj needs backward computation.
I0413 06:08:56.442147 22206 net.cpp:220] inception_3b/pool needs backward computation.
I0413 06:08:56.442150 22206 net.cpp:220] inception_3b/relu_5x5 needs backward computation.
I0413 06:08:56.442154 22206 net.cpp:220] inception_3b/5x5 needs backward computation.
I0413 06:08:56.442157 22206 net.cpp:220] inception_3b/relu_5x5_reduce needs backward computation.
I0413 06:08:56.442162 22206 net.cpp:220] inception_3b/5x5_reduce needs backward computation.
I0413 06:08:56.442165 22206 net.cpp:220] inception_3b/relu_3x3 needs backward computation.
I0413 06:08:56.442169 22206 net.cpp:220] inception_3b/3x3 needs backward computation.
I0413 06:08:56.442173 22206 net.cpp:220] inception_3b/relu_3x3_reduce needs backward computation.
I0413 06:08:56.442178 22206 net.cpp:220] inception_3b/3x3_reduce needs backward computation.
I0413 06:08:56.442181 22206 net.cpp:220] inception_3b/relu_1x1 needs backward computation.
I0413 06:08:56.442185 22206 net.cpp:220] inception_3b/1x1 needs backward computation.
I0413 06:08:56.442189 22206 net.cpp:220] inception_3a/output_inception_3a/output_0_split needs backward computation.
I0413 06:08:56.442199 22206 net.cpp:220] inception_3a/output needs backward computation.
I0413 06:08:56.442204 22206 net.cpp:220] inception_3a/relu_pool_proj needs backward computation.
I0413 06:08:56.442209 22206 net.cpp:220] inception_3a/pool_proj needs backward computation.
I0413 06:08:56.442212 22206 net.cpp:220] inception_3a/pool needs backward computation.
I0413 06:08:56.442216 22206 net.cpp:220] inception_3a/relu_5x5 needs backward computation.
I0413 06:08:56.442220 22206 net.cpp:220] inception_3a/5x5 needs backward computation.
I0413 06:08:56.442224 22206 net.cpp:220] inception_3a/relu_5x5_reduce needs backward computation.
I0413 06:08:56.442229 22206 net.cpp:220] inception_3a/5x5_reduce needs backward computation.
I0413 06:08:56.442232 22206 net.cpp:220] inception_3a/relu_3x3 needs backward computation.
I0413 06:08:56.442235 22206 net.cpp:220] inception_3a/3x3 needs backward computation.
I0413 06:08:56.442239 22206 net.cpp:220] inception_3a/relu_3x3_reduce needs backward computation.
I0413 06:08:56.442243 22206 net.cpp:220] inception_3a/3x3_reduce needs backward computation.
I0413 06:08:56.442247 22206 net.cpp:220] inception_3a/relu_1x1 needs backward computation.
I0413 06:08:56.442251 22206 net.cpp:220] inception_3a/1x1 needs backward computation.
I0413 06:08:56.442255 22206 net.cpp:220] pool2/3x3_s2_pool2/3x3_s2_0_split needs backward computation.
I0413 06:08:56.442260 22206 net.cpp:220] pool2/3x3_s2 needs backward computation.
I0413 06:08:56.442265 22206 net.cpp:220] conv2/norm2 needs backward computation.
I0413 06:08:56.442268 22206 net.cpp:220] conv2/relu_3x3 needs backward computation.
I0413 06:08:56.442272 22206 net.cpp:220] conv2/3x3 needs backward computation.
I0413 06:08:56.442276 22206 net.cpp:220] conv2/relu_3x3_reduce needs backward computation.
I0413 06:08:56.442281 22206 net.cpp:220] conv2/3x3_reduce needs backward computation.
I0413 06:08:56.442284 22206 net.cpp:220] pool1/norm1 needs backward computation.
I0413 06:08:56.442289 22206 net.cpp:220] pool1/3x3_s2 needs backward computation.
I0413 06:08:56.442293 22206 net.cpp:220] conv1/relu_7x7 needs backward computation.
I0413 06:08:56.442297 22206 net.cpp:220] conv1/7x7_s2 needs backward computation.
I0413 06:08:56.442301 22206 net.cpp:222] bb-obj-norm does not need backward computation.
I0413 06:08:56.442307 22206 net.cpp:222] bb-label-norm does not need backward computation.
I0413 06:08:56.442313 22206 net.cpp:222] obj-block_obj-block_0_split does not need backward computation.
I0413 06:08:56.442318 22206 net.cpp:222] obj-block does not need backward computation.
I0413 06:08:56.442325 22206 net.cpp:222] size-block_size-block_0_split does not need backward computation.
I0413 06:08:56.442329 22206 net.cpp:222] size-block does not need backward computation.
I0413 06:08:56.442335 22206 net.cpp:222] coverage-block does not need backward computation.
I0413 06:08:56.442342 22206 net.cpp:222] coverage-label_slice-label_4_split does not need backward computation.
I0413 06:08:56.442348 22206 net.cpp:222] obj-label_slice-label_3_split does not need backward computation.
I0413 06:08:56.442353 22206 net.cpp:222] size-label_slice-label_2_split does not need backward computation.
I0413 06:08:56.442358 22206 net.cpp:222] bbox-label_slice-label_1_split does not need backward computation.
I0413 06:08:56.442364 22206 net.cpp:222] foreground-label_slice-label_0_split does not need backward computation.
I0413 06:08:56.442370 22206 net.cpp:222] slice-label does not need backward computation.
I0413 06:08:56.442375 22206 net.cpp:222] val_transform does not need backward computation.
I0413 06:08:56.442380 22206 net.cpp:222] val_label does not need backward computation.
I0413 06:08:56.442384 22206 net.cpp:222] val_data does not need backward computation.
I0413 06:08:56.442387 22206 net.cpp:264] This network produces output loss_bbox
I0413 06:08:56.442391 22206 net.cpp:264] This network produces output loss_coverage
I0413 06:08:56.442395 22206 net.cpp:264] This network produces output mAP
I0413 06:08:56.442399 22206 net.cpp:264] This network produces output precision
I0413 06:08:56.442409 22206 net.cpp:264] This network produces output recall
I0413 06:08:56.442546 22206 net.cpp:284] Network initialization done.
I0413 06:08:56.443408 22206 solver.cpp:60] Solver scaffolding done.
I0413 06:08:56.448447 22206 caffe.cpp:231] Starting Optimization
I0413 06:08:56.448458 22206 solver.cpp:304] Solving
I0413 06:08:56.448462 22206 solver.cpp:305] Learning Rate Policy: step
I0413 06:08:56.454710 22206 solver.cpp:362] Iteration 0, Testing net (#0)
I0413 06:08:56.454725 22206 net.cpp:723] Ignoring source layer train_data
I0413 06:08:56.454730 22206 net.cpp:723] Ignoring source layer train_label
I0413 06:08:56.454733 22206 net.cpp:723] Ignoring source layer train_transform
I0413 06:09:23.007010 22206 solver.cpp:429] Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:09:23.007134 22206 solver.cpp:429] Test net output #1: loss_coverage = 305.735 (* 1 = 305.735 loss)
I0413 06:09:23.007150 22206 solver.cpp:429] Test net output #2: mAP = 0
I0413 06:09:23.007155 22206 solver.cpp:429] Test net output #3: precision = 0
I0413 06:09:23.007159 22206 solver.cpp:429] Test net output #4: recall = 0
I0413 06:09:40.952916 22206 solver.cpp:242] Iteration 0 (0 iter/s, 44.5051s/40 iter), loss = 317.739
I0413 06:09:40.952960 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:09:40.952968 22206 solver.cpp:261] Train net output #1: loss_coverage = 318.719 (* 1 = 318.719 loss)
I0413 06:09:40.952993 22206 sgd_solver.cpp:106] Iteration 0, lr = 0.001
I0413 06:12:09.236304 22206 solver.cpp:242] Iteration 40 (0.26975 iter/s, 148.286s/40 iter), loss = -6.43825e-20
I0413 06:12:09.236418 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:12:09.236426 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:12:09.236436 22206 sgd_solver.cpp:106] Iteration 40, lr = 0.001
I0413 06:14:37.587092 22206 solver.cpp:242] Iteration 80 (0.269627 iter/s, 148.353s/40 iter), loss = -6.43825e-20
I0413 06:14:37.587157 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:14:37.587165 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:14:37.587175 22206 sgd_solver.cpp:106] Iteration 80, lr = 0.001
I0413 06:17:05.949126 22206 solver.cpp:242] Iteration 120 (0.269607 iter/s, 148.364s/40 iter), loss = -6.43825e-20
I0413 06:17:05.949256 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:17:05.949266 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:17:05.949278 22206 sgd_solver.cpp:106] Iteration 120, lr = 0.001
I0413 06:19:34.343139 22206 solver.cpp:242] Iteration 160 (0.269549 iter/s, 148.396s/40 iter), loss = -6.43825e-20
I0413 06:19:34.343253 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:19:34.343263 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:19:34.343273 22206 sgd_solver.cpp:106] Iteration 160, lr = 0.001
I0413 06:22:02.687221 22206 solver.cpp:242] Iteration 200 (0.269639 iter/s, 148.346s/40 iter), loss = -6.43825e-20
I0413 06:22:02.687297 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:22:02.687307 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:22:02.687319 22206 sgd_solver.cpp:106] Iteration 200, lr = 0.001
I0413 06:24:31.148965 22206 solver.cpp:242] Iteration 240 (0.269426 iter/s, 148.464s/40 iter), loss = -6.43825e-20
I0413 06:24:31.149034 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:24:31.149042 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:24:31.149052 22206 sgd_solver.cpp:106] Iteration 240, lr = 0.001
I0413 06:26:59.616809 22206 solver.cpp:242] Iteration 280 (0.269415 iter/s, 148.47s/40 iter), loss = -6.43825e-20
I0413 06:26:59.616961 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:26:59.616971 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:26:59.616981 22206 sgd_solver.cpp:106] Iteration 280, lr = 0.001
I0413 06:29:27.940598 22206 solver.cpp:242] Iteration 320 (0.269676 iter/s, 148.326s/40 iter), loss = -6.43825e-20
I0413 06:29:27.940678 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:29:27.940688 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:29:27.940698 22206 sgd_solver.cpp:106] Iteration 320, lr = 0.001
I0413 06:29:31.628262 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_322.caffemodel
I0413 06:29:31.759322 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_322.solverstate
I0413 06:31:57.534430 22206 solver.cpp:242] Iteration 360 (0.267387 iter/s, 149.596s/40 iter), loss = -6.43825e-20
I0413 06:31:57.534548 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:31:57.534557 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:31:57.534569 22206 sgd_solver.cpp:106] Iteration 360, lr = 0.001
I0413 06:34:26.713977 22206 solver.cpp:242] Iteration 400 (0.268129 iter/s, 149.182s/40 iter), loss = -6.43825e-20
I0413 06:34:26.714052 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:34:26.714061 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:34:26.714072 22206 sgd_solver.cpp:106] Iteration 400, lr = 0.001
I0413 06:36:56.036567 22206 solver.cpp:242] Iteration 440 (0.267872 iter/s, 149.325s/40 iter), loss = -6.43825e-20
I0413 06:36:56.036634 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:36:56.036643 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:36:56.036653 22206 sgd_solver.cpp:106] Iteration 440, lr = 0.001
I0413 06:39:25.477144 22206 solver.cpp:242] Iteration 480 (0.267661 iter/s, 149.443s/40 iter), loss = -6.43825e-20
I0413 06:39:25.477215 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:39:25.477223 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:39:25.477234 22206 sgd_solver.cpp:106] Iteration 480, lr = 0.001
I0413 06:41:54.831908 22206 solver.cpp:242] Iteration 520 (0.267815 iter/s, 149.357s/40 iter), loss = -6.43825e-20
I0413 06:41:54.831998 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:41:54.832008 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:41:54.832020 22206 sgd_solver.cpp:106] Iteration 520, lr = 0.001
I0413 06:44:24.388000 22206 solver.cpp:242] Iteration 560 (0.267454 iter/s, 149.558s/40 iter), loss = -6.43825e-20
I0413 06:44:24.388065 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:44:24.388074 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:44:24.388085 22206 sgd_solver.cpp:106] Iteration 560, lr = 0.001
I0413 06:46:53.393358 22206 solver.cpp:242] Iteration 600 (0.268443 iter/s, 149.008s/40 iter), loss = -6.43825e-20
I0413 06:46:53.393478 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:46:53.393488 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:46:53.393498 22206 sgd_solver.cpp:106] Iteration 600, lr = 0.001
I0413 06:49:22.734746 22206 solver.cpp:242] Iteration 640 (0.267839 iter/s, 149.344s/40 iter), loss = -6.43825e-20
I0413 06:49:22.734871 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:49:22.734881 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:49:22.734892 22206 sgd_solver.cpp:106] Iteration 640, lr = 0.001
I0413 06:49:33.901051 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_644.caffemodel
I0413 06:49:33.993005 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_644.solverstate
I0413 06:51:52.247838 22206 solver.cpp:242] Iteration 680 (0.267531 iter/s, 149.515s/40 iter), loss = -6.43825e-20
I0413 06:51:52.248011 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:51:52.248023 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:51:52.248035 22206 sgd_solver.cpp:106] Iteration 680, lr = 0.001
I0413 06:54:21.442358 22206 solver.cpp:242] Iteration 720 (0.268103 iter/s, 149.197s/40 iter), loss = -6.43825e-20
I0413 06:54:21.442461 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:54:21.442471 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:54:21.442482 22206 sgd_solver.cpp:106] Iteration 720, lr = 0.001
I0413 06:56:50.671871 22206 solver.cpp:242] Iteration 760 (0.26804 iter/s, 149.232s/40 iter), loss = -6.43825e-20
I0413 06:56:50.671988 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:56:50.671998 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:56:50.672009 22206 sgd_solver.cpp:106] Iteration 760, lr = 0.001
I0413 06:59:19.819095 22206 solver.cpp:242] Iteration 800 (0.268188 iter/s, 149.149s/40 iter), loss = -6.43825e-20
I0413 06:59:19.819205 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 06:59:19.819213 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 06:59:19.819224 22206 sgd_solver.cpp:106] Iteration 800, lr = 0.001
