yolov3 back to its original space
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lr0: 0.01
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lrf: 0.1
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momentum: 0.937
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weight_decay: 0.0005
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warmup_epochs: 3.0
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warmup_momentum: 0.8
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warmup_bias_lr: 0.1
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box: 0.05
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cls: 0.5
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cls_pw: 1.0
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obj: 1.0
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obj_pw: 1.0
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iou_t: 0.2
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anchor_t: 4.0
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fl_gamma: 0.0
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hsv_h: 0.015
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hsv_s: 0.7
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hsv_v: 0.4
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degrees: 0.0
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translate: 0.1
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scale: 0.5
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shear: 0.0
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perspective: 0.0
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flipud: 0.0
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fliplr: 0.5
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mosaic: 1.0
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mixup: 0.0
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copy_paste: 0.0
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weights: ''
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cfg: /content/gdrive/MyDrive/yolov3/models/yolov3.yaml
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data: data/custom-yolov3.yaml
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hyp: data/hyps/hyp.scratch.yaml
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epochs: 300
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batch_size: 16
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imgsz: 1280
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rect: false
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resume: false
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nosave: false
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noval: false
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noautoanchor: false
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evolve: null
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bucket: ''
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cache: null
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image_weights: false
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device: ''
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multi_scale: false
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single_cls: false
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adam: false
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sync_bn: false
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workers: 8
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project: runs/train
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name: exp
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exist_ok: false
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quad: false
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linear_lr: false
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label_smoothing: 0.0
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patience: 100
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freeze: 0
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save_period: -1
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local_rank: -1
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entity: null
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upload_dataset: false
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bbox_interval: -1
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artifact_alias: latest
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save_dir: runs/train/exp15
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@ -1,301 +0,0 @@
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epoch, train/box_loss, train/obj_loss, train/cls_loss, metrics/precision, metrics/recall, metrics/mAP_0.5,metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss, x/lr0, x/lr1, x/lr2
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0, 0.1072, 0.089784, 0.045148, 3.1447e-05, 0.009434, 1.7021e-05, 1.7021e-06, 0.074383, 0.071557, 0.036102, 0.00019, 0.00019, 0.09829
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1, 0.09357, 0.073808, 0.04392, 6.2893e-05, 0.018868, 3.5032e-05, 6.2107e-06, 0.069977, 0.066751, 0.035672, 0.00038999, 0.00038999, 0.09649
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2, 0.082933, 0.073286, 0.042061, 6.2893e-05, 0.018868, 3.5252e-05, 3.5252e-06, 0.067869, 0.065488, 0.035208, 0.00058994, 0.00058994, 0.09469
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3, 0.078388, 0.073047, 0.042215, 9.434e-05, 0.028302, 5.6751e-05, 5.6751e-06, 0.067291, 0.064494, 0.034919, 0.00078982, 0.00078982, 0.09289
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4, 0.077606, 0.066943, 0.040052, 9.434e-05, 0.028302, 5.7173e-05, 9.217e-06, 0.065971, 0.064303, 0.034521, 0.00098961, 0.00098961, 0.09109
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5, 0.073009, 0.072096, 0.039418, 0.0010693, 0.082038, 0.00067087, 0.00010067, 0.065407, 0.064251, 0.034196, 0.0011893, 0.0011893, 0.089289
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6, 0.074801, 0.071712, 0.039586, 0.0010178, 0.16158, 0.00093658, 0.00021294, 0.065921, 0.063458, 0.033979, 0.0013888, 0.0013888, 0.087489
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7, 0.076127, 0.0702, 0.039439, 0.00084618, 0.17629, 0.0011685, 0.00023231, 0.065714, 0.063461, 0.033674, 0.0015881, 0.0015881, 0.085688
