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yolo3 pane

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dl-desktop
2020-03-11 00:25:11 -03:00
parent efa14dd25a
commit 423774f21b
14 changed files with 710 additions and 1 deletions
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3
Result_yolo3_fault_4/.gitignore vendored Normal file
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result_otros/
result_yolo3_fault4/

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Result_yolo3_panel/.gitignore vendored Normal file
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result_otros/
log_experimento_panel_gpu/
*.jpg
*.h5
*.pkl

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Result_yolo3_panel/config_full_yolo_panel_train.json Executable file
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{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["panel"],
"backend": "keras-yolo3-master/full_yolo_backend.h5"
},
"train": {
"train_image_folder": "Train&Test_A/Train/images/",
"train_annot_folder": "Train&Test_A/Train/anns/",
"cache_name": "Result_yolo3_panel/train_panel.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-3,
"nb_epochs": 500,
"warmup_epochs": 15,
"ignore_thresh": 0.5,
"gpus": "0,1",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "Result_yolo3_panel/log_experimento_panel_gpu",
"saved_weights_name": "Result_yolo3_panel/yolo3_full_panel.h5",
"debug": true
},
"valid": {
"valid_image_folder": "Train&Test_A/Test/images/",
"valid_annot_folder": "Train&Test_A/Test/anns/",
"cache_name": "Result_yolo3_panel/val_panel.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "Train&Test_A/Test/images/",
"test_annot_folder": "Train&Test_A/Test/anns/",
"cache_name": "Result_yolo3_panel/test_panel.pkl",
"test_times": 1
}
}

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Result_yolo3_panel/yolo3_full_yolo.err Normal file
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Using TensorFlow backend.
WARNING:tensorflow:From /home/dl-desktop/anaconda3/envs/tf_gpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2020-03-10 23:09:26.611545: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2020-03-10 23:09:26.633983: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3199460000 Hz
2020-03-10 23:09:26.634773: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5596494f7760 executing computations on platform Host. Devices:
2020-03-10 23:09:26.634805: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
2020-03-10 23:09:26.774492: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-03-10 23:09:26.775159: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55964938fa80 executing computations on platform CUDA. Devices:
2020-03-10 23:09:26.775177: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): GeForce GTX 1060 6GB, Compute Capability 6.1
2020-03-10 23:09:26.775430: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7845
pciBusID: 0000:22:00.0
totalMemory: 5.93GiB freeMemory: 5.52GiB
2020-03-10 23:09:26.775444: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-03-10 23:09:26.776250: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-03-10 23:09:26.776259: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2020-03-10 23:09:26.776264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2020-03-10 23:09:26.776403: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5355 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:22:00.0, compute capability: 6.1)
WARNING:tensorflow:From /home/dl-desktop/Desktop/Rentadrone/model-definition/keras-yolo3-master/yolo.py:24: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
/home/dl-desktop/anaconda3/envs/tf_gpu/lib/python3.6/site-packages/keras/callbacks.py:1065: UserWarning: `epsilon` argument is deprecated and will be removed, use `min_delta` instead.
warnings.warn('`epsilon` argument is deprecated and '
WARNING:tensorflow:From /home/dl-desktop/anaconda3/envs/tf_gpu/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
/home/dl-desktop/anaconda3/envs/tf_gpu/lib/python3.6/site-packages/keras/engine/saving.py:292: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.