I0413 07:01:49.744330 22206 solver.cpp:242] Iteration 840 (0.266796 iter/s, 149.927s/40 iter), loss = -6.43825e-20
I0413 07:01:49.744400 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:01:49.744408 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:01:49.744420 22206 sgd_solver.cpp:106] Iteration 840, lr = 0.001
I0413 07:04:18.976639 22206 solver.cpp:242] Iteration 880 (0.268035 iter/s, 149.234s/40 iter), loss = -6.43825e-20
I0413 07:04:18.976755 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:04:18.976765 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:04:18.976776 22206 sgd_solver.cpp:106] Iteration 880, lr = 0.001
I0413 07:06:48.090966 22206 solver.cpp:242] Iteration 920 (0.268247 iter/s, 149.116s/40 iter), loss = -6.43825e-20
I0413 07:06:48.091079 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:06:48.091089 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:06:48.091099 22206 sgd_solver.cpp:106] Iteration 920, lr = 0.001
I0413 07:09:17.116433 22206 solver.cpp:242] Iteration 960 (0.268407 iter/s, 149.028s/40 iter), loss = -6.43825e-20
I0413 07:09:17.116539 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:09:17.116549 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:09:17.116559 22206 sgd_solver.cpp:106] Iteration 960, lr = 0.001
I0413 07:09:35.753537 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_966.caffemodel
I0413 07:09:35.846139 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_966.solverstate
I0413 07:11:46.418128 22206 solver.cpp:242] Iteration 1000 (0.26791 iter/s, 149.304s/40 iter), loss = -6.43825e-20
I0413 07:11:46.418980 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:11:46.418990 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:11:46.419003 22206 sgd_solver.cpp:106] Iteration 1000, lr = 0.001
I0413 07:14:15.607887 22206 solver.cpp:242] Iteration 1040 (0.268112 iter/s, 149.191s/40 iter), loss = -6.43825e-20
I0413 07:14:15.608037 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:14:15.608047 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:14:15.608058 22206 sgd_solver.cpp:106] Iteration 1040, lr = 0.001
I0413 07:16:44.633111 22206 solver.cpp:242] Iteration 1080 (0.268407 iter/s, 149.027s/40 iter), loss = -6.43825e-20
I0413 07:16:44.633185 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:16:44.633195 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:16:44.633205 22206 sgd_solver.cpp:106] Iteration 1080, lr = 0.001
I0413 07:19:13.658380 22206 solver.cpp:242] Iteration 1120 (0.268407 iter/s, 149.027s/40 iter), loss = -6.43825e-20
I0413 07:19:13.658449 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:19:13.658458 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:19:13.658468 22206 sgd_solver.cpp:106] Iteration 1120, lr = 0.001
I0413 07:21:42.794762 22206 solver.cpp:242] Iteration 1160 (0.268207 iter/s, 149.139s/40 iter), loss = -6.43825e-20
I0413 07:21:42.794872 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:21:42.794881 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:21:42.794893 22206 sgd_solver.cpp:106] Iteration 1160, lr = 0.001
I0413 07:24:12.001484 22206 solver.cpp:242] Iteration 1200 (0.268081 iter/s, 149.209s/40 iter), loss = -6.43825e-20
I0413 07:24:12.001596 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:24:12.001606 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:24:12.001617 22206 sgd_solver.cpp:106] Iteration 1200, lr = 0.001
I0413 07:26:41.062177 22206 solver.cpp:242] Iteration 1240 (0.268343 iter/s, 149.063s/40 iter), loss = -6.43825e-20
I0413 07:26:41.062252 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:26:41.062261 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:26:41.062273 22206 sgd_solver.cpp:106] Iteration 1240, lr = 0.001
I0413 07:29:10.076431 22206 solver.cpp:242] Iteration 1280 (0.268427 iter/s, 149.016s/40 iter), loss = -6.43825e-20
I0413 07:29:10.076552 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:29:10.076562 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:29:10.076573 22206 sgd_solver.cpp:106] Iteration 1280, lr = 0.001
I0413 07:29:36.118050 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_1288.caffemodel
I0413 07:29:36.210352 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1288.solverstate
I0413 07:31:39.303977 22206 solver.cpp:242] Iteration 1320 (0.268043 iter/s, 149.23s/40 iter), loss = -6.43825e-20
I0413 07:31:39.304044 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:31:39.304054 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:31:39.304064 22206 sgd_solver.cpp:106] Iteration 1320, lr = 0.001
I0413 07:34:10.779850 22206 solver.cpp:242] Iteration 1360 (0.264065 iter/s, 151.478s/40 iter), loss = -6.43825e-20
I0413 07:34:10.779914 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:34:10.779924 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:34:10.779934 22206 sgd_solver.cpp:106] Iteration 1360, lr = 0.001
I0413 07:36:40.017544 22206 solver.cpp:242] Iteration 1400 (0.268025 iter/s, 149.24s/40 iter), loss = -6.43825e-20
I0413 07:36:40.017660 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:36:40.017670 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:36:40.017683 22206 sgd_solver.cpp:106] Iteration 1400, lr = 0.001
I0413 07:39:09.247287 22206 solver.cpp:242] Iteration 1440 (0.268039 iter/s, 149.232s/40 iter), loss = -6.43825e-20
I0413 07:39:09.247429 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:39:09.247440 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:39:09.247452 22206 sgd_solver.cpp:106] Iteration 1440, lr = 0.001
I0413 07:41:38.391238 22206 solver.cpp:242] Iteration 1480 (0.268194 iter/s, 149.146s/40 iter), loss = -6.43825e-20
I0413 07:41:38.391366 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:41:38.391376 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:41:38.391387 22206 sgd_solver.cpp:106] Iteration 1480, lr = 0.001
I0413 07:44:07.598134 22206 solver.cpp:242] Iteration 1520 (0.26808 iter/s, 149.209s/40 iter), loss = -6.43825e-20
I0413 07:44:07.599515 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:44:07.599524 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:44:07.599535 22206 sgd_solver.cpp:106] Iteration 1520, lr = 0.001
I0413 07:46:36.945183 22206 solver.cpp:242] Iteration 1560 (0.267831 iter/s, 149.348s/40 iter), loss = -6.43825e-20
I0413 07:46:36.945298 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:46:36.945309 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:46:36.945320 22206 sgd_solver.cpp:106] Iteration 1560, lr = 0.001
I0413 07:49:06.170992 22206 solver.cpp:242] Iteration 1600 (0.268046 iter/s, 149.228s/40 iter), loss = -6.43825e-20
I0413 07:49:06.171119 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:49:06.171129 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:49:06.171140 22206 sgd_solver.cpp:106] Iteration 1600, lr = 0.001
I0413 07:49:39.681165 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_1610.caffemodel
I0413 07:49:39.773512 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1610.solverstate
I0413 07:49:39.850608 22206 solver.cpp:362] Iteration 1610, Testing net (#0)
I0413 07:49:39.850631 22206 net.cpp:723] Ignoring source layer train_data
I0413 07:49:39.850636 22206 net.cpp:723] Ignoring source layer train_label
I0413 07:49:39.850641 22206 net.cpp:723] Ignoring source layer train_transform
I0413 07:49:58.498661 22206 solver.cpp:429] Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:49:58.498684 22206 solver.cpp:429] Test net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:49:58.498710 22206 solver.cpp:429] Test net output #2: mAP = 0
I0413 07:49:58.498716 22206 solver.cpp:429] Test net output #3: precision = 0
I0413 07:49:58.498721 22206 solver.cpp:429] Test net output #4: recall = 0
I0413 07:51:54.017457 22206 solver.cpp:242] Iteration 1640 (0.23831 iter/s, 167.849s/40 iter), loss = -6.43825e-20
I0413 07:51:54.017525 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:51:54.017534 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:51:54.017545 22206 sgd_solver.cpp:106] Iteration 1640, lr = 0.001
I0413 07:54:23.078280 22206 solver.cpp:242] Iteration 1680 (0.268343 iter/s, 149.063s/40 iter), loss = -6.43825e-20
I0413 07:54:23.078408 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:54:23.078419 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:54:23.078431 22206 sgd_solver.cpp:106] Iteration 1680, lr = 0.001
I0413 07:56:52.164952 22206 solver.cpp:242] Iteration 1720 (0.268297 iter/s, 149.089s/40 iter), loss = -6.43825e-20
I0413 07:56:52.165076 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:56:52.165087 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:56:52.165098 22206 sgd_solver.cpp:106] Iteration 1720, lr = 0.001
I0413 07:59:21.380599 22206 solver.cpp:242] Iteration 1760 (0.268065 iter/s, 149.218s/40 iter), loss = -6.43825e-20
I0413 07:59:21.380702 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 07:59:21.380712 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 07:59:21.380722 22206 sgd_solver.cpp:106] Iteration 1760, lr = 0.001
I0413 08:01:50.508353 22206 solver.cpp:242] Iteration 1800 (0.268223 iter/s, 149.13s/40 iter), loss = -6.43825e-20
I0413 08:01:50.508491 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:01:50.508500 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:01:50.508513 22206 sgd_solver.cpp:106] Iteration 1800, lr = 0.001
I0413 08:04:19.601210 22206 solver.cpp:242] Iteration 1840 (0.268285 iter/s, 149.095s/40 iter), loss = -6.43825e-20
I0413 08:04:19.601328 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:04:19.601339 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:04:19.601351 22206 sgd_solver.cpp:106] Iteration 1840, lr = 0.001
I0413 08:06:48.672987 22206 solver.cpp:242] Iteration 1880 (0.268323 iter/s, 149.074s/40 iter), loss = -6.43825e-20
I0413 08:06:48.673053 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:06:48.673063 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:06:48.673074 22206 sgd_solver.cpp:106] Iteration 1880, lr = 0.001
I0413 08:09:17.874629 22206 solver.cpp:242] Iteration 1920 (0.26809 iter/s, 149.204s/40 iter), loss = -6.43825e-20
I0413 08:09:17.874747 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:09:17.874758 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:09:17.874768 22206 sgd_solver.cpp:106] Iteration 1920, lr = 0.001
I0413 08:09:58.770267 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_1932.caffemodel
I0413 08:09:58.862397 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1932.solverstate
I0413 08:11:46.843391 22206 solver.cpp:242] Iteration 1960 (0.268509 iter/s, 148.971s/40 iter), loss = -6.43825e-20
I0413 08:11:46.843502 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:11:46.843513 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:11:46.843523 22206 sgd_solver.cpp:106] Iteration 1960, lr = 0.001
I0413 08:14:15.769006 22206 solver.cpp:242] Iteration 2000 (0.268587 iter/s, 148.928s/40 iter), loss = -6.43825e-20
I0413 08:14:15.769112 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:14:15.769122 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:14:15.769134 22206 sgd_solver.cpp:106] Iteration 2000, lr = 0.001
I0413 08:16:44.733536 22206 solver.cpp:242] Iteration 2040 (0.268516 iter/s, 148.967s/40 iter), loss = -6.43825e-20
I0413 08:16:44.733660 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:16:44.733671 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:16:44.733681 22206 sgd_solver.cpp:106] Iteration 2040, lr = 0.001
I0413 08:19:13.831907 22206 solver.cpp:242] Iteration 2080 (0.268275 iter/s, 149.1s/40 iter), loss = -6.43825e-20
I0413 08:19:13.832031 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:19:13.832041 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:19:13.832053 22206 sgd_solver.cpp:106] Iteration 2080, lr = 0.001
I0413 08:21:42.788041 22206 solver.cpp:242] Iteration 2120 (0.268532 iter/s, 148.958s/40 iter), loss = -6.43825e-20
I0413 08:21:42.788151 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:21:42.788161 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:21:42.788172 22206 sgd_solver.cpp:106] Iteration 2120, lr = 0.001
I0413 08:24:11.707514 22206 solver.cpp:242] Iteration 2160 (0.268598 iter/s, 148.922s/40 iter), loss = -6.43825e-20
I0413 08:24:11.707672 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:24:11.707684 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:24:11.707695 22206 sgd_solver.cpp:106] Iteration 2160, lr = 0.001
I0413 08:26:40.647996 22206 solver.cpp:242] Iteration 2200 (0.26856 iter/s, 148.943s/40 iter), loss = -6.43825e-20
I0413 08:26:40.648113 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:26:40.648124 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:26:40.648134 22206 sgd_solver.cpp:106] Iteration 2200, lr = 0.001
I0413 08:29:09.702431 22206 solver.cpp:242] Iteration 2240 (0.268355 iter/s, 149.057s/40 iter), loss = -6.43825e-20
I0413 08:29:09.702499 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:29:09.702508 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:29:09.702519 22206 sgd_solver.cpp:106] Iteration 2240, lr = 0.001
I0413 08:29:58.160796 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_2254.caffemodel
I0413 08:29:58.253464 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2254.solverstate
I0413 08:31:38.717820 22206 solver.cpp:242] Iteration 2280 (0.268425 iter/s, 149.018s/40 iter), loss = -6.43825e-20
I0413 08:31:38.717890 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:31:38.717898 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:31:38.717908 22206 sgd_solver.cpp:106] Iteration 2280, lr = 0.001