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8, 0.075849, 0.074078, 0.038786, 0.0092778, 0.030889, 0.0032644, 0.00070873, 0.065679, 0.062307, 0.030542, 0.0017872, 0.0017872, 0.083887
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9, 0.075436, 0.069392, 0.038983, 0.0060257, 0.10055, 0.0033271, 0.00079476, 0.064516, 0.062611, 0.030316, 0.001986, 0.001986, 0.082086
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10, 0.077201, 0.071009, 0.035982, 0.015542, 0.070484, 0.005924, 0.0010521, 0.070791, 0.061645, 0.026312, 0.0021846, 0.0021846, 0.080285
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11, 0.07411, 0.067135, 0.036761, 0.54391, 0.014074, 0.0064732, 0.0012823, 0.079597, 0.077907, 0.033935, 0.0023829, 0.0023829, 0.078483
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12, 0.072703, 0.066995, 0.036249, 0.015042, 0.15315, 0.0083551, 0.0012464, 0.075651, 0.067622, 0.028427, 0.0025808, 0.0025808, 0.076681
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13, 0.0737, 0.072585, 0.03594, 0.038881, 0.15844, 0.020356, 0.0033538, 0.063492, 0.061259, 0.026789, 0.0027784, 0.0027784, 0.074878
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14, 0.072014, 0.072332, 0.036173, 0.036296, 0.10895, 0.018669, 0.003782, 0.061362, 0.061768, 0.026689, 0.0029756, 0.0029756, 0.073076
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15, 0.072346, 0.071417, 0.033542, 0.090208, 0.14203, 0.0267, 0.0043627, 0.062477, 0.06224, 0.024864, 0.0031723, 0.0031723, 0.071272
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16, 0.070106, 0.068058, 0.034502, 0.067812, 0.14637, 0.037359, 0.0078725, 0.06237, 0.061756, 0.02467, 0.0033686, 0.0033686, 0.069469
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17, 0.074946, 0.064136, 0.032574, 0.13835, 0.12299, 0.072165, 0.012966, 0.058928, 0.05861, 0.024038, 0.0035645, 0.0035645, 0.067664
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18, 0.070358, 0.060051, 0.031907, 0.0479, 0.25203, 0.038178, 0.0060513, 0.05915, 0.058207, 0.025925, 0.0037598, 0.0037598, 0.06586
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19, 0.067322, 0.055626, 0.03065, 0.82252, 0.060739, 0.063727, 0.012951, 0.065587, 0.053063, 0.021251, 0.0039546, 0.0039546, 0.064055
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20, 0.068843, 0.057321, 0.030782, 0.18588, 0.17522, 0.10052, 0.0231, 0.057125, 0.052086, 0.021309, 0.0041488, 0.0041488, 0.062249
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21, 0.06779, 0.056333, 0.030423, 0.14705, 0.2448, 0.14866, 0.026988, 0.057788, 0.054503, 0.020962, 0.0043424, 0.0043424, 0.060442
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22, 0.065426, 0.052589, 0.030872, 0.31674, 0.30232, 0.25582, 0.068044, 0.051682, 0.048209, 0.020117, 0.0045354, 0.0045354, 0.058635
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23, 0.067626, 0.056669, 0.027687, 0.10589, 0.17271, 0.068596, 0.015701, 0.064839, 0.051478, 0.020254, 0.0047278, 0.0047278, 0.056828
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24, 0.059718, 0.052035, 0.026036, 0.42306, 0.42077, 0.40239, 0.11784, 0.055993, 0.041789, 0.015245, 0.0049195, 0.0049195, 0.055019
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25, 0.066427, 0.060109, 0.026517, 0.21072, 0.40616, 0.23959, 0.044691, 0.060816, 0.050553, 0.017379, 0.0051104, 0.0051104, 0.05321
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26, 0.068186, 0.057706, 0.026478, 0.14783, 0.51256, 0.19894, 0.060068, 0.057491, 0.04908, 0.018394, 0.0053007, 0.0053007, 0.051401
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27, 0.066612, 0.050319, 0.024494, 0.48579, 0.42298, 0.40479, 0.11374, 0.056078, 0.045788, 0.014807, 0.0054901, 0.0054901, 0.04959
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28, 0.063534, 0.052454, 0.024039, 0.40719, 0.36156, 0.30722, 0.10794, 0.055933, 0.046081, 0.021163, 0.0056788, 0.0056788, 0.047779
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29, 0.063979, 0.050578, 0.023715, 0.11747, 0.27502, 0.089202, 0.017614, 0.058407, 0.055614, 0.019128, 0.0058667, 0.0058667, 0.045967
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30, 0.061931, 0.04879, 0.021555, 0.097, 0.14031, 0.055955, 0.0091582, 0.056716, 0.063297, 0.024825, 0.0060537, 0.0060537, 0.044154
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31, 0.057106, 0.050596, 0.020186, 0.46931, 0.63839, 0.47727, 0.14214, 0.047358, 0.044311, 0.013691, 0.0062398, 0.0062398, 0.04234
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32, 0.061115, 0.046957, 0.020035, 0.63103, 0.71706, 0.687, 0.25922, 0.046848, 0.037924, 0.011027, 0.006425, 0.006425, 0.040525
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33, 0.056406, 0.046535, 0.01865, 0.43884, 0.62161, 0.51021, 0.17632, 0.05008, 0.038613, 0.014262, 0.0066094, 0.0066094, 0.038709
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34, 0.059193, 0.048328, 0.019087, 0.40999, 0.66487, 0.53293, 0.19979, 0.047951, 0.038267, 0.0089096, 0.0067927, 0.0067927, 0.036893
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35, 0.058858, 0.046026, 0.018089, 0.49914, 0.45985, 0.44086, 0.14173, 0.055914, 0.049698, 0.016622, 0.0069751, 0.0069751, 0.035075
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36, 0.05378, 0.046878, 0.017436, 0.54042, 0.40374, 0.42118, 0.12772, 0.053832, 0.048224, 0.015295, 0.0071565, 0.0071565, 0.033256
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37, 0.057157, 0.047375, 0.01825, 0.45283, 0.17341, 0.1188, 0.023888, 0.05297, 0.06591, 0.028537, 0.0073368, 0.0073368, 0.031437
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38, 0.055086, 0.044643, 0.016019, 0.59048, 0.58181, 0.54786, 0.20768, 0.046944, 0.041786, 0.011842, 0.0075161, 0.0075161, 0.029616
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39, 0.052514, 0.042485, 0.016678, 0.60654, 0.62859, 0.65293, 0.27689, 0.04138, 0.043998, 0.0078349, 0.0076943, 0.0076943, 0.027794
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40, 0.054016, 0.045163, 0.016713, 0.43989, 0.24508, 0.23914, 0.074126, 0.052986, 0.055093, 0.029361, 0.0078714, 0.0078714, 0.025971
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41, 0.052897, 0.042598, 0.014125, 0.66111, 0.45699, 0.54765, 0.20954, 0.04524, 0.048015, 0.013144, 0.0080473, 0.0080473, 0.024147
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42, 0.051021, 0.042313, 0.012967, 0.64228, 0.50983, 0.5556, 0.2054, 0.049423, 0.04853, 0.014758, 0.0082221, 0.0082221, 0.022322