warnings.warn('No training configuration found in save file: '

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Result_yolo3_panel/yolo3_full_yolo.output Normal file
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Seen labels: {'panel': 437}
Given labels: ['panel']
Training on: ['panel']
multi_gpu:2
Epoch 1/515
- 25s - loss: 788.5168 - yolo_layer_1_loss: 87.5552 - yolo_layer_2_loss: 209.3693 - yolo_layer_3_loss: 491.5923
Epoch 00001: loss improved from inf to 788.51681, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 2/515
- 1s - loss: 504.9818 - yolo_layer_1_loss: 63.4004 - yolo_layer_2_loss: 135.5804 - yolo_layer_3_loss: 306.0011
Epoch 00002: loss improved from 788.51681 to 504.98183, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 3/515
- 1s - loss: 356.6464 - yolo_layer_1_loss: 52.1744 - yolo_layer_2_loss: 101.1793 - yolo_layer_3_loss: 203.2926
Epoch 00003: loss improved from 504.98183 to 356.64636, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 4/515
- 1s - loss: 260.2328 - yolo_layer_1_loss: 37.8181 - yolo_layer_2_loss: 70.3983 - yolo_layer_3_loss: 152.0164
Epoch 00004: loss improved from 356.64636 to 260.23276, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 5/515
- 1s - loss: 209.4845 - yolo_layer_1_loss: 30.3574 - yolo_layer_2_loss: 57.3208 - yolo_layer_3_loss: 121.8064
Epoch 00005: loss improved from 260.23276 to 209.48454, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 6/515
- 2s - loss: 170.4902 - yolo_layer_1_loss: 26.7990 - yolo_layer_2_loss: 49.1645 - yolo_layer_3_loss: 94.5267
Epoch 00006: loss improved from 209.48454 to 170.49025, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 7/515
- 1s - loss: 143.8027 - yolo_layer_1_loss: 21.3999 - yolo_layer_2_loss: 43.7319 - yolo_layer_3_loss: 78.6709
Epoch 00007: loss improved from 170.49025 to 143.80266, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 8/515
- 1s - loss: 122.2331 - yolo_layer_1_loss: 18.3355 - yolo_layer_2_loss: 41.0849 - yolo_layer_3_loss: 62.8127
Epoch 00008: loss improved from 143.80266 to 122.23310, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 9/515
- 1s - loss: 113.8142 - yolo_layer_1_loss: 18.9330 - yolo_layer_2_loss: 35.3822 - yolo_layer_3_loss: 59.4990
Epoch 00009: loss improved from 122.23310 to 113.81417, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 10/515
- 1s - loss: 105.8606 - yolo_layer_1_loss: 15.5346 - yolo_layer_2_loss: 35.5356 - yolo_layer_3_loss: 54.7904
Epoch 00010: loss improved from 113.81417 to 105.86061, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 11/515
- 1s - loss: 103.5702 - yolo_layer_1_loss: 15.8540 - yolo_layer_2_loss: 36.8180 - yolo_layer_3_loss: 50.8981
Epoch 00011: loss improved from 105.86061 to 103.57018, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 12/515
- 1s - loss: 100.7059 - yolo_layer_1_loss: 18.7113 - yolo_layer_2_loss: 35.5497 - yolo_layer_3_loss: 46.4449
Epoch 00012: loss improved from 103.57018 to 100.70586, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 13/515
- 1s - loss: 94.6036 - yolo_layer_1_loss: 16.1128 - yolo_layer_2_loss: 32.3577 - yolo_layer_3_loss: 46.1332
Epoch 00013: loss improved from 100.70586 to 94.60364, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 14/515
- 1s - loss: 88.3558 - yolo_layer_1_loss: 16.6243 - yolo_layer_2_loss: 32.7020 - yolo_layer_3_loss: 39.0295
Epoch 00014: loss improved from 94.60364 to 88.35584, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 15/515
- 1s - loss: 87.2751 - yolo_layer_1_loss: 12.9079 - yolo_layer_2_loss: 32.8498 - yolo_layer_3_loss: 41.5175