I0413 08:34:07.770282 22206 solver.cpp:242] Iteration 2320 (0.268358 iter/s, 149.055s/40 iter), loss = -6.43825e-20
I0413 08:34:07.770356 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:34:07.770365 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:34:07.770376 22206 sgd_solver.cpp:106] Iteration 2320, lr = 0.001
I0413 08:36:36.791759 22206 solver.cpp:242] Iteration 2360 (0.268414 iter/s, 149.024s/40 iter), loss = -6.43825e-20
I0413 08:36:36.791877 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:36:36.791887 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:36:36.791899 22206 sgd_solver.cpp:106] Iteration 2360, lr = 0.001
I0413 08:39:05.874894 22206 solver.cpp:242] Iteration 2400 (0.268303 iter/s, 149.085s/40 iter), loss = -6.43825e-20
I0413 08:39:05.875010 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:39:05.875021 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:39:05.875032 22206 sgd_solver.cpp:106] Iteration 2400, lr = 0.001
I0413 08:41:34.985160 22206 solver.cpp:242] Iteration 2440 (0.268254 iter/s, 149.112s/40 iter), loss = -6.43825e-20
I0413 08:41:34.985235 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:41:34.985244 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:41:34.985255 22206 sgd_solver.cpp:106] Iteration 2440, lr = 0.001
I0413 08:44:03.968014 22206 solver.cpp:242] Iteration 2480 (0.268483 iter/s, 148.985s/40 iter), loss = -6.43825e-20
I0413 08:44:03.968082 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:44:03.968092 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:44:03.968103 22206 sgd_solver.cpp:106] Iteration 2480, lr = 0.001
I0413 08:46:33.126567 22206 solver.cpp:242] Iteration 2520 (0.268167 iter/s, 149.161s/40 iter), loss = -6.43825e-20
I0413 08:46:33.126674 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:46:33.126684 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:46:33.126695 22206 sgd_solver.cpp:106] Iteration 2520, lr = 0.001
I0413 08:49:02.207464 22206 solver.cpp:242] Iteration 2560 (0.268307 iter/s, 149.083s/40 iter), loss = -6.43825e-20
I0413 08:49:02.207599 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:49:02.207610 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:49:02.207622 22206 sgd_solver.cpp:106] Iteration 2560, lr = 0.001
I0413 08:49:58.178736 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_2576.caffemodel
I0413 08:49:58.270462 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2576.solverstate
I0413 08:51:31.441325 22206 solver.cpp:242] Iteration 2600 (0.268032 iter/s, 149.236s/40 iter), loss = -6.43825e-20
I0413 08:51:31.441433 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:51:31.441444 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:51:31.441455 22206 sgd_solver.cpp:106] Iteration 2600, lr = 0.001
I0413 08:54:00.436144 22206 solver.cpp:242] Iteration 2640 (0.268462 iter/s, 148.997s/40 iter), loss = -6.43825e-20
I0413 08:54:00.436259 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:54:00.436269 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:54:00.436280 22206 sgd_solver.cpp:106] Iteration 2640, lr = 0.001
I0413 08:56:29.580874 22206 solver.cpp:242] Iteration 2680 (0.268192 iter/s, 149.147s/40 iter), loss = -6.43825e-20
I0413 08:56:29.580984 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:56:29.580994 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:56:29.581006 22206 sgd_solver.cpp:106] Iteration 2680, lr = 0.001
I0413 08:58:58.719501 22206 solver.cpp:242] Iteration 2720 (0.268203 iter/s, 149.141s/40 iter), loss = -6.43825e-20
I0413 08:58:58.719606 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 08:58:58.719616 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 08:58:58.719629 22206 sgd_solver.cpp:106] Iteration 2720, lr = 0.001
I0413 09:01:27.701104 22206 solver.cpp:242] Iteration 2760 (0.268486 iter/s, 148.984s/40 iter), loss = -6.43825e-20
I0413 09:01:27.701212 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:01:27.701221 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:01:27.701232 22206 sgd_solver.cpp:106] Iteration 2760, lr = 0.001
I0413 09:03:56.649909 22206 solver.cpp:242] Iteration 2800 (0.268545 iter/s, 148.951s/40 iter), loss = -6.43825e-20
I0413 09:03:56.650017 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:03:56.650027 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:03:56.650038 22206 sgd_solver.cpp:106] Iteration 2800, lr = 0.001
I0413 09:06:25.543249 22206 solver.cpp:242] Iteration 2840 (0.268645 iter/s, 148.895s/40 iter), loss = -6.43825e-20
I0413 09:06:25.543360 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:06:25.543370 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:06:25.543381 22206 sgd_solver.cpp:106] Iteration 2840, lr = 0.001
I0413 09:08:54.533068 22206 solver.cpp:242] Iteration 2880 (0.268471 iter/s, 148.992s/40 iter), loss = -6.43825e-20
I0413 09:08:54.533181 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:08:54.533191 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:08:54.533202 22206 sgd_solver.cpp:106] Iteration 2880, lr = 0.001
I0413 09:09:57.946005 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_2898.caffemodel
I0413 09:09:58.037713 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2898.solverstate
I0413 09:11:23.805656 22206 solver.cpp:242] Iteration 2920 (0.267962 iter/s, 149.275s/40 iter), loss = -6.43825e-20
I0413 09:11:23.805763 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:11:23.805773 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:11:23.805783 22206 sgd_solver.cpp:106] Iteration 2920, lr = 0.001
I0413 09:13:52.782021 22206 solver.cpp:242] Iteration 2960 (0.268495 iter/s, 148.978s/40 iter), loss = -6.43825e-20
I0413 09:13:52.782095 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:13:52.782104 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:13:52.782114 22206 sgd_solver.cpp:106] Iteration 2960, lr = 0.001
I0413 09:16:21.811465 22206 solver.cpp:242] Iteration 3000 (0.268399 iter/s, 149.032s/40 iter), loss = -6.43825e-20
I0413 09:16:21.811580 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:16:21.811590 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:16:21.811601 22206 sgd_solver.cpp:106] Iteration 3000, lr = 0.001
I0413 09:18:50.943544 22206 solver.cpp:242] Iteration 3040 (0.268215 iter/s, 149.134s/40 iter), loss = -6.43825e-20
I0413 09:18:50.943651 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:18:50.943662 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:18:50.943673 22206 sgd_solver.cpp:106] Iteration 3040, lr = 0.001
I0413 09:21:20.150465 22206 solver.cpp:242] Iteration 3080 (0.26808 iter/s, 149.209s/40 iter), loss = -6.43825e-20
I0413 09:21:20.150588 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:21:20.150599 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:21:20.150609 22206 sgd_solver.cpp:106] Iteration 3080, lr = 0.001
I0413 09:23:49.286792 22206 solver.cpp:242] Iteration 3120 (0.268207 iter/s, 149.138s/40 iter), loss = -6.43825e-20
I0413 09:23:49.286911 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:23:49.286921 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:23:49.286932 22206 sgd_solver.cpp:106] Iteration 3120, lr = 0.001
I0413 09:26:18.334234 22206 solver.cpp:242] Iteration 3160 (0.268367 iter/s, 149.05s/40 iter), loss = -6.43825e-20
I0413 09:26:18.334349 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:26:18.334359 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:26:18.334370 22206 sgd_solver.cpp:106] Iteration 3160, lr = 0.001
I0413 09:28:47.519130 22206 solver.cpp:242] Iteration 3200 (0.26812 iter/s, 149.187s/40 iter), loss = -6.43825e-20
I0413 09:28:47.519201 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:28:47.519210 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:28:47.519222 22206 sgd_solver.cpp:106] Iteration 3200, lr = 0.0001
I0413 09:29:58.412418 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_3220.caffemodel
I0413 09:29:58.505291 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3220.solverstate
I0413 09:29:58.578584 22206 solver.cpp:362] Iteration 3220, Testing net (#0)
I0413 09:29:58.578608 22206 net.cpp:723] Ignoring source layer train_data
I0413 09:29:58.578613 22206 net.cpp:723] Ignoring source layer train_label
I0413 09:29:58.578616 22206 net.cpp:723] Ignoring source layer train_transform
I0413 09:30:17.187198 22206 solver.cpp:429] Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:30:17.187222 22206 solver.cpp:429] Test net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:30:17.187227 22206 solver.cpp:429] Test net output #2: mAP = 0
I0413 09:30:17.187232 22206 solver.cpp:429] Test net output #3: precision = 0
I0413 09:30:17.187237 22206 solver.cpp:429] Test net output #4: recall = 0
I0413 09:31:35.329571 22206 solver.cpp:242] Iteration 3240 (0.238361 iter/s, 167.813s/40 iter), loss = -6.43825e-20
I0413 09:31:35.329721 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:31:35.329732 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:31:35.329742 22206 sgd_solver.cpp:106] Iteration 3240, lr = 0.0001
I0413 09:34:04.281879 22206 solver.cpp:242] Iteration 3280 (0.268539 iter/s, 148.954s/40 iter), loss = -6.43825e-20
I0413 09:34:04.281991 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:34:04.282001 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:34:04.282011 22206 sgd_solver.cpp:106] Iteration 3280, lr = 0.0001
I0413 09:36:33.375701 22206 solver.cpp:242] Iteration 3320 (0.268284 iter/s, 149.096s/40 iter), loss = -6.43825e-20
I0413 09:36:33.375814 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:36:33.375824 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:36:33.375834 22206 sgd_solver.cpp:106] Iteration 3320, lr = 0.0001
I0413 09:39:02.467183 22206 solver.cpp:242] Iteration 3360 (0.268288 iter/s, 149.094s/40 iter), loss = -6.43825e-20
I0413 09:39:02.467304 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:39:02.467314 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:39:02.467324 22206 sgd_solver.cpp:106] Iteration 3360, lr = 0.0001
I0413 09:41:31.563366 22206 solver.cpp:242] Iteration 3400 (0.268279 iter/s, 149.098s/40 iter), loss = -6.43825e-20
I0413 09:41:31.563433 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:41:31.563442 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:41:31.563453 22206 sgd_solver.cpp:106] Iteration 3400, lr = 0.0001
I0413 09:44:00.769177 22206 solver.cpp:242] Iteration 3440 (0.268082 iter/s, 149.208s/40 iter), loss = -6.43825e-20
I0413 09:44:00.769311 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:44:00.769321 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:44:00.769332 22206 sgd_solver.cpp:106] Iteration 3440, lr = 0.0001
I0413 09:46:30.002990 22206 solver.cpp:242] Iteration 3480 (0.268032 iter/s, 149.236s/40 iter), loss = -6.43825e-20
I0413 09:46:30.003110 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:46:30.003120 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:46:30.003131 22206 sgd_solver.cpp:106] Iteration 3480, lr = 0.0001
I0413 09:48:59.040910 22206 solver.cpp:242] Iteration 3520 (0.268384 iter/s, 149.04s/40 iter), loss = -6.43825e-20
I0413 09:48:59.041010 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:48:59.041020 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:48:59.041031 22206 sgd_solver.cpp:106] Iteration 3520, lr = 0.0001
I0413 09:50:17.398932 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_3542.caffemodel
I0413 09:50:17.490676 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3542.solverstate
I0413 09:51:28.575299 22206 solver.cpp:242] Iteration 3560 (0.267493 iter/s, 149.537s/40 iter), loss = -6.43825e-20
I0413 09:51:28.575412 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:51:28.575422 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:51:28.575431 22206 sgd_solver.cpp:106] Iteration 3560, lr = 0.0001
I0413 09:53:57.796625 22206 solver.cpp:242] Iteration 3600 (0.268054 iter/s, 149.223s/40 iter), loss = -6.43825e-20
I0413 09:53:57.796737 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:53:57.796746 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:53:57.796758 22206 sgd_solver.cpp:106] Iteration 3600, lr = 0.0001
I0413 09:56:27.163589 22206 solver.cpp:242] Iteration 3640 (0.267793 iter/s, 149.369s/40 iter), loss = -6.43825e-20
I0413 09:56:27.163729 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:56:27.163738 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:56:27.163750 22206 sgd_solver.cpp:106] Iteration 3640, lr = 0.0001
I0413 09:58:56.335173 22206 solver.cpp:242] Iteration 3680 (0.268144 iter/s, 149.174s/40 iter), loss = -6.43825e-20
I0413 09:58:56.335278 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 09:58:56.335286 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 09:58:56.335299 22206 sgd_solver.cpp:106] Iteration 3680, lr = 0.0001
I0413 10:01:25.507982 22206 solver.cpp:242] Iteration 3720 (0.268141 iter/s, 149.175s/40 iter), loss = -6.43825e-20
I0413 10:01:25.508046 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:01:25.508055 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:01:25.508065 22206 sgd_solver.cpp:106] Iteration 3720, lr = 0.0001
I0413 10:03:54.554807 22206 solver.cpp:242] Iteration 3760 (0.268368 iter/s, 149.049s/40 iter), loss = -6.43825e-20
I0413 10:03:54.554925 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:03:54.554935 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:03:54.554946 22206 sgd_solver.cpp:106] Iteration 3760, lr = 0.0001
I0413 10:06:23.596776 22206 solver.cpp:242] Iteration 3800 (0.268377 iter/s, 149.044s/40 iter), loss = -6.43825e-20