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43, 0.05275, 0.041908, 0.012555, 0.76316, 0.7306, 0.72312, 0.21781, 0.046986, 0.032759, 0.0042896, 0.0083957, 0.0083957, 0.020496
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44, 0.052692, 0.040748, 0.012202, 0.73323, 0.7009, 0.74501, 0.32735, 0.040507, 0.036143, 0.0074856, 0.0085681, 0.0085681, 0.018668
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45, 0.051049, 0.040343, 0.010494, 0.79341, 0.8425, 0.8331, 0.38394, 0.042813, 0.033749, 0.0054575, 0.0087393, 0.0087393, 0.016839
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46, 0.052916, 0.04602, 0.016315, 0.0074664, 0.36345, 0.0069738, 0.0012171, 0.071852, 0.057659, 0.040379, 0.0089092, 0.0089092, 0.015009
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47, 0.061261, 0.055108, 0.025584, 0, 0, 0, 0, 0.10342, 0.10308, 0.61562, 0.0090778, 0.0090778, 0.013178
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48, 0.055741, 0.048942, 0.022914, 0, 0, 0, 0, 0.091703, 0.15503, 0.50829, 0.0092451, 0.0092451, 0.011345
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49, 0.055505, 0.046065, 0.01996, 0.40454, 0.43583, 0.36731, 0.1155, 0.054668, 0.048338, 0.01677, 0.009411, 0.009411, 0.009511
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50, 0.052316, 0.04637, 0.01857, 0.29994, 0.17425, 0.1908, 0.069672, 0.061657, 0.058302, 0.017473, 0.0093971, 0.0093971, 0.0093971
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51, 0.052513, 0.044853, 0.016164, 0.32316, 0.21994, 0.18133, 0.052024, 0.054571, 0.052566, 0.018779, 0.0093971, 0.0093971, 0.0093971
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52, 0.050169, 0.041122, 0.013702, 0.71349, 0.46749, 0.50083, 0.20943, 0.05027, 0.040049, 0.0090618, 0.0093733, 0.0093733, 0.0093733
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53, 0.052356, 0.038377, 0.012993, 0.70745, 0.58166, 0.63794, 0.29499, 0.043828, 0.039433, 0.006337, 0.0093491, 0.0093491, 0.0093491
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54, 0.049593, 0.041015, 0.013021, 0.8286, 0.83119, 0.85701, 0.39813, 0.044484, 0.031344, 0.0039315, 0.0093245, 0.0093245, 0.0093245
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55, 0.050457, 0.040325, 0.011648, 0.90411, 0.72727, 0.81333, 0.32555, 0.050267, 0.031773, 0.0036776, 0.0092995, 0.0092995, 0.0092995
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56, 0.048361, 0.037615, 0.01134, 0.83214, 0.70918, 0.7804, 0.32044, 0.048583, 0.035312, 0.0075229, 0.009274, 0.009274, 0.009274
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57, 0.045264, 0.037444, 0.010766, 0.73705, 0.60835, 0.67841, 0.33454, 0.037938, 0.035844, 0.011167, 0.0092481, 0.0092481, 0.0092481
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|
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58, 0.048281, 0.038769, 0.0095906, 0.77539, 0.72818, 0.77761, 0.35702, 0.038495, 0.033515, 0.0052658, 0.0092219, 0.0092219, 0.0092219
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59, 0.045365, 0.036449, 0.0083486, 0.84428, 0.80126, 0.86356, 0.45554, 0.039402, 0.029563, 0.00649, 0.0091952, 0.0091952, 0.0091952
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60, 0.047082, 0.038087, 0.01002, 0.82263, 0.84693, 0.89213, 0.45806, 0.038933, 0.03161, 0.0054989, 0.0091681, 0.0091681, 0.0091681
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61, 0.043742, 0.036556, 0.0088003, 0.92037, 0.8607, 0.9257, 0.48197, 0.036326, 0.030955, 0.0034465, 0.0091406, 0.0091406, 0.0091406
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62, 0.044255, 0.036951, 0.010437, 0.87895, 0.89227, 0.91443, 0.44407, 0.043374, 0.028684, 0.0026665, 0.0091127, 0.0091127, 0.0091127
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63, 0.045161, 0.03555, 0.0089086, 0.92405, 0.88652, 0.91847, 0.5026, 0.039014, 0.030431, 0.0025694, 0.0090844, 0.0090844, 0.0090844
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64, 0.045083, 0.037143, 0.0087739, 0.93807, 0.88789, 0.93251, 0.48687, 0.041815, 0.028742, 0.002422, 0.0090557, 0.0090557, 0.0090557
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||||||
65, 0.040342, 0.037515, 0.0077562, 0.92831, 0.92405, 0.95091, 0.55001, 0.033787, 0.028646, 0.0023556, 0.0090266, 0.0090266, 0.0090266
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66, 0.044417, 0.032584, 0.0076607, 0.92958, 0.86946, 0.92922, 0.53225, 0.035044, 0.028907, 0.0025468, 0.0089972, 0.0089972, 0.0089972
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||||||
67, 0.039694, 0.035995, 0.0079306, 0.88746, 0.90617, 0.92104, 0.51225, 0.035307, 0.027231, 0.003196, 0.0089673, 0.0089673, 0.0089673
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68, 0.044565, 0.034608, 0.0088018, 0.87307, 0.84145, 0.90489, 0.50325, 0.034845, 0.032796, 0.0049277, 0.0089371, 0.0089371, 0.0089371
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69, 0.042965, 0.033984, 0.01085, 0.9106, 0.85758, 0.91816, 0.51602, 0.035491, 0.030649, 0.0044527, 0.0089065, 0.0089065, 0.0089065
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70, 0.04196, 0.035426, 0.0084481, 0.86355, 0.90502, 0.92138, 0.55436, 0.032832, 0.029249, 0.0028974, 0.0088755, 0.0088755, 0.0088755
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71, 0.038512, 0.036, 0.0078214, 0.94121, 0.89692, 0.94946, 0.54232, 0.035271, 0.027604, 0.0020984, 0.0088442, 0.0088442, 0.0088442
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72, 0.041711, 0.035849, 0.0077841, 0.94633, 0.90146, 0.94532, 0.55551, 0.034369, 0.028245, 0.0028688, 0.0088124, 0.0088124, 0.0088124
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73, 0.043422, 0.034141, 0.008423, 0.89791, 0.79773, 0.89551, 0.49783, 0.036391, 0.032343, 0.0036569, 0.0087804, 0.0087804, 0.0087804
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74, 0.041125, 0.034275, 0.0088747, 0.94643, 0.86711, 0.9366, 0.54438, 0.031141, 0.029405, 0.0027034, 0.0087479, 0.0087479, 0.0087479
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75, 0.039719, 0.034131, 0.0079598, 0.9524, 0.91862, 0.9582, 0.59485, 0.03173, 0.026338, 0.0019864, 0.0087151, 0.0087151, 0.0087151
|
|
||||||
76, 0.040181, 0.033154, 0.0080667, 0.90734, 0.83848, 0.92208, 0.47094, 0.038296, 0.031134, 0.0040538, 0.008682, 0.008682, 0.008682
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|
||||||