Epoch 00015: loss improved from 88.35584 to 87.27512, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 16/515
- 1s - loss: 71.0853 - yolo_layer_1_loss: 8.6677 - yolo_layer_2_loss: 32.2301 - yolo_layer_3_loss: 30.1874
Epoch 00016: loss improved from 87.27512 to 71.08526, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 17/515
- 1s - loss: 68.7827 - yolo_layer_1_loss: 7.9907 - yolo_layer_2_loss: 29.9023 - yolo_layer_3_loss: 30.8898
Epoch 00017: loss improved from 71.08526 to 68.78272, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 18/515
- 1s - loss: 62.3005 - yolo_layer_1_loss: 7.7892 - yolo_layer_2_loss: 32.4176 - yolo_layer_3_loss: 22.0937
Epoch 00018: loss improved from 68.78272 to 62.30050, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 19/515
- 1s - loss: 62.9532 - yolo_layer_1_loss: 8.2018 - yolo_layer_2_loss: 29.9542 - yolo_layer_3_loss: 24.7972
Epoch 00019: loss did not improve from 62.30050
Epoch 20/515
- 1s - loss: 67.6720 - yolo_layer_1_loss: 9.1437 - yolo_layer_2_loss: 40.8387 - yolo_layer_3_loss: 17.6896
Epoch 00020: loss did not improve from 62.30050
Epoch 21/515
- 1s - loss: 65.4837 - yolo_layer_1_loss: 5.5533 - yolo_layer_2_loss: 37.4578 - yolo_layer_3_loss: 22.4726
Epoch 00021: loss did not improve from 62.30050
Epoch 22/515
- 1s - loss: 63.9490 - yolo_layer_1_loss: 5.9434 - yolo_layer_2_loss: 27.6707 - yolo_layer_3_loss: 30.3349
Epoch 00022: loss did not improve from 62.30050
Epoch 23/515
- 1s - loss: 60.8638 - yolo_layer_1_loss: 6.2403 - yolo_layer_2_loss: 24.1705 - yolo_layer_3_loss: 30.4530
Epoch 00023: loss improved from 62.30050 to 60.86383, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 24/515
- 2s - loss: 65.0853 - yolo_layer_1_loss: 8.4323 - yolo_layer_2_loss: 29.4372 - yolo_layer_3_loss: 27.2158
Epoch 00024: loss did not improve from 60.86383
Epoch 25/515
- 1s - loss: 57.1795 - yolo_layer_1_loss: 5.8742 - yolo_layer_2_loss: 33.2143 - yolo_layer_3_loss: 18.0910
Epoch 00025: loss improved from 60.86383 to 57.17952, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 26/515
- 1s - loss: 46.0742 - yolo_layer_1_loss: 8.4130 - yolo_layer_2_loss: 23.9355 - yolo_layer_3_loss: 13.7257
Epoch 00026: loss improved from 57.17952 to 46.07423, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 27/515
- 1s - loss: 54.5603 - yolo_layer_1_loss: 6.8313 - yolo_layer_2_loss: 26.2262 - yolo_layer_3_loss: 21.5029
Epoch 00027: loss did not improve from 46.07423
Epoch 28/515
- 1s - loss: 50.9116 - yolo_layer_1_loss: 5.6319 - yolo_layer_2_loss: 26.4363 - yolo_layer_3_loss: 18.8434
Epoch 00028: loss did not improve from 46.07423
Epoch 29/515
- 1s - loss: 67.2359 - yolo_layer_1_loss: 4.3374 - yolo_layer_2_loss: 44.0778 - yolo_layer_3_loss: 18.8207
Epoch 00029: loss did not improve from 46.07423
Epoch 30/515
- 1s - loss: 55.2418 - yolo_layer_1_loss: 4.6800 - yolo_layer_2_loss: 29.1531 - yolo_layer_3_loss: 21.4088
Epoch 00030: loss did not improve from 46.07423
Epoch 31/515
- 1s - loss: 49.5987 - yolo_layer_1_loss: 4.3250 - yolo_layer_2_loss: 27.4955 - yolo_layer_3_loss: 17.7781
Epoch 00031: loss did not improve from 46.07423
Epoch 32/515
- 1s - loss: 37.4884 - yolo_layer_1_loss: 0.3195 - yolo_layer_2_loss: 20.5042 - yolo_layer_3_loss: 16.6647
Epoch 00032: loss improved from 46.07423 to 37.48843, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 33/515
- 1s - loss: 44.6989 - yolo_layer_1_loss: 1.8672 - yolo_layer_2_loss: 17.2911 - yolo_layer_3_loss: 25.5406
Epoch 00033: loss did not improve from 37.48843
Epoch 34/515
- 1s - loss: 38.6178 - yolo_layer_1_loss: 1.8660 - yolo_layer_2_loss: 18.3823 - yolo_layer_3_loss: 18.3694
Epoch 00034: loss did not improve from 37.48843