I0413 10:06:23.596891 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:06:23.596902 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:06:23.596912 22206 sgd_solver.cpp:106] Iteration 3800, lr = 0.0001
I0413 10:08:52.669639 22206 solver.cpp:242] Iteration 3840 (0.268321 iter/s, 149.075s/40 iter), loss = -6.43825e-20
I0413 10:08:52.669754 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:08:52.669764 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:08:52.669773 22206 sgd_solver.cpp:106] Iteration 3840, lr = 0.0001
I0413 10:10:18.405055 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_3864.caffemodel
I0413 10:10:18.496944 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3864.solverstate
I0413 10:11:21.959678 22206 solver.cpp:242] Iteration 3880 (0.267931 iter/s, 149.292s/40 iter), loss = -6.43825e-20
I0413 10:11:21.959784 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:11:21.959792 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:11:21.959803 22206 sgd_solver.cpp:106] Iteration 3880, lr = 0.0001
I0413 10:13:51.061364 22206 solver.cpp:242] Iteration 3920 (0.268269 iter/s, 149.104s/40 iter), loss = -6.43825e-20
I0413 10:13:51.061491 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:13:51.061502 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:13:51.061514 22206 sgd_solver.cpp:106] Iteration 3920, lr = 0.0001
I0413 10:16:20.152899 22206 solver.cpp:242] Iteration 3960 (0.268288 iter/s, 149.094s/40 iter), loss = -6.43825e-20
I0413 10:16:20.152971 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:16:20.152978 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:16:20.152989 22206 sgd_solver.cpp:106] Iteration 3960, lr = 0.0001
I0413 10:18:49.210336 22206 solver.cpp:242] Iteration 4000 (0.268349 iter/s, 149.06s/40 iter), loss = -6.43825e-20
I0413 10:18:49.210407 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:18:49.210417 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:18:49.210427 22206 sgd_solver.cpp:106] Iteration 4000, lr = 0.0001
I0413 10:21:18.364866 22206 solver.cpp:242] Iteration 4040 (0.268174 iter/s, 149.157s/40 iter), loss = -6.43825e-20
I0413 10:21:18.365006 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:21:18.365016 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:21:18.365027 22206 sgd_solver.cpp:106] Iteration 4040, lr = 0.0001
I0413 10:23:47.478269 22206 solver.cpp:242] Iteration 4080 (0.268248 iter/s, 149.116s/40 iter), loss = -6.43825e-20
I0413 10:23:47.478392 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:23:47.478404 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:23:47.478413 22206 sgd_solver.cpp:106] Iteration 4080, lr = 0.0001
I0413 10:26:16.476501 22206 solver.cpp:242] Iteration 4120 (0.268456 iter/s, 149s/40 iter), loss = -6.43825e-20
I0413 10:26:16.476625 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:26:16.476635 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:26:16.476646 22206 sgd_solver.cpp:106] Iteration 4120, lr = 0.0001
I0413 10:28:45.564611 22206 solver.cpp:242] Iteration 4160 (0.268294 iter/s, 149.09s/40 iter), loss = -6.43825e-20
I0413 10:28:45.564725 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:28:45.564735 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:28:45.564745 22206 sgd_solver.cpp:106] Iteration 4160, lr = 0.0001
I0413 10:30:18.837824 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_4186.caffemodel
I0413 10:30:18.929880 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4186.solverstate
I0413 10:31:14.862416 22206 solver.cpp:242] Iteration 4200 (0.267917 iter/s, 149.3s/40 iter), loss = -6.43825e-20
I0413 10:31:14.862524 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:31:14.862534 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:31:14.862546 22206 sgd_solver.cpp:106] Iteration 4200, lr = 0.0001
I0413 10:33:43.922089 22206 solver.cpp:242] Iteration 4240 (0.268345 iter/s, 149.062s/40 iter), loss = -6.43825e-20
I0413 10:33:43.922157 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:33:43.922165 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:33:43.922175 22206 sgd_solver.cpp:106] Iteration 4240, lr = 0.0001
I0413 10:36:12.987992 22206 solver.cpp:242] Iteration 4280 (0.268334 iter/s, 149.068s/40 iter), loss = -6.43825e-20
I0413 10:36:12.988106 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:36:12.988116 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:36:12.988126 22206 sgd_solver.cpp:106] Iteration 4280, lr = 0.0001
I0413 10:38:42.116134 22206 solver.cpp:242] Iteration 4320 (0.268222 iter/s, 149.13s/40 iter), loss = -6.43825e-20
I0413 10:38:42.116241 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:38:42.116252 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:38:42.116263 22206 sgd_solver.cpp:106] Iteration 4320, lr = 0.0001
I0413 10:41:11.217193 22206 solver.cpp:242] Iteration 4360 (0.26827 iter/s, 149.103s/40 iter), loss = -6.43825e-20
I0413 10:41:11.217311 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:41:11.217321 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:41:11.217332 22206 sgd_solver.cpp:106] Iteration 4360, lr = 0.0001
I0413 10:43:40.140560 22206 solver.cpp:242] Iteration 4400 (0.268591 iter/s, 148.926s/40 iter), loss = -6.43825e-20
I0413 10:43:40.140676 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:43:40.140686 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:43:40.140697 22206 sgd_solver.cpp:106] Iteration 4400, lr = 0.0001
I0413 10:46:09.319939 22206 solver.cpp:242] Iteration 4440 (0.26813 iter/s, 149.182s/40 iter), loss = -6.43825e-20
I0413 10:46:09.320101 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:46:09.320112 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:46:09.320122 22206 sgd_solver.cpp:106] Iteration 4440, lr = 0.0001
I0413 10:48:38.501461 22206 solver.cpp:242] Iteration 4480 (0.268126 iter/s, 149.184s/40 iter), loss = -6.43825e-20
I0413 10:48:38.501576 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:48:38.501586 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:48:38.501597 22206 sgd_solver.cpp:106] Iteration 4480, lr = 0.0001
I0413 10:50:19.204316 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_4508.caffemodel
I0413 10:50:19.296655 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4508.solverstate
I0413 10:51:07.778781 22206 solver.cpp:242] Iteration 4520 (0.267954 iter/s, 149.279s/40 iter), loss = -6.43825e-20
I0413 10:51:07.778888 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:51:07.778899 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:51:07.778910 22206 sgd_solver.cpp:106] Iteration 4520, lr = 0.0001
I0413 10:53:36.721230 22206 solver.cpp:242] Iteration 4560 (0.268556 iter/s, 148.945s/40 iter), loss = -6.43825e-20
I0413 10:53:36.721348 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:53:36.721357 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:53:36.721369 22206 sgd_solver.cpp:106] Iteration 4560, lr = 0.0001
I0413 10:56:05.601032 22206 solver.cpp:242] Iteration 4600 (0.268669 iter/s, 148.882s/40 iter), loss = -6.43825e-20
I0413 10:56:05.601160 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:56:05.601171 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:56:05.601181 22206 sgd_solver.cpp:106] Iteration 4600, lr = 0.0001
I0413 10:58:34.820750 22206 solver.cpp:242] Iteration 4640 (0.268057 iter/s, 149.222s/40 iter), loss = -6.43825e-20
I0413 10:58:34.820994 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 10:58:34.821004 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 10:58:34.821015 22206 sgd_solver.cpp:106] Iteration 4640, lr = 0.0001
I0413 11:01:03.921664 22206 solver.cpp:242] Iteration 4680 (0.268271 iter/s, 149.103s/40 iter), loss = -6.43825e-20
I0413 11:01:03.921782 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:01:03.921792 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:01:03.921803 22206 sgd_solver.cpp:106] Iteration 4680, lr = 0.0001
I0413 11:03:33.017868 22206 solver.cpp:242] Iteration 4720 (0.268279 iter/s, 149.098s/40 iter), loss = -6.43825e-20
I0413 11:03:33.017990 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:03:33.018000 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:03:33.018012 22206 sgd_solver.cpp:106] Iteration 4720, lr = 0.0001
I0413 11:06:02.146970 22206 solver.cpp:242] Iteration 4760 (0.26822 iter/s, 149.131s/40 iter), loss = -6.43825e-20
I0413 11:06:02.147102 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:06:02.147112 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:06:02.147123 22206 sgd_solver.cpp:106] Iteration 4760, lr = 0.0001
I0413 11:08:31.073314 22206 solver.cpp:242] Iteration 4800 (0.268585 iter/s, 148.928s/40 iter), loss = -6.43825e-20
I0413 11:08:31.073456 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:08:31.073467 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:08:31.073477 22206 sgd_solver.cpp:106] Iteration 4800, lr = 0.0001
I0413 11:10:19.107978 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_4830.caffemodel
I0413 11:10:19.199867 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4830.solverstate
I0413 11:10:19.276968 22206 solver.cpp:362] Iteration 4830, Testing net (#0)
I0413 11:10:19.276990 22206 net.cpp:723] Ignoring source layer train_data
I0413 11:10:19.276995 22206 net.cpp:723] Ignoring source layer train_label
I0413 11:10:19.276999 22206 net.cpp:723] Ignoring source layer train_transform
I0413 11:10:37.890264 22206 solver.cpp:429] Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:10:37.890288 22206 solver.cpp:429] Test net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:10:37.890295 22206 solver.cpp:429] Test net output #2: mAP = 0
I0413 11:10:37.890300 22206 solver.cpp:429] Test net output #3: precision = 0
I0413 11:10:37.890305 22206 solver.cpp:429] Test net output #4: recall = 0
I0413 11:11:18.855345 22206 solver.cpp:242] Iteration 4840 (0.238401 iter/s, 167.784s/40 iter), loss = -6.43825e-20
I0413 11:11:18.855469 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:11:18.855479 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:11:18.855490 22206 sgd_solver.cpp:106] Iteration 4840, lr = 0.0001
I0413 11:13:47.931879 22206 solver.cpp:242] Iteration 4880 (0.268315 iter/s, 149.079s/40 iter), loss = -6.43825e-20
I0413 11:13:47.931987 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:13:47.931996 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:13:47.932008 22206 sgd_solver.cpp:106] Iteration 4880, lr = 0.0001
I0413 11:16:17.053846 22206 solver.cpp:242] Iteration 4920 (0.268233 iter/s, 149.124s/40 iter), loss = -6.43825e-20
I0413 11:16:17.053958 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:16:17.053969 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:16:17.053980 22206 sgd_solver.cpp:106] Iteration 4920, lr = 0.0001
I0413 11:18:46.184485 22206 solver.cpp:242] Iteration 4960 (0.268217 iter/s, 149.133s/40 iter), loss = -6.43825e-20
I0413 11:18:46.184582 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:18:46.184592 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:18:46.184602 22206 sgd_solver.cpp:106] Iteration 4960, lr = 0.0001
I0413 11:21:15.249085 22206 solver.cpp:242] Iteration 5000 (0.268336 iter/s, 149.067s/40 iter), loss = -6.43825e-20
I0413 11:21:15.249160 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:21:15.249169 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:21:15.249181 22206 sgd_solver.cpp:106] Iteration 5000, lr = 0.0001
I0413 11:23:44.405105 22206 solver.cpp:242] Iteration 5040 (0.268172 iter/s, 149.158s/40 iter), loss = -6.43825e-20
I0413 11:23:44.405216 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:23:44.405226 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:23:44.405237 22206 sgd_solver.cpp:106] Iteration 5040, lr = 0.0001
I0413 11:26:13.267356 22206 solver.cpp:242] Iteration 5080 (0.268701 iter/s, 148.864s/40 iter), loss = -6.43825e-20
I0413 11:26:13.267424 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:26:13.267433 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:26:13.267443 22206 sgd_solver.cpp:106] Iteration 5080, lr = 0.0001
I0413 11:28:42.219928 22206 solver.cpp:242] Iteration 5120 (0.268538 iter/s, 148.955s/40 iter), loss = -6.43825e-20
I0413 11:28:42.220067 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:28:42.220077 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:28:42.220088 22206 sgd_solver.cpp:106] Iteration 5120, lr = 0.0001
I0413 11:30:37.844563 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_5152.caffemodel
I0413 11:30:37.936465 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5152.solverstate
I0413 11:31:11.507647 22206 solver.cpp:242] Iteration 5160 (0.267935 iter/s, 149.29s/40 iter), loss = -6.43825e-20
I0413 11:31:11.507751 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:31:11.507761 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:31:11.507772 22206 sgd_solver.cpp:106] Iteration 5160, lr = 0.0001
I0413 11:33:40.654230 22206 solver.cpp:242] Iteration 5200 (0.268189 iter/s, 149.149s/40 iter), loss = -6.43825e-20
I0413 11:33:40.654347 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:33:40.654357 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:33:40.654368 22206 sgd_solver.cpp:106] Iteration 5200, lr = 0.0001
I0413 11:36:09.591799 22206 solver.cpp:242] Iteration 5240 (0.268565 iter/s, 148.94s/40 iter), loss = -6.43825e-20
I0413 11:36:09.591915 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:36:09.591925 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:36:09.591936 22206 sgd_solver.cpp:106] Iteration 5240, lr = 0.0001