77, 0.03966, 0.033198, 0.0066327, 0.94442, 0.86113, 0.92597, 0.45845, 0.035619, 0.028766, 0.0026854, 0.0086485, 0.0086485, 0.0086485
|
|
||||||
78, 0.038331, 0.034961, 0.0069487, 0.91974, 0.89982, 0.94746, 0.51601, 0.034682, 0.02985, 0.0028031, 0.0086146, 0.0086146, 0.0086146
|
|
||||||
79, 0.038131, 0.034181, 0.0070184, 0.9505, 0.92209, 0.95505, 0.56273, 0.031882, 0.030202, 0.0025396, 0.0085805, 0.0085805, 0.0085805
|
|
||||||
80, 0.037099, 0.033424, 0.0061094, 0.95886, 0.9318, 0.94569, 0.56409, 0.031647, 0.027277, 0.0028533, 0.0085459, 0.0085459, 0.0085459
|
|
||||||
81, 0.038213, 0.035, 0.0062455, 0.90117, 0.91763, 0.93225, 0.58747, 0.030732, 0.026239, 0.0024431, 0.0085111, 0.0085111, 0.0085111
|
|
||||||
82, 0.038884, 0.031852, 0.0060274, 0.94817, 0.92094, 0.95121, 0.55533, 0.031829, 0.026745, 0.0023725, 0.0084759, 0.0084759, 0.0084759
|
|
||||||
83, 0.036625, 0.033138, 0.0079261, 0.95916, 0.91625, 0.94151, 0.5457, 0.034776, 0.026776, 0.0021613, 0.0084404, 0.0084404, 0.0084404
|
|
||||||
84, 0.038809, 0.032023, 0.0062357, 0.93504, 0.92504, 0.95141, 0.58087, 0.033784, 0.026052, 0.0023802, 0.0084046, 0.0084046, 0.0084046
|
|
||||||
85, 0.037893, 0.034964, 0.0068261, 0.92978, 0.93218, 0.95634, 0.58927, 0.031465, 0.026274, 0.0021452, 0.0083684, 0.0083684, 0.0083684
|
|
||||||
86, 0.038298, 0.033131, 0.0060222, 0.94402, 0.95637, 0.96109, 0.55015, 0.033578, 0.028523, 0.002081, 0.0083319, 0.0083319, 0.0083319
|
|
||||||
87, 0.037152, 0.034427, 0.0058415, 0.93586, 0.91752, 0.94697, 0.59167, 0.030662, 0.027937, 0.0022087, 0.0082952, 0.0082952, 0.0082952
|
|
||||||
88, 0.033444, 0.033919, 0.0065007, 0.97881, 0.93894, 0.95851, 0.60322, 0.031015, 0.025488, 0.0023777, 0.0082581, 0.0082581, 0.0082581
|
|
||||||
89, 0.034102, 0.033245, 0.006238, 0.97284, 0.92101, 0.95236, 0.58836, 0.031254, 0.026429, 0.0026268, 0.0082207, 0.0082207, 0.0082207
|
|
||||||
90, 0.033522, 0.03244, 0.0050727, 0.96762, 0.87823, 0.94835, 0.59588, 0.029, 0.026984, 0.003767, 0.008183, 0.008183, 0.008183
|
|
||||||
91, 0.034385, 0.03257, 0.0068179, 0.96143, 0.94048, 0.96293, 0.61393, 0.032598, 0.025748, 0.0025069, 0.008145, 0.008145, 0.008145
|
|
||||||
92, 0.038411, 0.03149, 0.008301, 0.85757, 0.82488, 0.90553, 0.50806, 0.03392, 0.030222, 0.0066837, 0.0081068, 0.0081068, 0.0081068
|
|
||||||
93, 0.038098, 0.030603, 0.0062063, 0.87368, 0.83447, 0.8986, 0.48715, 0.032855, 0.030639, 0.0038948, 0.0080682, 0.0080682, 0.0080682
|
|
||||||
94, 0.038408, 0.031401, 0.0071762, 0.97569, 0.88239, 0.94377, 0.57059, 0.032335, 0.027143, 0.0022352, 0.0080294, 0.0080294, 0.0080294
|
|
||||||
95, 0.037455, 0.031985, 0.0056255, 0.92462, 0.91911, 0.95528, 0.57158, 0.032933, 0.02751, 0.0028774, 0.0079903, 0.0079903, 0.0079903
|
|
||||||
96, 0.034081, 0.032974, 0.0055842, 0.95494, 0.95877, 0.96191, 0.56896, 0.031626, 0.02607, 0.0021001, 0.0079509, 0.0079509, 0.0079509
|
|
||||||
97, 0.036189, 0.031882, 0.0066565, 0.95704, 0.92485, 0.96058, 0.61761, 0.031169, 0.024964, 0.0026779, 0.0079112, 0.0079112, 0.0079112
|
|
||||||
98, 0.034716, 0.033089, 0.0062765, 0.95208, 0.9259, 0.95999, 0.58112, 0.031563, 0.025833, 0.002693, 0.0078713, 0.0078713, 0.0078713
|
|
||||||
99, 0.035807, 0.032488, 0.0062112, 0.94346, 0.93805, 0.95484, 0.58984, 0.030902, 0.025948, 0.0019203, 0.0078311, 0.0078311, 0.0078311
|
|
||||||
100, 0.035404, 0.030942, 0.0070651, 0.90669, 0.90848, 0.94371, 0.55885, 0.032847, 0.031035, 0.0054341, 0.0077907, 0.0077907, 0.0077907
|
|
||||||
101, 0.034805, 0.032716, 0.007314, 0.95797, 0.95318, 0.96146, 0.57165, 0.031743, 0.027374, 0.0019765, 0.00775, 0.00775, 0.00775
|
|
||||||
102, 0.036556, 0.032288, 0.0068758, 0.93197, 0.95396, 0.96154, 0.58878, 0.030179, 0.028715, 0.0022038, 0.0077091, 0.0077091, 0.0077091
|
|
||||||
103, 0.034745, 0.03311, 0.0060098, 0.91789, 0.95751, 0.9557, 0.60553, 0.030048, 0.028515, 0.0018339, 0.0076679, 0.0076679, 0.0076679
|
|
||||||
104, 0.035191, 0.032241, 0.0060717, 0.89044, 0.92859, 0.9459, 0.55454, 0.031789, 0.029479, 0.0041273, 0.0076265, 0.0076265, 0.0076265
|
|
||||||
105, 0.035232, 0.030591, 0.0058525, 0.88359, 0.89753, 0.93313, 0.58269, 0.030801, 0.027946, 0.0030299, 0.0075848, 0.0075848, 0.0075848
|
|
||||||
106, 0.034891, 0.031808, 0.0054986, 0.92895, 0.85635, 0.92882, 0.49609, 0.034223, 0.029069, 0.0034086, 0.007543, 0.007543, 0.007543
|
|
||||||
107, 0.034206, 0.032183, 0.0059379, 0.95328, 0.90682, 0.95609, 0.5899, 0.032747, 0.02519, 0.002409, 0.0075009, 0.0075009, 0.0075009
|
|
||||||
108, 0.035193, 0.032691, 0.006186, 0.76003, 0.88884, 0.92562, 0.52785, 0.033637, 0.027941, 0.0085591, 0.0074585, 0.0074585, 0.0074585
|
|
||||||
109, 0.032678, 0.031774, 0.0051808, 0.9394, 0.94668, 0.95971, 0.59036, 0.029259, 0.025816, 0.0018039, 0.007416, 0.007416, 0.007416
|
|
||||||
110, 0.034928, 0.032253, 0.0052045, 0.96947, 0.9156, 0.95994, 0.57839, 0.031965, 0.025841, 0.0017381, 0.0073733, 0.0073733, 0.0073733
|
|
||||||
111, 0.032909, 0.030654, 0.0068091, 0.93353, 0.9431, 0.95796, 0.6155, 0.029402, 0.024355, 0.00166, 0.0073303, 0.0073303, 0.0073303
|
|
||||||
112, 0.034421, 0.030596, 0.0056472, 0.97009, 0.96553, 0.96802, 0.62231, 0.027934, 0.024545, 0.0017626, 0.0072872, 0.0072872, 0.0072872
|
|
||||||
113, 0.033877, 0.031202, 0.0048802, 0.96084, 0.94522, 0.95601, 0.59425, 0.029266, 0.025481, 0.0019383, 0.0072438, 0.0072438, 0.0072438
|
|
||||||
114, 0.032888, 0.030615, 0.0059414, 0.96707, 0.96629, 0.96299, 0.58985, 0.028752, 0.02581, 0.001971, 0.0072003, 0.0072003, 0.0072003
|
|
||||||
115, 0.033697, 0.032449, 0.0059314, 0.98178, 0.96195, 0.96413, 0.62237, 0.030004, 0.025665, 0.0022348, 0.0071566, 0.0071566, 0.0071566
|
|
||||||
116, 0.032169, 0.028243, 0.0053777, 0.96855, 0.95665, 0.96663, 0.59072, 0.030777, 0.027957, 0.0025654, 0.0071127, 0.0071127, 0.0071127
|
|
||||||
117, 0.031322, 0.029406, 0.0044561, 0.95818, 0.95873, 0.96789, 0.61904, 0.03089, 0.02532, 0.0023165, 0.0070686, 0.0070686, 0.0070686
|
|
||||||
118, 0.032621, 0.031882, 0.0060545, 0.96766, 0.9703, 0.97043, 0.61416, 0.031658, 0.02589, 0.0024626, 0.0070243, 0.0070243, 0.0070243
|
|
||||||
119, 0.033223, 0.027714, 0.0065067, 0.98299, 0.96548, 0.96768, 0.62695, 0.029253, 0.02492, 0.0019283, 0.0069799, 0.0069799, 0.0069799
|
|
||||||
120, 0.033553, 0.029447, 0.0063224, 0.98701, 0.95656, 0.96794, 0.64578, 0.027592, 0.02491, 0.0018422, 0.0069353, 0.0069353, 0.0069353
|
|
||||||