Epoch 35/515
- 1s - loss: 43.7057 - yolo_layer_1_loss: 8.0134 - yolo_layer_2_loss: 17.5727 - yolo_layer_3_loss: 18.1196
Epoch 00035: loss did not improve from 37.48843
Epoch 36/515
- 1s - loss: 62.5768 - yolo_layer_1_loss: 1.7798 - yolo_layer_2_loss: 28.3934 - yolo_layer_3_loss: 32.4037
Epoch 00036: loss did not improve from 37.48843
Epoch 37/515
- 1s - loss: 73.5258 - yolo_layer_1_loss: 3.4326 - yolo_layer_2_loss: 51.4524 - yolo_layer_3_loss: 18.6408
Epoch 00037: loss did not improve from 37.48843
Epoch 38/515
- 1s - loss: 38.3015 - yolo_layer_1_loss: 2.4276 - yolo_layer_2_loss: 17.7967 - yolo_layer_3_loss: 18.0771
Epoch 00038: loss did not improve from 37.48843
Epoch 39/515
- 1s - loss: 44.9760 - yolo_layer_1_loss: 6.3320 - yolo_layer_2_loss: 17.1594 - yolo_layer_3_loss: 21.4846
Epoch 00039: loss did not improve from 37.48843
Epoch 40/515
- 1s - loss: 45.5982 - yolo_layer_1_loss: 4.1392 - yolo_layer_2_loss: 21.4428 - yolo_layer_3_loss: 20.0161
Epoch 00040: loss did not improve from 37.48843
Epoch 41/515
- 1s - loss: 43.7091 - yolo_layer_1_loss: 8.5827 - yolo_layer_2_loss: 22.6787 - yolo_layer_3_loss: 12.4476
Epoch 00041: loss did not improve from 37.48843
Epoch 42/515
- 1s - loss: 45.8335 - yolo_layer_1_loss: 4.0810 - yolo_layer_2_loss: 22.8724 - yolo_layer_3_loss: 18.8801
Epoch 00042: loss did not improve from 37.48843
Epoch 43/515
- 1s - loss: 31.9653 - yolo_layer_1_loss: 0.0709 - yolo_layer_2_loss: 17.7897 - yolo_layer_3_loss: 14.1048
Epoch 00043: loss improved from 37.48843 to 31.96534, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 44/515
- 1s - loss: 36.0202 - yolo_layer_1_loss: 4.0964 - yolo_layer_2_loss: 17.7335 - yolo_layer_3_loss: 14.1903
Epoch 00044: loss did not improve from 31.96534
Epoch 45/515
- 1s - loss: 40.4280 - yolo_layer_1_loss: 9.6541 - yolo_layer_2_loss: 17.5647 - yolo_layer_3_loss: 13.2092
Epoch 00045: loss did not improve from 31.96534
Epoch 46/515
- 1s - loss: 41.8808 - yolo_layer_1_loss: 8.0392 - yolo_layer_2_loss: 17.5653 - yolo_layer_3_loss: 16.2764
Epoch 00046: loss did not improve from 31.96534
Epoch 47/515
- 1s - loss: 38.5636 - yolo_layer_1_loss: 5.8415 - yolo_layer_2_loss: 17.6460 - yolo_layer_3_loss: 15.0761
Epoch 00047: loss did not improve from 31.96534
Epoch 48/515
- 1s - loss: 32.4542 - yolo_layer_1_loss: 0.0745 - yolo_layer_2_loss: 14.5561 - yolo_layer_3_loss: 17.8236
Epoch 00048: loss did not improve from 31.96534
Epoch 49/515
- 1s - loss: 45.5076 - yolo_layer_1_loss: 9.9762 - yolo_layer_2_loss: 20.7552 - yolo_layer_3_loss: 14.7761
Epoch 00049: loss did not improve from 31.96534
Epoch 50/515
- 1s - loss: 26.3506 - yolo_layer_1_loss: 0.0744 - yolo_layer_2_loss: 12.4786 - yolo_layer_3_loss: 13.7976
Epoch 00050: loss improved from 31.96534 to 26.35056, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 51/515
- 1s - loss: 36.9198 - yolo_layer_1_loss: 2.9727 - yolo_layer_2_loss: 16.5861 - yolo_layer_3_loss: 17.3610
Epoch 00051: loss did not improve from 26.35056
Epoch 52/515
- 1s - loss: 31.7385 - yolo_layer_1_loss: 4.8504 - yolo_layer_2_loss: 12.5606 - yolo_layer_3_loss: 14.3275
Epoch 00052: loss did not improve from 26.35056
Epoch 53/515
- 1s - loss: 39.2903 - yolo_layer_1_loss: 4.2159 - yolo_layer_2_loss: 15.0361 - yolo_layer_3_loss: 20.0383
Epoch 00053: loss did not improve from 26.35056
Epoch 54/515
- 1s - loss: 47.4222 - yolo_layer_1_loss: 11.2283 - yolo_layer_2_loss: 15.8715 - yolo_layer_3_loss: 20.3223
Epoch 00054: loss did not improve from 26.35056
Epoch 55/515
- 1s - loss: 44.5798 - yolo_layer_1_loss: 10.6720 - yolo_layer_2_loss: 15.0779 - yolo_layer_3_loss: 18.8299