I0413 11:38:38.533434 22206 solver.cpp:242] Iteration 5280 (0.268558 iter/s, 148.944s/40 iter), loss = -6.43825e-20
I0413 11:38:38.533537 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:38:38.533547 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:38:38.533558 22206 sgd_solver.cpp:106] Iteration 5280, lr = 0.0001
I0413 11:41:07.509539 22206 solver.cpp:242] Iteration 5320 (0.268496 iter/s, 148.978s/40 iter), loss = -6.43825e-20
I0413 11:41:07.509606 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:41:07.509615 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:41:07.509626 22206 sgd_solver.cpp:106] Iteration 5320, lr = 0.0001
I0413 11:43:36.430819 22206 solver.cpp:242] Iteration 5360 (0.268594 iter/s, 148.923s/40 iter), loss = -6.43825e-20
I0413 11:43:36.430930 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:43:36.430940 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:43:36.430951 22206 sgd_solver.cpp:106] Iteration 5360, lr = 0.0001
I0413 11:46:05.434841 22206 solver.cpp:242] Iteration 5400 (0.268445 iter/s, 149.006s/40 iter), loss = -6.43825e-20
I0413 11:46:05.434968 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:46:05.434978 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:46:05.434988 22206 sgd_solver.cpp:106] Iteration 5400, lr = 0.0001
I0413 11:48:34.255635 22206 solver.cpp:242] Iteration 5440 (0.268776 iter/s, 148.823s/40 iter), loss = -6.43825e-20
I0413 11:48:34.255708 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:48:34.255717 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:48:34.255728 22206 sgd_solver.cpp:106] Iteration 5440, lr = 0.0001
I0413 11:50:37.201242 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_5474.caffemodel
I0413 11:50:37.292891 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5474.solverstate
I0413 11:51:03.436975 22206 solver.cpp:242] Iteration 5480 (0.268126 iter/s, 149.184s/40 iter), loss = -6.43825e-20
I0413 11:51:03.437016 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:51:03.437024 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:51:03.437034 22206 sgd_solver.cpp:106] Iteration 5480, lr = 0.0001
I0413 11:53:32.492372 22206 solver.cpp:242] Iteration 5520 (0.268353 iter/s, 149.058s/40 iter), loss = -6.43825e-20
I0413 11:53:32.492514 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:53:32.492525 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:53:32.492535 22206 sgd_solver.cpp:106] Iteration 5520, lr = 0.0001
I0413 11:56:01.845796 22206 solver.cpp:242] Iteration 5560 (0.267817 iter/s, 149.356s/40 iter), loss = -6.43825e-20
I0413 11:56:01.845908 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:56:01.845919 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:56:01.845930 22206 sgd_solver.cpp:106] Iteration 5560, lr = 0.0001
I0413 11:58:30.763732 22206 solver.cpp:242] Iteration 5600 (0.2686 iter/s, 148.92s/40 iter), loss = -6.43825e-20
I0413 11:58:30.763833 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 11:58:30.763842 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 11:58:30.763854 22206 sgd_solver.cpp:106] Iteration 5600, lr = 0.0001
I0413 12:00:59.810398 22206 solver.cpp:242] Iteration 5640 (0.268368 iter/s, 149.049s/40 iter), loss = -6.43825e-20
I0413 12:00:59.810508 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:00:59.810519 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:00:59.810530 22206 sgd_solver.cpp:106] Iteration 5640, lr = 0.0001
I0413 12:03:28.841716 22206 solver.cpp:242] Iteration 5680 (0.268396 iter/s, 149.033s/40 iter), loss = -6.43825e-20
I0413 12:03:28.841830 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:03:28.841840 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:03:28.841852 22206 sgd_solver.cpp:106] Iteration 5680, lr = 0.0001
I0413 12:05:57.901038 22206 solver.cpp:242] Iteration 5720 (0.268346 iter/s, 149.061s/40 iter), loss = -6.43825e-20
I0413 12:05:57.901135 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:05:57.901145 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:05:57.901156 22206 sgd_solver.cpp:106] Iteration 5720, lr = 0.0001
I0413 12:08:27.050364 22206 solver.cpp:242] Iteration 5760 (0.268184 iter/s, 149.152s/40 iter), loss = -6.43825e-20
I0413 12:08:27.050463 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:08:27.050473 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:08:27.050484 22206 sgd_solver.cpp:106] Iteration 5760, lr = 0.0001
I0413 12:10:37.396387 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_5796.caffemodel
I0413 12:10:37.488693 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5796.solverstate
I0413 12:10:56.210398 22206 solver.cpp:242] Iteration 5800 (0.268164 iter/s, 149.162s/40 iter), loss = -6.43825e-20
I0413 12:10:56.210441 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:10:56.210450 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:10:56.210460 22206 sgd_solver.cpp:106] Iteration 5800, lr = 0.0001
I0413 12:13:25.227550 22206 solver.cpp:242] Iteration 5840 (0.268422 iter/s, 149.019s/40 iter), loss = -6.43825e-20
I0413 12:13:25.227655 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:13:25.227665 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:13:25.227676 22206 sgd_solver.cpp:106] Iteration 5840, lr = 0.0001
I0413 12:15:54.367265 22206 solver.cpp:242] Iteration 5880 (0.268201 iter/s, 149.142s/40 iter), loss = -6.43825e-20
I0413 12:15:54.367399 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:15:54.367416 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:15:54.367434 22206 sgd_solver.cpp:106] Iteration 5880, lr = 0.0001
I0413 12:18:23.401654 22206 solver.cpp:242] Iteration 5920 (0.268391 iter/s, 149.037s/40 iter), loss = -6.43825e-20
I0413 12:18:23.401799 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:18:23.401809 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:18:23.401820 22206 sgd_solver.cpp:106] Iteration 5920, lr = 0.0001
I0413 12:20:52.562824 22206 solver.cpp:242] Iteration 5960 (0.268162 iter/s, 149.163s/40 iter), loss = -6.43825e-20
I0413 12:20:52.562935 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:20:52.562944 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:20:52.562955 22206 sgd_solver.cpp:106] Iteration 5960, lr = 0.0001
I0413 12:23:21.625830 22206 solver.cpp:242] Iteration 6000 (0.268339 iter/s, 149.065s/40 iter), loss = -6.43825e-20
I0413 12:23:21.625941 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:23:21.625950 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:23:21.625960 22206 sgd_solver.cpp:106] Iteration 6000, lr = 0.0001
I0413 12:25:50.510841 22206 solver.cpp:242] Iteration 6040 (0.26866 iter/s, 148.887s/40 iter), loss = -6.43825e-20
I0413 12:25:50.510959 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:25:50.510969 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:25:50.510980 22206 sgd_solver.cpp:106] Iteration 6040, lr = 0.0001
I0413 12:28:19.695318 22206 solver.cpp:242] Iteration 6080 (0.26812 iter/s, 149.187s/40 iter), loss = -6.43825e-20
I0413 12:28:19.695426 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:28:19.695436 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:28:19.695448 22206 sgd_solver.cpp:106] Iteration 6080, lr = 0.0001
I0413 12:30:37.608603 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_6118.caffemodel
I0413 12:30:37.700292 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6118.solverstate
I0413 12:30:48.915585 22206 solver.cpp:242] Iteration 6120 (0.268056 iter/s, 149.222s/40 iter), loss = -6.43825e-20
I0413 12:30:48.915632 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:30:48.915639 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:30:48.915649 22206 sgd_solver.cpp:106] Iteration 6120, lr = 0.0001
I0413 12:33:18.196563 22206 solver.cpp:242] Iteration 6160 (0.267947 iter/s, 149.283s/40 iter), loss = -6.43825e-20
I0413 12:33:18.196668 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:33:18.196678 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:33:18.196689 22206 sgd_solver.cpp:106] Iteration 6160, lr = 0.0001
I0413 12:35:47.248986 22206 solver.cpp:242] Iteration 6200 (0.268358 iter/s, 149.055s/40 iter), loss = -6.43825e-20
I0413 12:35:47.249079 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:35:47.249089 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:35:47.249099 22206 sgd_solver.cpp:106] Iteration 6200, lr = 0.0001
I0413 12:38:16.190786 22206 solver.cpp:242] Iteration 6240 (0.268557 iter/s, 148.944s/40 iter), loss = -6.43825e-20
I0413 12:38:16.190904 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:38:16.190914 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:38:16.190924 22206 sgd_solver.cpp:106] Iteration 6240, lr = 0.0001
I0413 12:40:45.315899 22206 solver.cpp:242] Iteration 6280 (0.268227 iter/s, 149.127s/40 iter), loss = -6.43825e-20
I0413 12:40:45.316043 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:40:45.316054 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:40:45.316066 22206 sgd_solver.cpp:106] Iteration 6280, lr = 0.0001
I0413 12:43:14.356199 22206 solver.cpp:242] Iteration 6320 (0.26838 iter/s, 149.042s/40 iter), loss = -6.43825e-20
I0413 12:43:14.356298 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:43:14.356307 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:43:14.356318 22206 sgd_solver.cpp:106] Iteration 6320, lr = 0.0001
I0413 12:45:43.493907 22206 solver.cpp:242] Iteration 6360 (0.268205 iter/s, 149.14s/40 iter), loss = -6.43825e-20
I0413 12:45:43.494006 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:45:43.494016 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:45:43.494027 22206 sgd_solver.cpp:106] Iteration 6360, lr = 0.0001
I0413 12:48:12.582765 22206 solver.cpp:242] Iteration 6400 (0.268292 iter/s, 149.091s/40 iter), loss = -6.43825e-20
I0413 12:48:12.582886 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:48:12.582897 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:48:12.582908 22206 sgd_solver.cpp:106] Iteration 6400, lr = 1e-05
I0413 12:50:38.043826 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_6440.caffemodel
I0413 12:50:38.135321 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6440.solverstate
I0413 12:50:38.208289 22206 solver.cpp:362] Iteration 6440, Testing net (#0)
I0413 12:50:38.208309 22206 net.cpp:723] Ignoring source layer train_data
I0413 12:50:38.208314 22206 net.cpp:723] Ignoring source layer train_label
I0413 12:50:38.208317 22206 net.cpp:723] Ignoring source layer train_transform
I0413 12:50:56.811767 22206 solver.cpp:429] Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:50:56.811790 22206 solver.cpp:429] Test net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:50:56.811796 22206 solver.cpp:429] Test net output #2: mAP = 0
I0413 12:50:56.811801 22206 solver.cpp:429] Test net output #3: precision = 0
I0413 12:50:56.811805 22206 solver.cpp:429] Test net output #4: recall = 0
I0413 12:51:00.520951 22206 solver.cpp:242] Iteration 6440 (0.238179 iter/s, 167.941s/40 iter), loss = -6.43825e-20
I0413 12:51:00.520995 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:51:00.521003 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:51:00.521014 22206 sgd_solver.cpp:106] Iteration 6440, lr = 1e-05
I0413 12:53:29.562093 22206 solver.cpp:242] Iteration 6480 (0.268378 iter/s, 149.043s/40 iter), loss = -6.43825e-20
I0413 12:53:29.562188 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:53:29.562198 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:53:29.562208 22206 sgd_solver.cpp:106] Iteration 6480, lr = 1e-05
I0413 12:55:58.574651 22206 solver.cpp:242] Iteration 6520 (0.26843 iter/s, 149.015s/40 iter), loss = -6.43825e-20
I0413 12:55:58.574770 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:55:58.574780 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:55:58.574791 22206 sgd_solver.cpp:106] Iteration 6520, lr = 1e-05
I0413 12:58:27.761814 22206 solver.cpp:242] Iteration 6560 (0.268116 iter/s, 149.189s/40 iter), loss = -6.43825e-20
I0413 12:58:27.761919 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 12:58:27.761929 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 12:58:27.761940 22206 sgd_solver.cpp:106] Iteration 6560, lr = 1e-05
I0413 13:00:56.901226 22206 solver.cpp:242] Iteration 6600 (0.268202 iter/s, 149.142s/40 iter), loss = -6.43825e-20
I0413 13:00:56.901410 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:00:56.901429 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:00:56.901448 22206 sgd_solver.cpp:106] Iteration 6600, lr = 1e-05
I0413 13:03:25.910713 22206 solver.cpp:242] Iteration 6640 (0.268435 iter/s, 149.012s/40 iter), loss = -6.43825e-20
I0413 13:03:25.910823 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:03:25.910832 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:03:25.910843 22206 sgd_solver.cpp:106] Iteration 6640, lr = 1e-05
I0413 13:05:55.244607 22206 solver.cpp:242] Iteration 6680 (0.267852 iter/s, 149.336s/40 iter), loss = -6.43825e-20
I0413 13:05:55.244720 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:05:55.244731 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:05:55.244742 22206 sgd_solver.cpp:106] Iteration 6680, lr = 1e-05
I0413 13:08:24.363471 22206 solver.cpp:242] Iteration 6720 (0.268238 iter/s, 149.121s/40 iter), loss = -6.43825e-20
I0413 13:08:24.363572 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:08:24.363582 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:08:24.363593 22206 sgd_solver.cpp:106] Iteration 6720, lr = 1e-05