121, 0.03325, 0.028541, 0.0051204, 0.98317, 0.93651, 0.96604, 0.61816, 0.032385, 0.023813, 0.0019208, 0.0068906, 0.0068906, 0.0068906
|
|
||||||
122, 0.033097, 0.02979, 0.0057041, 0.97144, 0.93651, 0.96183, 0.61828, 0.027016, 0.026034, 0.0019498, 0.0068457, 0.0068457, 0.0068457
|
|
||||||
123, 0.032385, 0.031679, 0.0052411, 0.97262, 0.94493, 0.96298, 0.62283, 0.026364, 0.025603, 0.0019448, 0.0068006, 0.0068006, 0.0068006
|
|
||||||
124, 0.033781, 0.029724, 0.0050386, 0.95657, 0.95188, 0.96177, 0.64612, 0.028362, 0.024317, 0.0019741, 0.0067555, 0.0067555, 0.0067555
|
|
||||||
125, 0.032106, 0.029237, 0.0045665, 0.94958, 0.9452, 0.96075, 0.64017, 0.028601, 0.024065, 0.0021955, 0.0067101, 0.0067101, 0.0067101
|
|
||||||
126, 0.032264, 0.029086, 0.0054842, 0.95887, 0.96354, 0.96768, 0.66344, 0.026937, 0.023887, 0.0024299, 0.0066647, 0.0066647, 0.0066647
|
|
||||||
127, 0.032503, 0.030248, 0.0055057, 0.96337, 0.96553, 0.96908, 0.6688, 0.026015, 0.023054, 0.002129, 0.0066191, 0.0066191, 0.0066191
|
|
||||||
128, 0.031848, 0.030237, 0.0040163, 0.97874, 0.9667, 0.96928, 0.65926, 0.026758, 0.024459, 0.0019228, 0.0065734, 0.0065734, 0.0065734
|
|
||||||
129, 0.033826, 0.03026, 0.0075954, 0.97185, 0.97532, 0.97586, 0.65575, 0.027961, 0.023636, 0.0018942, 0.0065276, 0.0065276, 0.0065276
|
|
||||||
130, 0.034133, 0.029008, 0.0056355, 0.98736, 0.94112, 0.9718, 0.63606, 0.02707, 0.024257, 0.0019156, 0.0064816, 0.0064816, 0.0064816
|
|
||||||
131, 0.032031, 0.029106, 0.0046239, 0.97221, 0.91833, 0.96514, 0.62362, 0.027138, 0.024794, 0.0020008, 0.0064356, 0.0064356, 0.0064356
|
|
||||||
132, 0.031064, 0.028333, 0.005266, 0.90643, 0.90848, 0.94398, 0.52023, 0.032159, 0.02668, 0.0031502, 0.0063895, 0.0063895, 0.0063895
|
|
||||||
133, 0.031294, 0.030179, 0.0047473, 0.9751, 0.94523, 0.96989, 0.60873, 0.028775, 0.024644, 0.0019185, 0.0063432, 0.0063432, 0.0063432
|
|
||||||
134, 0.03095, 0.029861, 0.004757, 0.93719, 0.96354, 0.96023, 0.64725, 0.026088, 0.024737, 0.001847, 0.0062969, 0.0062969, 0.0062969
|
|
||||||
135, 0.030516, 0.028845, 0.0047947, 0.95301, 0.95197, 0.96786, 0.62838, 0.026574, 0.024545, 0.0017698, 0.0062505, 0.0062505, 0.0062505
|
|
||||||
136, 0.030475, 0.02846, 0.0055107, 0.96437, 0.9451, 0.96404, 0.64108, 0.027123, 0.024913, 0.0017149, 0.006204, 0.006204, 0.006204
|
|
||||||
137, 0.030362, 0.029429, 0.0048351, 0.97425, 0.96309, 0.96501, 0.6316, 0.027754, 0.025372, 0.0018266, 0.0061574, 0.0061574, 0.0061574
|
|
||||||
138, 0.030377, 0.030139, 0.0047596, 0.96266, 0.96512, 0.96441, 0.63686, 0.027537, 0.024936, 0.0019117, 0.0061107, 0.0061107, 0.0061107
|
|
||||||
139, 0.030934, 0.027383, 0.0046041, 0.97265, 0.9703, 0.96765, 0.64827, 0.027235, 0.024562, 0.0018573, 0.006064, 0.006064, 0.006064
|
|
||||||
140, 0.030503, 0.030103, 0.005123, 0.96566, 0.95537, 0.96187, 0.62706, 0.027453, 0.024727, 0.0018926, 0.0060172, 0.0060172, 0.0060172
|
|
||||||
141, 0.031252, 0.028443, 0.0041605, 0.97251, 0.96352, 0.96748, 0.66484, 0.026909, 0.024105, 0.0020043, 0.0059704, 0.0059704, 0.0059704
|
|
||||||
142, 0.029091, 0.029802, 0.004882, 0.98107, 0.97705, 0.97517, 0.6692, 0.025419, 0.023475, 0.0019698, 0.0059235, 0.0059235, 0.0059235
|
|
||||||
143, 0.029031, 0.027532, 0.0044102, 0.96415, 0.95001, 0.96306, 0.65746, 0.026583, 0.024414, 0.0018636, 0.0058766, 0.0058766, 0.0058766
|
|
||||||
144, 0.030322, 0.029805, 0.0045858, 0.95213, 0.97029, 0.96608, 0.66087, 0.02622, 0.024185, 0.0019045, 0.0058296, 0.0058296, 0.0058296
|
|
||||||
145, 0.03003, 0.027735, 0.0042047, 0.9629, 0.97049, 0.97254, 0.6706, 0.025957, 0.023952, 0.0019944, 0.0057826, 0.0057826, 0.0057826
|
|
||||||
146, 0.030178, 0.028212, 0.0038413, 0.97726, 0.96369, 0.96698, 0.6622, 0.025378, 0.025052, 0.0019726, 0.0057355, 0.0057355, 0.0057355
|
|
||||||
147, 0.0287, 0.027819, 0.0042913, 0.97216, 0.96354, 0.96631, 0.64059, 0.025874, 0.024205, 0.0019755, 0.0056884, 0.0056884, 0.0056884
|
|
||||||
148, 0.032043, 0.027684, 0.0048876, 0.98775, 0.95678, 0.96185, 0.61574, 0.029168, 0.025582, 0.001935, 0.0056413, 0.0056413, 0.0056413
|
|
||||||
149, 0.030177, 0.027118, 0.0045694, 0.98348, 0.95001, 0.96349, 0.66463, 0.026593, 0.023216, 0.0019195, 0.0055942, 0.0055942, 0.0055942
|
|
||||||
150, 0.028507, 0.030105, 0.0049029, 0.98161, 0.95661, 0.96846, 0.65684, 0.02566, 0.023492, 0.0021502, 0.0055471, 0.0055471, 0.0055471
|
|
||||||
151, 0.030499, 0.028858, 0.0074011, 0.98125, 0.9703, 0.97297, 0.68484, 0.026201, 0.022504, 0.002315, 0.0055, 0.0055, 0.0055
|
|
||||||
152, 0.02793, 0.028107, 0.0056783, 0.98268, 0.95669, 0.96742, 0.68158, 0.026543, 0.023369, 0.0024157, 0.0054529, 0.0054529, 0.0054529
|
|
||||||
153, 0.028745, 0.027211, 0.0047151, 0.98282, 0.95678, 0.96778, 0.67643, 0.024136, 0.022676, 0.0020738, 0.0054058, 0.0054058, 0.0054058
|
|
||||||
154, 0.02988, 0.028095, 0.0047024, 0.97564, 0.95678, 0.96446, 0.67542, 0.026337, 0.02249, 0.002291, 0.0053587, 0.0053587, 0.0053587
|
|
||||||
155, 0.028457, 0.029243, 0.0047708, 0.96159, 0.95678, 0.95796, 0.66082, 0.026913, 0.022992, 0.0021378, 0.0053116, 0.0053116, 0.0053116
|
|
||||||
156, 0.028992, 0.027253, 0.0048825, 0.97888, 0.96996, 0.9719, 0.6495, 0.027016, 0.022958, 0.001994, 0.0052645, 0.0052645, 0.0052645
|
|
||||||
157, 0.028998, 0.028506, 0.0044686, 0.95299, 0.97018, 0.96508, 0.6518, 0.026524, 0.022246, 0.0018938, 0.0052174, 0.0052174, 0.0052174
|
|
||||||
158, 0.029876, 0.028789, 0.0045456, 0.97905, 0.9703, 0.96656, 0.67184, 0.026013, 0.023414, 0.0020316, 0.0051704, 0.0051704, 0.0051704
|
|
||||||
159, 0.02783, 0.027954, 0.0047788, 0.95981, 0.9703, 0.97331, 0.65536, 0.026615, 0.023393, 0.0021369, 0.0051234, 0.0051234, 0.0051234
|
|
||||||
160, 0.02881, 0.029834, 0.0051372, 0.96231, 0.97705, 0.97276, 0.67234, 0.02516, 0.023334, 0.0020906, 0.0050765, 0.0050765, 0.0050765
|
|
||||||
161, 0.02907, 0.028024, 0.0050542, 0.97128, 0.97694, 0.97459, 0.67987, 0.024301, 0.022933, 0.0018993, 0.0050296, 0.0050296, 0.0050296
|
|
||||||
162, 0.027088, 0.02831, 0.0049067, 0.97555, 0.97705, 0.97511, 0.68172, 0.02416, 0.022443, 0.0016887, 0.0049828, 0.0049828, 0.0049828
|
|
||||||
163, 0.030222, 0.026841, 0.004733, 0.97753, 0.97705, 0.97482, 0.68213, 0.025884, 0.02258, 0.0016608, 0.004936, 0.004936, 0.004936
|
|
||||||
164, 0.028023, 0.028466, 0.0040835, 0.95802, 0.94365, 0.96412, 0.68017, 0.027131, 0.022298, 0.0018087, 0.0048893, 0.0048893, 0.0048893
|
|
||||||
165, 0.028881, 0.028552, 0.0037733, 0.98463, 0.9703, 0.966, 0.69216, 0.024287, 0.022506, 0.0017584, 0.0048426, 0.0048426, 0.0048426