Epoch 00055: loss did not improve from 26.35056
Epoch 56/515
- 1s - loss: 31.5055 - yolo_layer_1_loss: 2.9636 - yolo_layer_2_loss: 13.8120 - yolo_layer_3_loss: 14.7300
Epoch 00056: loss did not improve from 26.35056
Epoch 57/515
- 1s - loss: 45.6505 - yolo_layer_1_loss: 3.0302 - yolo_layer_2_loss: 25.9562 - yolo_layer_3_loss: 16.6641
Epoch 00057: loss did not improve from 26.35056
Epoch 58/515
- 1s - loss: 32.1284 - yolo_layer_1_loss: 3.4196 - yolo_layer_2_loss: 16.2056 - yolo_layer_3_loss: 12.5032
Epoch 00058: loss did not improve from 26.35056
Epoch 59/515
- 1s - loss: 41.3005 - yolo_layer_1_loss: 6.1761 - yolo_layer_2_loss: 15.2596 - yolo_layer_3_loss: 19.8648
Epoch 00059: loss did not improve from 26.35056
Epoch 60/515
- 1s - loss: 32.2059 - yolo_layer_1_loss: 6.4127 - yolo_layer_2_loss: 12.1855 - yolo_layer_3_loss: 13.6077
Epoch 00060: loss did not improve from 26.35056
Epoch 61/515
- 1s - loss: 27.8015 - yolo_layer_1_loss: 3.4488 - yolo_layer_2_loss: 12.7568 - yolo_layer_3_loss: 11.5959
Epoch 00061: loss did not improve from 26.35056
Epoch 62/515
- 1s - loss: 33.8720 - yolo_layer_1_loss: 5.4526 - yolo_layer_2_loss: 13.9204 - yolo_layer_3_loss: 14.4990
Epoch 00062: loss did not improve from 26.35056
Epoch 63/515
- 1s - loss: 35.2335 - yolo_layer_1_loss: 5.4847 - yolo_layer_2_loss: 14.5669 - yolo_layer_3_loss: 15.1819
Epoch 00063: loss did not improve from 26.35056
Epoch 64/515
- 1s - loss: 29.2284 - yolo_layer_1_loss: 1.7453 - yolo_layer_2_loss: 12.6682 - yolo_layer_3_loss: 14.8148
Epoch 00064: loss did not improve from 26.35056
Epoch 65/515
- 1s - loss: 32.8887 - yolo_layer_1_loss: 7.1504 - yolo_layer_2_loss: 13.9416 - yolo_layer_3_loss: 11.7968
Epoch 00065: loss did not improve from 26.35056
Epoch 00065: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.
Epoch 66/515
- 1s - loss: 30.6386 - yolo_layer_1_loss: 7.4096 - yolo_layer_2_loss: 11.9253 - yolo_layer_3_loss: 11.3036
Epoch 00066: loss did not improve from 26.35056
Epoch 67/515
- 1s - loss: 34.6849 - yolo_layer_1_loss: 8.5647 - yolo_layer_2_loss: 12.7178 - yolo_layer_3_loss: 13.4024
Epoch 00067: loss did not improve from 26.35056
Epoch 68/515
- 1s - loss: 34.8349 - yolo_layer_1_loss: 5.3199 - yolo_layer_2_loss: 12.9783 - yolo_layer_3_loss: 16.5366
Epoch 00068: loss did not improve from 26.35056
Epoch 69/515
- 1s - loss: 34.7599 - yolo_layer_1_loss: 11.6977 - yolo_layer_2_loss: 12.3417 - yolo_layer_3_loss: 10.7205
Epoch 00069: loss did not improve from 26.35056
Epoch 70/515
- 1s - loss: 21.3366 - yolo_layer_1_loss: 0.0459 - yolo_layer_2_loss: 9.8588 - yolo_layer_3_loss: 11.4318
Epoch 00070: loss improved from 26.35056 to 21.33656, saving model to Result_yolo3_panel/yolo3_full_panel.h5
Epoch 71/515
- 1s - loss: 26.8873 - yolo_layer_1_loss: 4.2104 - yolo_layer_2_loss: 10.8009 - yolo_layer_3_loss: 11.8760
Epoch 00071: loss did not improve from 21.33656
Epoch 72/515
- 1s - loss: 25.8545 - yolo_layer_1_loss: 5.8853 - yolo_layer_2_loss: 11.1242 - yolo_layer_3_loss: 8.8450
Epoch 00072: loss did not improve from 21.33656
Epoch 73/515
- 1s - loss: 27.0369 - yolo_layer_1_loss: 6.7487 - yolo_layer_2_loss: 10.0174 - yolo_layer_3_loss: 10.2707
Epoch 00073: loss did not improve from 21.33656
Epoch 74/515
- 1s - loss: 29.8378 - yolo_layer_1_loss: 9.2319 - yolo_layer_2_loss: 10.3877 - yolo_layer_3_loss: 10.2182
Epoch 00074: loss did not improve from 21.33656
Epoch 75/515
- 1s - loss: 25.7785 - yolo_layer_1_loss: 2.4645 - yolo_layer_2_loss: 9.9619 - yolo_layer_3_loss: 13.3522
Epoch 00075: loss did not improve from 21.33656
Epoch 76/515
- 1s - loss: 24.1168 - yolo_layer_1_loss: 5.1117 - yolo_layer_2_loss: 10.4667 - yolo_layer_3_loss: 8.5384