I0413 13:10:53.399459 22206 solver.cpp:242] Iteration 6760 (0.268388 iter/s, 149.038s/40 iter), loss = -6.43825e-20
I0413 13:10:53.399587 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:10:53.399597 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:10:53.399607 22206 sgd_solver.cpp:106] Iteration 6760, lr = 1e-05
I0413 13:10:57.139369 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_6762.caffemodel
I0413 13:10:57.231012 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6762.solverstate
I0413 13:13:22.875146 22206 solver.cpp:242] Iteration 6800 (0.267598 iter/s, 149.478s/40 iter), loss = -6.43825e-20
I0413 13:13:22.875260 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:13:22.875272 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:13:22.875283 22206 sgd_solver.cpp:106] Iteration 6800, lr = 1e-05
I0413 13:15:51.919904 22206 solver.cpp:242] Iteration 6840 (0.268372 iter/s, 149.047s/40 iter), loss = -6.43825e-20
I0413 13:15:51.920022 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:15:51.920032 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:15:51.920043 22206 sgd_solver.cpp:106] Iteration 6840, lr = 1e-05
I0413 13:18:21.060111 22206 solver.cpp:242] Iteration 6880 (0.2682 iter/s, 149.142s/40 iter), loss = -6.43825e-20
I0413 13:18:21.060223 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:18:21.060233 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:18:21.060245 22206 sgd_solver.cpp:106] Iteration 6880, lr = 1e-05
I0413 13:20:50.300302 22206 solver.cpp:242] Iteration 6920 (0.26802 iter/s, 149.242s/40 iter), loss = -6.43825e-20
I0413 13:20:50.300415 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:20:50.300423 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:20:50.300434 22206 sgd_solver.cpp:106] Iteration 6920, lr = 1e-05
I0413 13:23:19.393328 22206 solver.cpp:242] Iteration 6960 (0.268285 iter/s, 149.095s/40 iter), loss = -6.43825e-20
I0413 13:23:19.393443 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:23:19.393453 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:23:19.393465 22206 sgd_solver.cpp:106] Iteration 6960, lr = 1e-05
I0413 13:25:48.366895 22206 solver.cpp:242] Iteration 7000 (0.2685 iter/s, 148.976s/40 iter), loss = -6.43825e-20
I0413 13:25:48.367045 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:25:48.367056 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:25:48.367066 22206 sgd_solver.cpp:106] Iteration 7000, lr = 1e-05
I0413 13:28:17.712879 22206 solver.cpp:242] Iteration 7040 (0.267831 iter/s, 149.348s/40 iter), loss = -6.43825e-20
I0413 13:28:17.713006 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:28:17.713016 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:28:17.713027 22206 sgd_solver.cpp:106] Iteration 7040, lr = 1e-05
I0413 13:30:47.053527 22206 solver.cpp:242] Iteration 7080 (0.26784 iter/s, 149.343s/40 iter), loss = -6.43825e-20
I0413 13:30:47.053652 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:30:47.053661 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:30:47.053673 22206 sgd_solver.cpp:106] Iteration 7080, lr = 1e-05
I0413 13:30:58.248433 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_7084.caffemodel
I0413 13:30:58.340050 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7084.solverstate
I0413 13:33:16.445003 22206 solver.cpp:242] Iteration 7120 (0.267749 iter/s, 149.394s/40 iter), loss = -6.43825e-20
I0413 13:33:16.445122 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:33:16.445132 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:33:16.445143 22206 sgd_solver.cpp:106] Iteration 7120, lr = 1e-05
I0413 13:35:45.404561 22206 solver.cpp:242] Iteration 7160 (0.268525 iter/s, 148.962s/40 iter), loss = -6.43825e-20
I0413 13:35:45.404664 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:35:45.404673 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:35:45.404685 22206 sgd_solver.cpp:106] Iteration 7160, lr = 1e-05
I0413 13:38:14.508970 22206 solver.cpp:242] Iteration 7200 (0.268264 iter/s, 149.107s/40 iter), loss = -6.43825e-20
I0413 13:38:14.509091 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:38:14.509101 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:38:14.509112 22206 sgd_solver.cpp:106] Iteration 7200, lr = 1e-05
I0413 13:40:43.527680 22206 solver.cpp:242] Iteration 7240 (0.268419 iter/s, 149.021s/40 iter), loss = -6.43825e-20
I0413 13:40:43.527781 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:40:43.527791 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:40:43.527801 22206 sgd_solver.cpp:106] Iteration 7240, lr = 1e-05
I0413 13:43:12.574939 22206 solver.cpp:242] Iteration 7280 (0.268367 iter/s, 149.049s/40 iter), loss = -6.43825e-20
I0413 13:43:12.575076 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:43:12.575086 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:43:12.575098 22206 sgd_solver.cpp:106] Iteration 7280, lr = 1e-05
I0413 13:45:41.590486 22206 solver.cpp:242] Iteration 7320 (0.268425 iter/s, 149.018s/40 iter), loss = -6.43825e-20
I0413 13:45:41.590557 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:45:41.590566 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:45:41.590577 22206 sgd_solver.cpp:106] Iteration 7320, lr = 1e-05
I0413 13:48:10.610152 22206 solver.cpp:242] Iteration 7360 (0.268417 iter/s, 149.022s/40 iter), loss = -6.43825e-20
I0413 13:48:10.610265 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:48:10.610275 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:48:10.610286 22206 sgd_solver.cpp:106] Iteration 7360, lr = 1e-05
I0413 13:50:39.783463 22206 solver.cpp:242] Iteration 7400 (0.268141 iter/s, 149.175s/40 iter), loss = -6.43825e-20
I0413 13:50:39.783591 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:50:39.783601 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:50:39.783612 22206 sgd_solver.cpp:106] Iteration 7400, lr = 1e-05
I0413 13:50:58.456598 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_7406.caffemodel
I0413 13:50:58.548084 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7406.solverstate
I0413 13:53:09.213758 22206 solver.cpp:242] Iteration 7440 (0.26768 iter/s, 149.432s/40 iter), loss = -6.43825e-20
I0413 13:53:09.213860 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:53:09.213870 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:53:09.213881 22206 sgd_solver.cpp:106] Iteration 7440, lr = 1e-05
I0413 13:55:38.401008 22206 solver.cpp:242] Iteration 7480 (0.268116 iter/s, 149.189s/40 iter), loss = -6.43825e-20
I0413 13:55:38.401109 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:55:38.401119 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:55:38.401130 22206 sgd_solver.cpp:106] Iteration 7480, lr = 1e-05
I0413 13:58:07.487855 22206 solver.cpp:242] Iteration 7520 (0.268296 iter/s, 149.089s/40 iter), loss = -6.43825e-20
I0413 13:58:07.487967 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 13:58:07.487977 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 13:58:07.487988 22206 sgd_solver.cpp:106] Iteration 7520, lr = 1e-05
I0413 14:00:36.686660 22206 solver.cpp:242] Iteration 7560 (0.268095 iter/s, 149.201s/40 iter), loss = -6.43825e-20
I0413 14:00:36.686791 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:00:36.686801 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:00:36.686813 22206 sgd_solver.cpp:106] Iteration 7560, lr = 1e-05
I0413 14:03:05.957505 22206 solver.cpp:242] Iteration 7600 (0.267965 iter/s, 149.273s/40 iter), loss = -6.43825e-20
I0413 14:03:05.957608 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:03:05.957618 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:03:05.957629 22206 sgd_solver.cpp:106] Iteration 7600, lr = 1e-05
I0413 14:05:35.178156 22206 solver.cpp:242] Iteration 7640 (0.268056 iter/s, 149.223s/40 iter), loss = -6.43825e-20
I0413 14:05:35.178259 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:05:35.178268 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:05:35.178279 22206 sgd_solver.cpp:106] Iteration 7640, lr = 1e-05
I0413 14:08:04.350474 22206 solver.cpp:242] Iteration 7680 (0.268142 iter/s, 149.174s/40 iter), loss = -6.43825e-20
I0413 14:08:04.350613 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:08:04.350625 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:08:04.350634 22206 sgd_solver.cpp:106] Iteration 7680, lr = 1e-05
I0413 14:10:33.404428 22206 solver.cpp:242] Iteration 7720 (0.268355 iter/s, 149.056s/40 iter), loss = -6.43825e-20
I0413 14:10:33.404542 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:10:33.404552 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:10:33.404563 22206 sgd_solver.cpp:106] Iteration 7720, lr = 1e-05
I0413 14:10:59.537984 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_7728.caffemodel
I0413 14:10:59.629503 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7728.solverstate
I0413 14:13:02.751924 22206 solver.cpp:242] Iteration 7760 (0.267828 iter/s, 149.35s/40 iter), loss = -6.43825e-20
I0413 14:13:02.752023 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:13:02.752033 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:13:02.752044 22206 sgd_solver.cpp:106] Iteration 7760, lr = 1e-05
I0413 14:15:32.002921 22206 solver.cpp:242] Iteration 7800 (0.268001 iter/s, 149.253s/40 iter), loss = -6.43825e-20
I0413 14:15:32.003027 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:15:32.003038 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:15:32.003049 22206 sgd_solver.cpp:106] Iteration 7800, lr = 1e-05
I0413 14:18:01.238049 22206 solver.cpp:242] Iteration 7840 (0.26803 iter/s, 149.237s/40 iter), loss = -6.43825e-20
I0413 14:18:01.238193 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:18:01.238219 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:18:01.238237 22206 sgd_solver.cpp:106] Iteration 7840, lr = 1e-05
I0413 14:20:30.413532 22206 solver.cpp:242] Iteration 7880 (0.268137 iter/s, 149.178s/40 iter), loss = -6.43825e-20
I0413 14:20:30.413650 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:20:30.413661 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:20:30.413671 22206 sgd_solver.cpp:106] Iteration 7880, lr = 1e-05
I0413 14:22:59.518537 22206 solver.cpp:242] Iteration 7920 (0.268263 iter/s, 149.107s/40 iter), loss = -6.43825e-20
I0413 14:22:59.518635 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:22:59.518645 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:22:59.518656 22206 sgd_solver.cpp:106] Iteration 7920, lr = 1e-05
I0413 14:25:28.803967 22206 solver.cpp:242] Iteration 7960 (0.267939 iter/s, 149.288s/40 iter), loss = -6.43825e-20
I0413 14:25:28.804087 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:25:28.804098 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:25:28.804110 22206 sgd_solver.cpp:106] Iteration 7960, lr = 1e-05
I0413 14:27:57.851713 22206 solver.cpp:242] Iteration 8000 (0.268367 iter/s, 149.05s/40 iter), loss = -6.43825e-20
I0413 14:27:57.851835 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:27:57.851845 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:27:57.851856 22206 sgd_solver.cpp:106] Iteration 8000, lr = 1e-05
I0413 14:30:27.084547 22206 solver.cpp:242] Iteration 8040 (0.268034 iter/s, 149.235s/40 iter), loss = -6.43825e-20
I0413 14:30:27.084661 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:30:27.084671 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:30:27.084682 22206 sgd_solver.cpp:106] Iteration 8040, lr = 1e-05
I0413 14:31:00.638676 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_8050.caffemodel
I0413 14:31:00.730593 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8050.solverstate
I0413 14:31:00.803562 22206 solver.cpp:362] Iteration 8050, Testing net (#0)
I0413 14:31:00.803586 22206 net.cpp:723] Ignoring source layer train_data
I0413 14:31:00.803591 22206 net.cpp:723] Ignoring source layer train_label
I0413 14:31:00.803594 22206 net.cpp:723] Ignoring source layer train_transform
I0413 14:31:19.506942 22206 solver.cpp:429] Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:31:19.506966 22206 solver.cpp:429] Test net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:31:19.506973 22206 solver.cpp:429] Test net output #2: mAP = 0
I0413 14:31:19.506978 22206 solver.cpp:429] Test net output #3: precision = 0
I0413 14:31:19.506983 22206 solver.cpp:429] Test net output #4: recall = 0
I0413 14:33:15.070456 22206 solver.cpp:242] Iteration 8080 (0.238112 iter/s, 167.988s/40 iter), loss = -6.43825e-20
I0413 14:33:15.070543 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:33:15.070551 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:33:15.070562 22206 sgd_solver.cpp:106] Iteration 8080, lr = 1e-05
I0413 14:35:44.386499 22206 solver.cpp:242] Iteration 8120 (0.267884 iter/s, 149.318s/40 iter), loss = -6.43825e-20
I0413 14:35:44.386603 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:35:44.386613 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:35:44.386625 22206 sgd_solver.cpp:106] Iteration 8120, lr = 1e-05
I0413 14:38:13.611971 22206 solver.cpp:242] Iteration 8160 (0.268047 iter/s, 149.228s/40 iter), loss = -6.43825e-20
I0413 14:38:13.612078 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:38:13.612088 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:38:13.612099 22206 sgd_solver.cpp:106] Iteration 8160, lr = 1e-05
I0413 14:40:42.722482 22206 solver.cpp:242] Iteration 8200 (0.268254 iter/s, 149.113s/40 iter), loss = -6.43825e-20
I0413 14:40:42.722584 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:40:42.722594 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:40:42.722604 22206 sgd_solver.cpp:106] Iteration 8200, lr = 1e-05