|
|
||||||
166, 0.02887, 0.028066, 0.0051819, 0.97967, 0.97023, 0.96605, 0.67742, 0.023991, 0.022966, 0.0017765, 0.004796, 0.004796, 0.004796
|
|
||||||
167, 0.027769, 0.027876, 0.0045376, 0.97336, 0.9703, 0.96585, 0.67378, 0.024131, 0.022832, 0.0018437, 0.0047495, 0.0047495, 0.0047495
|
|
||||||
168, 0.026105, 0.026978, 0.0043823, 0.98068, 0.9703, 0.97141, 0.68531, 0.02506, 0.022971, 0.0019168, 0.0047031, 0.0047031, 0.0047031
|
|
||||||
169, 0.027398, 0.02762, 0.0039622, 0.98055, 0.96775, 0.96652, 0.69533, 0.023761, 0.022578, 0.0018546, 0.0046568, 0.0046568, 0.0046568
|
|
||||||
170, 0.028034, 0.027971, 0.0037273, 0.97684, 0.9703, 0.96783, 0.69053, 0.023457, 0.0223, 0.0017781, 0.0046105, 0.0046105, 0.0046105
|
|
||||||
171, 0.027347, 0.028535, 0.0038968, 0.97337, 0.96354, 0.96296, 0.67883, 0.024073, 0.022644, 0.001817, 0.0045644, 0.0045644, 0.0045644
|
|
||||||
172, 0.028142, 0.026267, 0.0034699, 0.98644, 0.96343, 0.96703, 0.69874, 0.02388, 0.02208, 0.0017773, 0.0045184, 0.0045184, 0.0045184
|
|
||||||
173, 0.026171, 0.027753, 0.0050813, 0.97569, 0.96352, 0.96478, 0.67178, 0.02579, 0.022882, 0.0018091, 0.0044724, 0.0044724, 0.0044724
|
|
||||||
174, 0.026128, 0.026436, 0.0053865, 0.97295, 0.96346, 0.96565, 0.67965, 0.025346, 0.023009, 0.001846, 0.0044266, 0.0044266, 0.0044266
|
|
||||||
175, 0.02726, 0.027188, 0.0035265, 0.97704, 0.96351, 0.96655, 0.68568, 0.024316, 0.022898, 0.0017214, 0.0043809, 0.0043809, 0.0043809
|
|
||||||
176, 0.026716, 0.026838, 0.0031932, 0.98022, 0.96354, 0.96597, 0.68301, 0.025983, 0.023226, 0.0016424, 0.0043353, 0.0043353, 0.0043353
|
|
||||||
177, 0.027251, 0.028037, 0.0054986, 0.97271, 0.96354, 0.96646, 0.68557, 0.026387, 0.022748, 0.0016146, 0.0042899, 0.0042899, 0.0042899
|
|
||||||
178, 0.026909, 0.026743, 0.0046035, 0.98252, 0.97027, 0.97551, 0.66755, 0.026705, 0.022883, 0.0016144, 0.0042445, 0.0042445, 0.0042445
|
|
||||||
179, 0.026095, 0.026161, 0.0058214, 0.98155, 0.97028, 0.96818, 0.66911, 0.025493, 0.023625, 0.0016812, 0.0041994, 0.0041994, 0.0041994
|
|
||||||
180, 0.024472, 0.026628, 0.0041876, 0.98266, 0.97705, 0.97576, 0.66959, 0.024833, 0.022997, 0.0017462, 0.0041543, 0.0041543, 0.0041543
|
|
||||||
181, 0.027072, 0.027344, 0.0044676, 0.98132, 0.97705, 0.9754, 0.68045, 0.024837, 0.022527, 0.0017458, 0.0041094, 0.0041094, 0.0041094
|
|
||||||
182, 0.028166, 0.02681, 0.0042947, 0.98137, 0.9703, 0.9683, 0.68976, 0.024242, 0.022963, 0.001793, 0.0040647, 0.0040647, 0.0040647
|
|
||||||
183, 0.026102, 0.027943, 0.0043331, 0.95813, 0.97705, 0.97464, 0.69977, 0.023437, 0.022746, 0.0018653, 0.0040201, 0.0040201, 0.0040201
|
|
||||||
184, 0.026373, 0.026178, 0.0037455, 0.98558, 0.96354, 0.97444, 0.69813, 0.024044, 0.022799, 0.0018616, 0.0039757, 0.0039757, 0.0039757
|
|
||||||
185, 0.028673, 0.026108, 0.0044021, 0.98181, 0.95199, 0.96343, 0.67845, 0.024732, 0.023188, 0.0018667, 0.0039314, 0.0039314, 0.0039314
|
|
||||||
186, 0.026675, 0.02758, 0.0041627, 0.98565, 0.96353, 0.96589, 0.68148, 0.022764, 0.022821, 0.001741, 0.0038873, 0.0038873, 0.0038873
|
|
||||||
187, 0.026643, 0.027148, 0.0038995, 0.98353, 0.97028, 0.96679, 0.68438, 0.02252, 0.022897, 0.0017302, 0.0038434, 0.0038434, 0.0038434
|
|
||||||
188, 0.027608, 0.027644, 0.0039359, 0.98275, 0.977, 0.97892, 0.69636, 0.023269, 0.022497, 0.0018708, 0.0037997, 0.0037997, 0.0037997
|
|
||||||
189, 0.024131, 0.027909, 0.0027875, 0.97406, 0.9703, 0.97597, 0.69888, 0.023948, 0.022371, 0.0023066, 0.0037562, 0.0037562, 0.0037562
|
|
||||||
190, 0.027156, 0.027026, 0.0036558, 0.98347, 0.96347, 0.9715, 0.68745, 0.023983, 0.023429, 0.0027685, 0.0037128, 0.0037128, 0.0037128
|
|
||||||
191, 0.027734, 0.026852, 0.0039689, 0.97298, 0.97025, 0.9738, 0.6853, 0.026086, 0.022382, 0.0022475, 0.0036697, 0.0036697, 0.0036697
|
|
||||||
192, 0.026927, 0.026752, 0.0037148, 0.96858, 0.96354, 0.96962, 0.69858, 0.023535, 0.022065, 0.0017252, 0.0036267, 0.0036267, 0.0036267
|
|
||||||
193, 0.027003, 0.027176, 0.0053776, 0.96122, 0.97705, 0.97883, 0.66762, 0.023695, 0.022581, 0.0018148, 0.003584, 0.003584, 0.003584
|
|
||||||
194, 0.026989, 0.027615, 0.006495, 0.95862, 0.95678, 0.96378, 0.61374, 0.02806, 0.024838, 0.0017687, 0.0035415, 0.0035415, 0.0035415
|
|
||||||
195, 0.027666, 0.026871, 0.0057181, 0.96489, 0.9702, 0.96448, 0.66116, 0.024083, 0.024089, 0.0018062, 0.0034991, 0.0034991, 0.0034991
|
|
||||||
196, 0.026176, 0.02691, 0.004667, 0.97361, 0.96348, 0.96482, 0.67681, 0.024865, 0.023765, 0.0019973, 0.003457, 0.003457, 0.003457
|
|
||||||
197, 0.026469, 0.027627, 0.0049022, 0.96453, 0.96354, 0.97173, 0.68511, 0.024681, 0.022624, 0.0021242, 0.0034152, 0.0034152, 0.0034152
|
|
||||||
198, 0.027208, 0.028003, 0.0049884, 0.97647, 0.96354, 0.96577, 0.69568, 0.024119, 0.022065, 0.0021831, 0.0033735, 0.0033735, 0.0033735
|
|
||||||
199, 0.025903, 0.027341, 0.0048099, 0.98137, 0.95876, 0.96996, 0.69246, 0.024701, 0.022356, 0.0023582, 0.0033321, 0.0033321, 0.0033321
|
|
||||||
200, 0.026184, 0.027374, 0.0042712, 0.98338, 0.9635, 0.96994, 0.67492, 0.025071, 0.022943, 0.0024592, 0.0032909, 0.0032909, 0.0032909
|
|
||||||
201, 0.026189, 0.025554, 0.0040891, 0.98737, 0.94994, 0.96182, 0.66253, 0.025125, 0.023302, 0.0023766, 0.00325, 0.00325, 0.00325
|
|
||||||
202, 0.024545, 0.026731, 0.0041204, 0.9815, 0.9703, 0.97592, 0.68519, 0.024929, 0.022513, 0.0020454, 0.0032093, 0.0032093, 0.0032093
|
|
||||||
203, 0.02617, 0.025825, 0.0042871, 0.95509, 0.97705, 0.97726, 0.68377, 0.024432, 0.022358, 0.0020345, 0.0031689, 0.0031689, 0.0031689
|
|
||||||
204, 0.023137, 0.024813, 0.0036278, 0.96776, 0.96354, 0.97128, 0.69433, 0.026057, 0.022551, 0.0019471, 0.0031287, 0.0031287, 0.0031287
|
|
||||||
205, 0.025338, 0.025502, 0.0037127, 0.99338, 0.95089, 0.96514, 0.68198, 0.025945, 0.023259, 0.0021286, 0.0030888, 0.0030888, 0.0030888
|
|
||||||
206, 0.026339, 0.02602, 0.0039489, 0.98518, 0.97028, 0.96743, 0.67505, 0.023719, 0.022723, 0.0020737, 0.0030491, 0.0030491, 0.0030491
|
|
||||||
207, 0.026491, 0.027071, 0.0040128, 0.98605, 0.96354, 0.96766, 0.68313, 0.024904, 0.022284, 0.0022663, 0.0030097, 0.0030097, 0.0030097
|
|
||||||
208, 0.025094, 0.025137, 0.0043872, 0.99331, 0.9505, 0.96795, 0.6957, 0.024963, 0.021901, 0.0026323, 0.0029706, 0.0029706, 0.0029706
|
|
||||||
209, 0.026184, 0.027171, 0.0039022, 0.9868, 0.95678, 0.96685, 0.69064, 0.026298, 0.022596, 0.0027955, 0.0029318, 0.0029318, 0.0029318
