yolo3 pane

Result_yolo3_fault_4/.gitignore

@@ -0,0 +1,3 @@
+result_otros/
+result_yolo3_fault4/
+

Result_yolo3_panel/.gitignore

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+result_otros/
+log_experimento_panel_gpu/
+
+*.jpg
+*.h5
+*.pkl

Result_yolo3_panel/config_full_yolo_panel_train.json

@@ -0,0 +1,49 @@
+{
+    "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
+    }
+}

Result_yolo3_panel/yolo3_full_yolo.err

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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: '

Result_yolo3_panel/yolo3_full_yolo.output

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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

Result_yolo3_panel/yolo3_full_yolo_test.err

@@ -0,0 +1,22 @@
+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: '

Result_yolo3_panel/yolo3_full_yolo_test.output

@@ -0,0 +1,4 @@
+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

config_full_yolo_panel_train.json

@@ -0,0 +1,49 @@
+{
+    "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
+    }
+}

predict_yolo3.py

@@ -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

predict_yolo3_disconnect.py

@@ -0,0 +1,151 @@
+#! /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)