I0413 14:43:11.992506 22206 solver.cpp:242] Iteration 8240 (0.267967 iter/s, 149.272s/40 iter), loss = -6.43825e-20
I0413 14:43:11.992638 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:43:11.992650 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:43:11.992662 22206 sgd_solver.cpp:106] Iteration 8240, lr = 1e-05
I0413 14:45:41.087404 22206 solver.cpp:242] Iteration 8280 (0.268282 iter/s, 149.097s/40 iter), loss = -6.43825e-20
I0413 14:45:41.087527 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:45:41.087538 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:45:41.087548 22206 sgd_solver.cpp:106] Iteration 8280, lr = 1e-05
I0413 14:48:10.234676 22206 solver.cpp:242] Iteration 8320 (0.268188 iter/s, 149.149s/40 iter), loss = -6.43825e-20
I0413 14:48:10.234797 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:48:10.234807 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:48:10.234819 22206 sgd_solver.cpp:106] Iteration 8320, lr = 1e-05
I0413 14:50:39.711995 22206 solver.cpp:242] Iteration 8360 (0.267595 iter/s, 149.479s/40 iter), loss = -6.43825e-20
I0413 14:50:39.712088 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:50:39.712097 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:50:39.712108 22206 sgd_solver.cpp:106] Iteration 8360, lr = 1e-05
I0413 14:51:20.728899 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_8372.caffemodel
I0413 14:51:20.821074 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8372.solverstate
I0413 14:53:09.080492 22206 solver.cpp:242] Iteration 8400 (0.26779 iter/s, 149.371s/40 iter), loss = -6.43825e-20
I0413 14:53:09.080615 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:53:09.080626 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:53:09.080637 22206 sgd_solver.cpp:106] Iteration 8400, lr = 1e-05
I0413 14:55:38.304270 22206 solver.cpp:242] Iteration 8440 (0.26805 iter/s, 149.226s/40 iter), loss = -6.43825e-20
I0413 14:55:38.304374 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:55:38.304385 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:55:38.304396 22206 sgd_solver.cpp:106] Iteration 8440, lr = 1e-05
I0413 14:58:07.588831 22206 solver.cpp:242] Iteration 8480 (0.267941 iter/s, 149.287s/40 iter), loss = -6.43825e-20
I0413 14:58:07.588932 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 14:58:07.588943 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 14:58:07.588955 22206 sgd_solver.cpp:106] Iteration 8480, lr = 1e-05
I0413 15:00:36.969568 22206 solver.cpp:242] Iteration 8520 (0.267768 iter/s, 149.383s/40 iter), loss = -6.43825e-20
I0413 15:00:36.969687 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:00:36.969698 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:00:36.969708 22206 sgd_solver.cpp:106] Iteration 8520, lr = 1e-05
I0413 15:03:06.332303 22206 solver.cpp:242] Iteration 8560 (0.267801 iter/s, 149.365s/40 iter), loss = -6.43825e-20
I0413 15:03:06.332402 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:03:06.332412 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:03:06.332423 22206 sgd_solver.cpp:106] Iteration 8560, lr = 1e-05
I0413 15:05:35.588589 22206 solver.cpp:242] Iteration 8600 (0.267992 iter/s, 149.258s/40 iter), loss = -6.43825e-20
I0413 15:05:35.588707 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:05:35.588717 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:05:35.588728 22206 sgd_solver.cpp:106] Iteration 8600, lr = 1e-05
I0413 15:08:04.741399 22206 solver.cpp:242] Iteration 8640 (0.268178 iter/s, 149.155s/40 iter), loss = -6.43825e-20
I0413 15:08:04.741468 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:08:04.741478 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:08:04.741488 22206 sgd_solver.cpp:106] Iteration 8640, lr = 1e-05
I0413 15:10:34.081730 22206 solver.cpp:242] Iteration 8680 (0.267841 iter/s, 149.342s/40 iter), loss = -6.43825e-20
I0413 15:10:34.081846 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:10:34.081857 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:10:34.081869 22206 sgd_solver.cpp:106] Iteration 8680, lr = 1e-05
I0413 15:11:22.549012 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_8694.caffemodel
I0413 15:11:22.642297 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8694.solverstate
I0413 15:13:03.606060 22206 solver.cpp:242] Iteration 8720 (0.267511 iter/s, 149.526s/40 iter), loss = -6.43825e-20
I0413 15:13:03.606163 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:13:03.606173 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:13:03.606184 22206 sgd_solver.cpp:106] Iteration 8720, lr = 1e-05
I0413 15:15:33.144889 22206 solver.cpp:242] Iteration 8760 (0.267485 iter/s, 149.541s/40 iter), loss = -6.43825e-20
I0413 15:15:33.144995 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:15:33.145006 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:15:33.145016 22206 sgd_solver.cpp:106] Iteration 8760, lr = 1e-05
I0413 15:18:02.505872 22206 solver.cpp:242] Iteration 8800 (0.267804 iter/s, 149.363s/40 iter), loss = -6.43825e-20
I0413 15:18:02.506003 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:18:02.506013 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:18:02.506026 22206 sgd_solver.cpp:106] Iteration 8800, lr = 1e-05
I0413 15:20:32.117471 22206 solver.cpp:242] Iteration 8840 (0.267355 iter/s, 149.614s/40 iter), loss = -6.43825e-20
I0413 15:20:32.117594 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:20:32.117605 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:20:32.117616 22206 sgd_solver.cpp:106] Iteration 8840, lr = 1e-05
I0413 15:23:01.613730 22206 solver.cpp:242] Iteration 8880 (0.267561 iter/s, 149.498s/40 iter), loss = -6.43825e-20
I0413 15:23:01.613893 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:23:01.613903 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:23:01.613915 22206 sgd_solver.cpp:106] Iteration 8880, lr = 1e-05
I0413 15:25:31.309424 22206 solver.cpp:242] Iteration 8920 (0.267205 iter/s, 149.698s/40 iter), loss = -6.43825e-20
I0413 15:25:31.309556 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:25:31.309566 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:25:31.309579 22206 sgd_solver.cpp:106] Iteration 8920, lr = 1e-05
I0413 15:28:01.191401 22206 solver.cpp:242] Iteration 8960 (0.266873 iter/s, 149.884s/40 iter), loss = -6.43825e-20
I0413 15:28:01.191548 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:28:01.191558 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:28:01.191572 22206 sgd_solver.cpp:106] Iteration 8960, lr = 1e-05
I0413 15:30:31.321321 22206 solver.cpp:242] Iteration 9000 (0.266432 iter/s, 150.132s/40 iter), loss = -6.43825e-20
I0413 15:30:31.321535 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:30:31.321545 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:30:31.321558 22206 sgd_solver.cpp:106] Iteration 9000, lr = 1e-05
I0413 15:31:27.611608 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_9016.caffemodel
I0413 15:31:27.705700 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9016.solverstate
I0413 15:33:01.407253 22206 solver.cpp:242] Iteration 9040 (0.26651 iter/s, 150.088s/40 iter), loss = -6.43825e-20
I0413 15:33:01.407387 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:33:01.407397 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:33:01.407411 22206 sgd_solver.cpp:106] Iteration 9040, lr = 1e-05
I0413 15:35:31.367146 22206 solver.cpp:242] Iteration 9080 (0.266734 iter/s, 149.962s/40 iter), loss = -6.43825e-20
I0413 15:35:31.367285 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:35:31.367297 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:35:31.367310 22206 sgd_solver.cpp:106] Iteration 9080, lr = 1e-05
I0413 15:38:01.585503 22206 solver.cpp:242] Iteration 9120 (0.266275 iter/s, 150.22s/40 iter), loss = -6.43825e-20
I0413 15:38:01.585613 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:38:01.585623 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:38:01.585635 22206 sgd_solver.cpp:106] Iteration 9120, lr = 1e-05
I0413 15:40:31.827421 22206 solver.cpp:242] Iteration 9160 (0.266233 iter/s, 150.244s/40 iter), loss = -6.43825e-20
I0413 15:40:31.827508 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:40:31.827517 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:40:31.827530 22206 sgd_solver.cpp:106] Iteration 9160, lr = 1e-05
I0413 15:43:01.989017 22206 solver.cpp:242] Iteration 9200 (0.266376 iter/s, 150.164s/40 iter), loss = -6.43825e-20
I0413 15:43:01.989157 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:43:01.989168 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:43:01.989181 22206 sgd_solver.cpp:106] Iteration 9200, lr = 1e-05
I0413 15:45:32.128882 22206 solver.cpp:242] Iteration 9240 (0.266414 iter/s, 150.142s/40 iter), loss = -6.43825e-20
I0413 15:45:32.128963 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:45:32.128973 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:45:32.128984 22206 sgd_solver.cpp:106] Iteration 9240, lr = 1e-05
I0413 15:48:01.950661 22206 solver.cpp:242] Iteration 9280 (0.26698 iter/s, 149.824s/40 iter), loss = -6.43825e-20
I0413 15:48:01.950836 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:48:01.950848 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:48:01.950861 22206 sgd_solver.cpp:106] Iteration 9280, lr = 1e-05
I0413 15:50:31.726364 22206 solver.cpp:242] Iteration 9320 (0.267062 iter/s, 149.778s/40 iter), loss = -6.43825e-20
I0413 15:50:31.726516 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:50:31.726527 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:50:31.726542 22206 sgd_solver.cpp:106] Iteration 9320, lr = 1e-05
I0413 15:51:35.415482 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_9338.caffemodel
I0413 15:51:35.509249 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9338.solverstate
I0413 15:53:01.691311 22206 solver.cpp:242] Iteration 9360 (0.266725 iter/s, 149.967s/40 iter), loss = -6.43825e-20
I0413 15:53:01.691435 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:53:01.691445 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:53:01.691457 22206 sgd_solver.cpp:106] Iteration 9360, lr = 1e-05
I0413 15:55:31.599824 22206 solver.cpp:242] Iteration 9400 (0.266826 iter/s, 149.911s/40 iter), loss = -6.43825e-20
I0413 15:55:31.599951 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:55:31.599961 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:55:31.599973 22206 sgd_solver.cpp:106] Iteration 9400, lr = 1e-05
I0413 15:58:01.239950 22206 solver.cpp:242] Iteration 9440 (0.267304 iter/s, 149.642s/40 iter), loss = -6.43825e-20
I0413 15:58:01.240053 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 15:58:01.240063 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 15:58:01.240074 22206 sgd_solver.cpp:106] Iteration 9440, lr = 1e-05
I0413 16:00:31.107925 22206 solver.cpp:242] Iteration 9480 (0.266898 iter/s, 149.87s/40 iter), loss = -6.43825e-20
I0413 16:00:31.108005 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:00:31.108013 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:00:31.108026 22206 sgd_solver.cpp:106] Iteration 9480, lr = 1e-05
I0413 16:03:01.155339 22206 solver.cpp:242] Iteration 9520 (0.266578 iter/s, 150.05s/40 iter), loss = -6.43825e-20
I0413 16:03:01.155455 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:03:01.155465 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:03:01.155478 22206 sgd_solver.cpp:106] Iteration 9520, lr = 1e-05
I0413 16:05:31.172293 22206 solver.cpp:242] Iteration 9560 (0.266633 iter/s, 150.019s/40 iter), loss = -6.43825e-20
I0413 16:05:31.172405 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:05:31.172415 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:05:31.172428 22206 sgd_solver.cpp:106] Iteration 9560, lr = 1e-05
I0413 16:08:01.182947 22206 solver.cpp:242] Iteration 9600 (0.266644 iter/s, 150.013s/40 iter), loss = -6.43825e-20
I0413 16:08:01.183090 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:08:01.183101 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:08:01.183115 22206 sgd_solver.cpp:106] Iteration 9600, lr = 1e-06
I0413 16:10:31.031286 22206 solver.cpp:242] Iteration 9640 (0.266933 iter/s, 149.85s/40 iter), loss = -6.43825e-20
I0413 16:10:31.031422 22206 solver.cpp:261] Train net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:10:31.031432 22206 solver.cpp:261] Train net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:10:31.031445 22206 sgd_solver.cpp:106] Iteration 9640, lr = 1e-06
I0413 16:11:42.178346 22206 solver.cpp:479] Snapshotting to binary proto file snapshot_iter_9660.caffemodel
I0413 16:11:42.270309 22206 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9660.solverstate
I0413 16:11:42.342959 22206 solver.cpp:362] Iteration 9660, Testing net (#0)
I0413 16:11:42.342983 22206 net.cpp:723] Ignoring source layer train_data
I0413 16:11:42.342988 22206 net.cpp:723] Ignoring source layer train_label
I0413 16:11:42.342991 22206 net.cpp:723] Ignoring source layer train_transform
I0413 16:12:01.052938 22206 solver.cpp:429] Test net output #0: loss_bbox = 0 (* 2 = 0 loss)
I0413 16:12:01.052963 22206 solver.cpp:429] Test net output #1: loss_coverage = 0 (* 1 = 0 loss)
I0413 16:12:01.052969 22206 solver.cpp:429] Test net output #2: mAP = 0
I0413 16:12:01.052974 22206 solver.cpp:429] Test net output #3: precision = 0
I0413 16:12:01.052979 22206 solver.cpp:429] Test net output #4: recall = 0
I0413 16:12:01.052984 22206 solver.cpp:347] Optimization Done.
I0413 16:12:01.052987 22206 caffe.cpp:234] Optimization Done.