|
|
||||||
210, 0.025956, 0.025668, 0.0049788, 0.97688, 0.95678, 0.97237, 0.69002, 0.025732, 0.022511, 0.0025778, 0.0028932, 0.0028932, 0.0028932
|
|
||||||
211, 0.027297, 0.025791, 0.0040839, 0.98462, 0.9703, 0.96931, 0.6969, 0.026088, 0.0223, 0.0022371, 0.002855, 0.002855, 0.002855
|
|
||||||
212, 0.02525, 0.026303, 0.0047559, 0.9774, 0.96349, 0.96719, 0.69013, 0.025161, 0.022704, 0.0021132, 0.002817, 0.002817, 0.002817
|
|
||||||
213, 0.024295, 0.026973, 0.0041341, 0.97281, 0.97705, 0.9826, 0.68997, 0.024569, 0.022605, 0.0019869, 0.0027793, 0.0027793, 0.0027793
|
|
||||||
214, 0.024136, 0.025274, 0.0038694, 0.97589, 0.94326, 0.97578, 0.68725, 0.02548, 0.022043, 0.0018324, 0.0027419, 0.0027419, 0.0027419
|
|
||||||
215, 0.025528, 0.027067, 0.0040959, 0.96603, 0.9567, 0.96499, 0.68876, 0.024627, 0.022841, 0.0020294, 0.0027048, 0.0027048, 0.0027048
|
|
||||||
216, 0.025221, 0.027184, 0.0047706, 0.95868, 0.96353, 0.96453, 0.6809, 0.025527, 0.022776, 0.0021757, 0.0026681, 0.0026681, 0.0026681
|
|
||||||
217, 0.024044, 0.024836, 0.0042798, 0.98084, 0.95673, 0.96482, 0.68198, 0.025761, 0.022618, 0.002021, 0.0026316, 0.0026316, 0.0026316
|
|
||||||
218, 0.025253, 0.027373, 0.0041113, 0.96325, 0.9689, 0.97805, 0.69718, 0.025244, 0.021851, 0.0018038, 0.0025954, 0.0025954, 0.0025954
|
|
||||||
219, 0.025786, 0.024412, 0.0038547, 0.9784, 0.96354, 0.97334, 0.67628, 0.024993, 0.022038, 0.001677, 0.0025596, 0.0025596, 0.0025596
|
|
||||||
220, 0.024866, 0.025262, 0.0039628, 0.97738, 0.96354, 0.97295, 0.67586, 0.025099, 0.02182, 0.0016662, 0.0025241, 0.0025241, 0.0025241
|
|
||||||
221, 0.025464, 0.027381, 0.0037889, 0.95635, 0.97015, 0.96925, 0.70037, 0.024523, 0.021997, 0.0017885, 0.0024889, 0.0024889, 0.0024889
|
|
||||||
222, 0.024976, 0.025851, 0.0034427, 0.95665, 0.96354, 0.96272, 0.70145, 0.024242, 0.021824, 0.0018279, 0.0024541, 0.0024541, 0.0024541
|
|
||||||
223, 0.024802, 0.02694, 0.0031541, 0.95601, 0.96294, 0.9618, 0.68561, 0.024934, 0.022417, 0.0018971, 0.0024195, 0.0024195, 0.0024195
|
|
||||||
224, 0.024682, 0.026038, 0.0032441, 0.97713, 0.96354, 0.97524, 0.68246, 0.026721, 0.022906, 0.0020948, 0.0023854, 0.0023854, 0.0023854
|
|
||||||
225, 0.025083, 0.026697, 0.0037998, 0.96721, 0.9703, 0.97428, 0.68679, 0.02599, 0.022761, 0.0020577, 0.0023515, 0.0023515, 0.0023515
|
|
||||||
226, 0.024088, 0.025932, 0.0035885, 0.96876, 0.97703, 0.97508, 0.68276, 0.024445, 0.022962, 0.0019322, 0.002318, 0.002318, 0.002318
|
|
||||||
227, 0.023332, 0.025505, 0.0037625, 0.96578, 0.97705, 0.97358, 0.68462, 0.024101, 0.02255, 0.0018148, 0.0022849, 0.0022849, 0.0022849
|
|
||||||
228, 0.024702, 0.024819, 0.0034758, 0.97385, 0.97028, 0.97462, 0.69212, 0.024558, 0.022178, 0.0017297, 0.0022521, 0.0022521, 0.0022521
|
|
||||||
229, 0.024602, 0.025566, 0.0038347, 0.96628, 0.96353, 0.96616, 0.70351, 0.023898, 0.022227, 0.0016842, 0.0022196, 0.0022196, 0.0022196
|
|
||||||
230, 0.023372, 0.025582, 0.0035108, 0.98707, 0.9567, 0.96796, 0.6879, 0.024704, 0.022368, 0.0017089, 0.0021876, 0.0021876, 0.0021876
|
|
||||||
231, 0.024074, 0.025893, 0.0031354, 0.98694, 0.95678, 0.9676, 0.68024, 0.024659, 0.023358, 0.0017729, 0.0021558, 0.0021558, 0.0021558
|
|
||||||
232, 0.024966, 0.027324, 0.0032754, 0.9826, 0.95678, 0.9736, 0.70037, 0.02362, 0.022499, 0.001837, 0.0021245, 0.0021245, 0.0021245
|
|
||||||
233, 0.025124, 0.026055, 0.0039871, 0.9706, 0.96971, 0.97903, 0.70731, 0.0229, 0.021873, 0.0017568, 0.0020935, 0.0020935, 0.0020935
|
|
||||||
234, 0.022206, 0.02586, 0.0033446, 0.9787, 0.95003, 0.96954, 0.70051, 0.023775, 0.022511, 0.0017292, 0.0020629, 0.0020629, 0.0020629
|
|
||||||
235, 0.024355, 0.023389, 0.0040127, 0.98374, 0.9703, 0.96776, 0.68733, 0.022898, 0.022383, 0.0017637, 0.0020327, 0.0020327, 0.0020327
|
|
||||||
236, 0.024096, 0.026566, 0.0032116, 0.98492, 0.96354, 0.97267, 0.70113, 0.021918, 0.022037, 0.0017403, 0.0020028, 0.0020028, 0.0020028
|
|
||||||
237, 0.024077, 0.025841, 0.0032894, 0.97582, 0.96353, 0.967, 0.69301, 0.021952, 0.02197, 0.0017976, 0.0019734, 0.0019734, 0.0019734
|
|
||||||
238, 0.024347, 0.025564, 0.0039452, 0.97899, 0.96354, 0.96707, 0.69185, 0.022981, 0.022335, 0.0018357, 0.0019443, 0.0019443, 0.0019443
|
|
||||||
239, 0.023853, 0.024579, 0.0044756, 0.97532, 0.95678, 0.96534, 0.69215, 0.023931, 0.022646, 0.0018563, 0.0019156, 0.0019156, 0.0019156
|
|
||||||
240, 0.023002, 0.025647, 0.0035852, 0.97411, 0.96353, 0.97209, 0.69136, 0.023933, 0.022786, 0.0019303, 0.0018873, 0.0018873, 0.0018873
|
|
||||||
241, 0.023632, 0.025356, 0.0034878, 0.96367, 0.97026, 0.98007, 0.70663, 0.023831, 0.022384, 0.0019032, 0.0018594, 0.0018594, 0.0018594
|
|
||||||
242, 0.023527, 0.025798, 0.0036633, 0.97056, 0.96832, 0.97529, 0.70001, 0.024231, 0.022414, 0.001796, 0.0018319, 0.0018319, 0.0018319
|
|
||||||
243, 0.022682, 0.025564, 0.003487, 0.97515, 0.96351, 0.9668, 0.69187, 0.024127, 0.022944, 0.0017143, 0.0018048, 0.0018048, 0.0018048
|
|
||||||
244, 0.024222, 0.026106, 0.0035024, 0.97655, 0.95675, 0.96682, 0.69463, 0.023624, 0.022958, 0.0016919, 0.0017781, 0.0017781, 0.0017781
|
|
||||||
245, 0.02326, 0.025099, 0.0029967, 0.96808, 0.97681, 0.97562, 0.70458, 0.023273, 0.02276, 0.0016949, 0.0017519, 0.0017519, 0.0017519
|
|
||||||
246, 0.024062, 0.024739, 0.0040586, 0.96483, 0.9635, 0.97182, 0.69647, 0.023717, 0.022898, 0.0017371, 0.001726, 0.001726, 0.001726
|
|
||||||
247, 0.023201, 0.0253, 0.0032933, 0.97972, 0.95633, 0.97253, 0.69153, 0.024038, 0.022889, 0.0017084, 0.0017005, 0.0017005, 0.0017005
|
|
||||||
248, 0.024654, 0.026136, 0.0033753, 0.98866, 0.95667, 0.97459, 0.68796, 0.023902, 0.022352, 0.0017804, 0.0016755, 0.0016755, 0.0016755
|
|
||||||
249, 0.023838, 0.025222, 0.0033712, 0.96588, 0.9703, 0.97386, 0.69894, 0.023947, 0.022392, 0.0018015, 0.0016509, 0.0016509, 0.0016509
|
|
||||||
250, 0.023668, 0.024787, 0.0034169, 0.98732, 0.95003, 0.96765, 0.69254, 0.023956, 0.022749, 0.0017763, 0.0016267, 0.0016267, 0.0016267
|
|
||||||
251, 0.022859, 0.025103, 0.003568, 0.95814, 0.97702, 0.9737, 0.70092, 0.023604, 0.022479, 0.0017848, 0.0016029, 0.0016029, 0.0016029
|
|
||||||
252, 0.022666, 0.024463, 0.0032009, 0.98704, 0.97027, 0.97466, 0.69009, 0.023513, 0.022304, 0.0017728, 0.0015795, 0.0015795, 0.0015795
|
|
||||||
253, 0.023492, 0.026417, 0.0028279, 0.96436, 0.98372, 0.98019, 0.69935, 0.023358, 0.021836, 0.00174, 0.0015566, 0.0015566, 0.0015566