Epoch 00076: loss did not improve from 21.33656
Epoch 77/515
- 1s - loss: 42.0458 - yolo_layer_1_loss: 6.3775 - yolo_layer_2_loss: 26.4004 - yolo_layer_3_loss: 9.2679
Epoch 00077: loss did not improve from 21.33656
Epoch 78/515
- 1s - loss: 33.3634 - yolo_layer_1_loss: 5.4202 - yolo_layer_2_loss: 12.1631 - yolo_layer_3_loss: 15.7801
Epoch 00078: loss did not improve from 21.33656
Epoch 79/515
- 1s - loss: 24.2920 - yolo_layer_1_loss: 2.4456 - yolo_layer_2_loss: 9.3274 - yolo_layer_3_loss: 12.5190
Epoch 00079: loss did not improve from 21.33656
Epoch 80/515
- 1s - loss: 24.7078 - yolo_layer_1_loss: 5.8791 - yolo_layer_2_loss: 10.1861 - yolo_layer_3_loss: 8.6425
Epoch 00080: loss did not improve from 21.33656
Epoch 81/515
- 1s - loss: 22.3276 - yolo_layer_1_loss: 2.9378 - yolo_layer_2_loss: 9.8449 - yolo_layer_3_loss: 9.5449
Epoch 00081: loss did not improve from 21.33656
Epoch 82/515
- 1s - loss: 27.2679 - yolo_layer_1_loss: 2.9763 - yolo_layer_2_loss: 11.4190 - yolo_layer_3_loss: 12.8726
Epoch 00082: loss did not improve from 21.33656
Epoch 83/515
- 1s - loss: 33.1467 - yolo_layer_1_loss: 5.8364 - yolo_layer_2_loss: 17.8746 - yolo_layer_3_loss: 9.4357
Epoch 00083: loss did not improve from 21.33656
Epoch 84/515
- 1s - loss: 27.7957 - yolo_layer_1_loss: 4.1142 - yolo_layer_2_loss: 11.9726 - yolo_layer_3_loss: 11.7089
Epoch 00084: loss did not improve from 21.33656
Epoch 85/515
- 1s - loss: 26.8175 - yolo_layer_1_loss: 6.6295 - yolo_layer_2_loss: 11.1890 - yolo_layer_3_loss: 8.9989
Epoch 00085: loss did not improve from 21.33656
Epoch 00085: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
Epoch 86/515
- 1s - loss: 24.0317 - yolo_layer_1_loss: 2.9722 - yolo_layer_2_loss: 10.2039 - yolo_layer_3_loss: 10.8555
Epoch 00086: loss did not improve from 21.33656
Epoch 87/515
- 1s - loss: 27.8479 - yolo_layer_1_loss: 8.7551 - yolo_layer_2_loss: 11.2185 - yolo_layer_3_loss: 7.8743
Epoch 00087: loss did not improve from 21.33656
Epoch 88/515
- 1s - loss: 25.3220 - yolo_layer_1_loss: 5.8200 - yolo_layer_2_loss: 8.7615 - yolo_layer_3_loss: 10.7404
Epoch 00088: loss did not improve from 21.33656
Epoch 89/515
- 1s - loss: 26.4045 - yolo_layer_1_loss: 6.4138 - yolo_layer_2_loss: 8.4404 - yolo_layer_3_loss: 11.5503
Epoch 00089: loss did not improve from 21.33656
Epoch 90/515
- 1s - loss: 28.3891 - yolo_layer_1_loss: 10.1978 - yolo_layer_2_loss: 9.8820 - yolo_layer_3_loss: 8.3093
Epoch 00090: loss did not improve from 21.33656
Epoch 91/515
- 1s - loss: 22.0173 - yolo_layer_1_loss: 6.1661 - yolo_layer_2_loss: 8.0216 - yolo_layer_3_loss: 7.8296
Epoch 00091: loss did not improve from 21.33656
Epoch 92/515
- 1s - loss: 27.9332 - yolo_layer_1_loss: 8.3314 - yolo_layer_2_loss: 10.9083 - yolo_layer_3_loss: 8.6935
Epoch 00092: loss did not improve from 21.33656
Epoch 93/515
- 1s - loss: 25.7863 - yolo_layer_1_loss: 8.8430 - yolo_layer_2_loss: 9.4397 - yolo_layer_3_loss: 7.5037
Epoch 00093: loss did not improve from 21.33656
Epoch 94/515
- 1s - loss: 24.2174 - yolo_layer_1_loss: 3.3887 - yolo_layer_2_loss: 8.9126 - yolo_layer_3_loss: 11.9161
Epoch 00094: loss did not improve from 21.33656
Epoch 95/515
- 1s - loss: 29.5621 - yolo_layer_1_loss: 10.5950 - yolo_layer_2_loss: 7.8444 - yolo_layer_3_loss: 11.1227
Epoch 00095: loss did not improve from 21.33656
Epoch 00095: early stopping
118 instances of class panel with average precision: 0.8625
mAP using the weighted average of precisions among classes: 0.8625
mAP: 0.8625

22
Result_yolo3_panel/yolo3_full_yolo_test.err Normal file
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Using TensorFlow backend.