@PriyaKaza
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@shreyasramesh - I am currently in the same scenario. Did you manage to get it to train?

@MohammadMoradi
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Same problem. Any idea?

@firefox7025
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I am also having this issue. Commenting for visibility.

@ironhide23586
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I had the same issue, it turned out that my label files were incorrect (the bounding box co-ordinates were in the wrong places). I'd suggest you re-verify your label text files.

Thanks

@druedaplata
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had the same problem as @ironhide23586 check your label files, and also train for more epochs had an mAP at 0 for a long time until it rose

@ironhide23586
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For how many epochs was your mAP at 0 @druedaplata ?

@druedaplata
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Just around 10, more or less...
map

@ironhide23586
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Could you share exactly how you preprocessed your data? That's encouraging, thanks! :)

@druedaplata
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In that picture above, I used a dataset without augmentation of around 600 images. I used that picture to show how mAP does change after some epochs.

In a different one, using mirror and rotations, I got like 3000 images and mAP was 65% which is nice.

@ironhide23586
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So, did you resize the images by interpolation or by padding them? And that's pretty impressive! If its possible for you to share your pre-processed data and screenshots of the dataset creation page, that'd be a lifesaver.

Thanks!

@druedaplata
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I resize them outside digits with Imagemagick using mogrify to 640x640, i can't share data though.

Just in case, I had problems with mAP in 0 before for this reasons:

  • labels were not exactly in KITTI format or some values where in the wrong place Xmin where Xmax should be
  • the min size for a bbox label is 50x50 and max size is 400x400, if you need something different you should change detectnet params

@ironhide23586
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Thanks a lot! I'll give this a try...

@pinsondg pinsondg mentioned this issue Jun 26, 2018
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@luhongwei
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luhongwei commented Sep 4, 2018 •
edited

I pulled about 1000 pictures with boats from MS-COCO data set in order to train a model that recognizes boats. But on my DIGITS, after about 300 epochs, the conv1_7x7_s2 became so "pale" like in attached picture even through I had started training from a pre-trained model, detectnet(KITTI)
conv1_7x7_s2

I'm wondering if the training set was not good enough since I put small boats pictures in a generated 1024x768 background. like in attached
sourceimg

By the way, I did use Alps Labeling Tool to check the correctness of labels.

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