WARNING:tensorflow:From /home/dl-desktop/anaconda3/envs/tf_gpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2020-03-10 23:14:56.951487: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2020-03-10 23:14:56.973302: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3199460000 Hz
2020-03-10 23:14:56.975039: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x564a6b9b61c0 executing computations on platform Host. Devices:
2020-03-10 23:14:56.975089: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
2020-03-10 23:14:57.074882: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-03-10 23:14:57.076194: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x564a6b2a3d30 executing computations on platform CUDA. Devices:
2020-03-10 23:14:57.076252: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): GeForce GTX 1060 6GB, Compute Capability 6.1
2020-03-10 23:14:57.076780: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7845
pciBusID: 0000:22:00.0
totalMemory: 5.93GiB freeMemory: 5.45GiB
2020-03-10 23:14:57.076809: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-03-10 23:14:57.078242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-03-10 23:14:57.078269: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2020-03-10 23:14:57.078282: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2020-03-10 23:14:57.078583: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5281 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:22:00.0, compute capability: 6.1)
/home/dl-desktop/anaconda3/envs/tf_gpu/lib/python3.6/site-packages/keras/engine/saving.py:292: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.
warnings.warn('No training configuration found in save file: '

4
Result_yolo3_panel/yolo3_full_yolo_test.output Normal file
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dict_items([(0, (0.8603589799894383, 118.0))])
118 instances of class panel with average precision: 0.8604
mAP using the weighted average of precisions among classes: 0.8604
mAP: 0.8604

49
config_full_yolo_panel_train.json Executable file
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{
"model" : {
"min_input_size": 400,
"max_input_size": 400,
"anchors": [5,7, 10,14, 15, 15, 26,32, 45,119, 54,18, 94,59, 109,183, 200,21],
"labels": ["panel"],
"backend": "keras-yolo3-master/full_yolo_backend.h5"
},
"train": {
"train_image_folder": "Train&Test_A/Train/images/",
"train_annot_folder": "Train&Test_A/Train/anns/",
"cache_name": "Result_yolo3_panel/train_panel.pkl",
"train_times": 1,
"batch_size": 2,
"learning_rate": 1e-3,
"nb_epochs": 500,
"warmup_epochs": 15,
"ignore_thresh": 0.5,
"gpus": "0,1",
"grid_scales": [1,1,1],
"obj_scale": 5,
"noobj_scale": 1,
"xywh_scale": 1,
"class_scale": 1,
"tensorboard_dir": "Result_yolo3_panel/log_experimento_panel_gpu",
"saved_weights_name": "Result_yolo3_panel/yolo3_full_panel.h5",
"debug": true
},
"valid": {
"valid_image_folder": "Train&Test_A/Test/images/",
"valid_annot_folder": "Train&Test_A/Test/anns/",
"cache_name": "Result_yolo3_panel/val_panel.pkl",
"valid_times": 1
},
"test": {
"test_image_folder": "Train&Test_A/Test/images/",
"test_annot_folder": "Train&Test_A/Test/anns/",
"cache_name": "Result_yolo3_panel/test_panel.pkl",
"test_times": 1
}
}

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predict_yolo3.py
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@@ -30,7 +30,7 @@ def _main_(args):
# Set some parameter
###############################
net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster
obj_thresh, nms_thresh = 0.5, 0.45
obj_thresh, nms_thresh = 0.8, 0.3
###############################
# Load the model

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predict_yolo3_disconnect.py Executable file
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#! /usr/bin/env python
import time
import os
import argparse
import json
import cv2
import sys
sys.path += [os.path.abspath('keras-yolo3-master')]
from utils.utils import get_yolo_boxes, makedirs
from utils.bbox import draw_boxes
from keras.models import load_model
from tqdm import tqdm
import numpy as np
def _main_(args):
config_path = args.conf
input_path = args.input
output_path = args.output
with open(config_path) as config_buffer:
config = json.load(config_buffer)
makedirs(output_path)
###############################
# Set some parameter
###############################
net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster
obj_thresh, nms_thresh = 0.8, 0.3
###############################
# Load the model
###############################
os.environ['CUDA_VISIBLE_DEVICES'] = config['train']['gpus']
infer_model = load_model(config['train']['saved_weights_name'])
###############################
# Predict bounding boxes
###############################
if 'webcam' in input_path: # do detection on the first webcam
video_reader = cv2.VideoCapture(0)
# the main loop
batch_size = 1
images = []
while True:
ret_val, image = video_reader.read()
if ret_val == True: images += [image]
if (len(images)==batch_size) or (ret_val==False and len(images)>0):
batch_boxes = get_yolo_boxes(infer_model, images, net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)
for i in range(len(images)):
draw_boxes(images[i], batch_boxes[i], config['model']['labels'], obj_thresh)
cv2.imshow('video with bboxes', images[i])
images = []
if cv2.waitKey(1) == 27:
break # esc to quit
cv2.destroyAllWindows()
elif input_path[-4:] == '.mp4': # do detection on a video
video_out = output_path + input_path.split('/')[-1]
video_reader = cv2.VideoCapture(input_path)
nb_frames = int(video_reader.get(cv2.CAP_PROP_FRAME_COUNT))
frame_h = int(video_reader.get(cv2.CAP_PROP_FRAME_HEIGHT))
frame_w = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))
video_writer = cv2.VideoWriter(video_out,
cv2.VideoWriter_fourcc(*'MPEG'),
50.0,
(frame_w, frame_h))
# the main loop
batch_size = 1
images = []
start_point = 0 #%
show_window = False
for i in tqdm(range(nb_frames)):
_, image = video_reader.read()
if (float(i+1)/nb_frames) > start_point/100.:
images += [image]
if (i%batch_size == 0) or (i == (nb_frames-1) and len(images) > 0):
# predict the bounding boxes
batch_boxes = get_yolo_boxes(infer_model, images, net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)
for i in range(len(images)):
# draw bounding boxes on the image using labels
draw_boxes(images[i], batch_boxes[i], config['model']['labels'], obj_thresh)
# show the video with detection bounding boxes
if show_window: cv2.imshow('video with bboxes', images[i])
# write result to the output video
video_writer.write(images[i])
images = []
if show_window and cv2.waitKey(1) == 27: break # esc to quit
if show_window: cv2.destroyAllWindows()
video_reader.release()
video_writer.release()
else: # do detection on an image or a set of images
image_paths = []
if os.path.isdir(input_path):
for inp_file in os.listdir(input_path):
image_paths += [input_path + inp_file]
else:
image_paths += [input_path]
image_paths = [inp_file for inp_file in image_paths if (inp_file[-4:] in ['.jpg', '.png', 'JPEG'])]
# the main loop
times = []
for image_path in image_paths:
image = cv2.imread(image_path)
print(image_path)
start = time.time()
# predict the bounding boxes
boxes = get_yolo_boxes(infer_model, [image], net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)[0]
print('Elapsed time = {}'.format(time.time() - start))
times.append(time.time() - start)
# draw bounding boxes on the image using labels
for box in boxes:
draw_boxes(image, boxes, config['model']['labels'], obj_thresh)
# write the image with bounding boxes to file
cv2.imwrite(output_path + image_path.split('/')[-1], np.uint8(image))
file = open(args.output + '/time.txt','w')
file.write('Tiempo promedio:' + str(np.mean(times)))
file.close()
if __name__ == '__main__':
argparser = argparse.ArgumentParser(description='Predict with a trained yolo model')
argparser.add_argument('-c', '--conf', help='path to configuration file')
argparser.add_argument('-i', '--input', help='path to an image, a directory of images, a video, or webcam')
argparser.add_argument('-o', '--output', default='output/', help='path to output directory')
args = argparser.parse_args()
_main_(args)