|
|
||||||
254, 0.024491, 0.025485, 0.002979, 0.9853, 0.95678, 0.96646, 0.69592, 0.023924, 0.022304, 0.0018352, 0.0015341, 0.0015341, 0.0015341
|
|
||||||
255, 0.02484, 0.025362, 0.0035361, 0.96452, 0.96353, 0.9651, 0.69518, 0.024445, 0.022635, 0.0018837, 0.0015121, 0.0015121, 0.0015121
|
|
||||||
256, 0.023908, 0.025521, 0.0036297, 0.96519, 0.96354, 0.96511, 0.6864, 0.024269, 0.022565, 0.0018298, 0.0014905, 0.0014905, 0.0014905
|
|
||||||
257, 0.02373, 0.025435, 0.0035398, 0.97944, 0.97703, 0.98018, 0.69367, 0.023299, 0.022476, 0.001668, 0.0014693, 0.0014693, 0.0014693
|
|
||||||
258, 0.023375, 0.025846, 0.0030582, 0.98528, 0.97531, 0.97812, 0.69421, 0.023276, 0.022128, 0.0015514, 0.0014486, 0.0014486, 0.0014486
|
|
||||||
259, 0.021545, 0.024249, 0.0026146, 0.97663, 0.97029, 0.97653, 0.71099, 0.023939, 0.022179, 0.0015795, 0.0014283, 0.0014283, 0.0014283
|
|
||||||
260, 0.022169, 0.025532, 0.0040977, 0.97732, 0.9703, 0.97464, 0.68688, 0.024342, 0.022362, 0.001622, 0.0014084, 0.0014084, 0.0014084
|
|
||||||
261, 0.022755, 0.024997, 0.0032319, 0.97619, 0.9703, 0.97406, 0.70667, 0.024472, 0.021516, 0.0016024, 0.001389, 0.001389, 0.001389
|
|
||||||
262, 0.023288, 0.02633, 0.0034072, 0.97402, 0.97705, 0.97597, 0.70608, 0.023844, 0.021676, 0.0016408, 0.0013701, 0.0013701, 0.0013701
|
|
||||||
263, 0.02379, 0.025776, 0.0036441, 0.97543, 0.98358, 0.98268, 0.70729, 0.024337, 0.022026, 0.0016763, 0.0013516, 0.0013516, 0.0013516
|
|
||||||
264, 0.02391, 0.024676, 0.0036971, 0.97962, 0.97023, 0.98219, 0.72072, 0.024265, 0.021404, 0.0017216, 0.0013336, 0.0013336, 0.0013336
|
|
||||||
265, 0.02313, 0.024861, 0.0031131, 0.97831, 0.97705, 0.9824, 0.71233, 0.023289, 0.021431, 0.001778, 0.001316, 0.001316, 0.001316
|
|
||||||
266, 0.022911, 0.023178, 0.0040326, 0.9855, 0.95678, 0.97393, 0.70797, 0.023489, 0.021917, 0.001843, 0.0012989, 0.0012989, 0.0012989
|
|
||||||
267, 0.022923, 0.025628, 0.0032744, 0.98466, 0.95676, 0.97389, 0.70614, 0.023732, 0.021756, 0.0018387, 0.0012822, 0.0012822, 0.0012822
|
|
||||||
268, 0.021734, 0.024794, 0.0037717, 0.97172, 0.9703, 0.9748, 0.69889, 0.023854, 0.021882, 0.0018406, 0.001266, 0.001266, 0.001266
|
|
||||||
269, 0.023233, 0.023997, 0.0026434, 0.98878, 0.95002, 0.97413, 0.69813, 0.024157, 0.022212, 0.0018839, 0.0012503, 0.0012503, 0.0012503
|
|
||||||
270, 0.022645, 0.024259, 0.0028535, 0.9733, 0.9703, 0.97535, 0.70777, 0.024298, 0.021893, 0.001847, 0.001235, 0.001235, 0.001235
|
|
||||||
271, 0.022842, 0.025684, 0.003931, 0.97335, 0.9703, 0.97546, 0.6987, 0.023966, 0.022058, 0.0018554, 0.0012202, 0.0012202, 0.0012202
|
|
||||||
272, 0.022658, 0.02243, 0.0029644, 0.98127, 0.97703, 0.97619, 0.70566, 0.024319, 0.022133, 0.0018315, 0.0012059, 0.0012059, 0.0012059
|
|
||||||
273, 0.021985, 0.023866, 0.003224, 0.97334, 0.97028, 0.97532, 0.71404, 0.02463, 0.021925, 0.0017855, 0.0011921, 0.0011921, 0.0011921
|
|
||||||
274, 0.021942, 0.025971, 0.0030934, 0.9921, 0.94045, 0.96804, 0.70548, 0.023867, 0.02181, 0.0017593, 0.0011787, 0.0011787, 0.0011787
|
|
||||||
275, 0.022654, 0.025224, 0.002868, 0.97576, 0.96344, 0.98036, 0.71113, 0.023825, 0.021833, 0.001792, 0.0011658, 0.0011658, 0.0011658
|
|
||||||
276, 0.022061, 0.023995, 0.0044932, 0.96396, 0.97519, 0.98094, 0.70244, 0.025151, 0.022106, 0.0018909, 0.0011533, 0.0011533, 0.0011533
|
|
||||||
277, 0.022082, 0.024009, 0.0043146, 0.9688, 0.96354, 0.96678, 0.69519, 0.025253, 0.022238, 0.0019813, 0.0011414, 0.0011414, 0.0011414
|
|
||||||
278, 0.022718, 0.025438, 0.0033991, 0.98149, 0.97029, 0.96863, 0.69842, 0.024404, 0.021862, 0.0021195, 0.0011299, 0.0011299, 0.0011299
|
|
||||||
279, 0.021459, 0.024987, 0.0033223, 0.98556, 0.96354, 0.97556, 0.70391, 0.024428, 0.021777, 0.002116, 0.0011189, 0.0011189, 0.0011189
|
|
||||||
280, 0.022531, 0.025328, 0.0029854, 0.97082, 0.97026, 0.98069, 0.70413, 0.024501, 0.021852, 0.0020207, 0.0011084, 0.0011084, 0.0011084
|
|
||||||
281, 0.021432, 0.02498, 0.0037385, 0.97039, 0.9703, 0.98025, 0.70643, 0.024077, 0.021808, 0.0018792, 0.0010983, 0.0010983, 0.0010983
|
|
||||||
282, 0.022082, 0.024033, 0.0035464, 0.97989, 0.97705, 0.98222, 0.70106, 0.023699, 0.021782, 0.0018304, 0.0010888, 0.0010888, 0.0010888
|
|
||||||
283, 0.022438, 0.026502, 0.0037712, 0.97219, 0.97702, 0.9805, 0.70248, 0.023944, 0.022022, 0.0018007, 0.0010797, 0.0010797, 0.0010797
|
|
||||||
284, 0.021952, 0.026204, 0.0030446, 0.9716, 0.977, 0.9802, 0.71073, 0.024671, 0.022283, 0.0017623, 0.0010711, 0.0010711, 0.0010711
|
|
||||||
285, 0.02244, 0.024696, 0.0031012, 0.97361, 0.9703, 0.98002, 0.71442, 0.024199, 0.022152, 0.0017836, 0.001063, 0.001063, 0.001063
|
|
||||||
286, 0.021664, 0.023039, 0.0027769, 0.97304, 0.96354, 0.97902, 0.70917, 0.023735, 0.022079, 0.0017841, 0.0010554, 0.0010554, 0.0010554
|
|
||||||
287, 0.022677, 0.025335, 0.0041636, 0.97759, 0.97705, 0.98093, 0.71153, 0.022869, 0.022005, 0.0017778, 0.0010483, 0.0010483, 0.0010483
|
|
||||||
288, 0.022281, 0.025492, 0.0036219, 0.98065, 0.97025, 0.98087, 0.70983, 0.022568, 0.022306, 0.0018028, 0.0010416, 0.0010416, 0.0010416
|
|
||||||
289, 0.023508, 0.024725, 0.0040105, 0.97198, 0.97023, 0.98068, 0.70539, 0.023018, 0.022493, 0.001852, 0.0010355, 0.0010355, 0.0010355
|
|
||||||
290, 0.023213, 0.024867, 0.0040221, 0.97374, 0.9703, 0.98036, 0.70964, 0.023247, 0.022265, 0.0018443, 0.0010298, 0.0010298, 0.0010298
|
|
||||||
291, 0.021429, 0.023571, 0.0036408, 0.97524, 0.96354, 0.97319, 0.7067, 0.023258, 0.022189, 0.0018254, 0.0010247, 0.0010247, 0.0010247
|
|
||||||
292, 0.021587, 0.024857, 0.0031796, 0.97449, 0.9703, 0.98051, 0.71474, 0.02327, 0.022178, 0.0018069, 0.00102, 0.00102, 0.00102
|
|
||||||
293, 0.022102, 0.02446, 0.0038188, 0.9726, 0.9703, 0.98018, 0.71203, 0.023105, 0.022171, 0.0017905, 0.0010158, 0.0010158, 0.0010158
|
|
||||||
294, 0.021959, 0.024979, 0.0029226, 0.98156, 0.9703, 0.97487, 0.70595, 0.022793, 0.022188, 0.0018304, 0.0010121, 0.0010121, 0.0010121
|
|
||||||
295, 0.021182, 0.025238, 0.0033527, 0.96627, 0.9703, 0.97301, 0.71552, 0.023089, 0.021885, 0.0018715, 0.0010089, 0.0010089, 0.0010089
|
|
||||||
296, 0.022339, 0.024582, 0.0034718, 0.96882, 0.97705, 0.98079, 0.719, 0.023042, 0.021523, 0.0018468, 0.0010062, 0.0010062, 0.0010062
|
|
||||||
297, 0.022934, 0.025444, 0.0036708, 0.97071, 0.97619, 0.9806, 0.72045, 0.023387, 0.021511, 0.0017952, 0.0010039, 0.0010039, 0.0010039
|
|
||||||
298, 0.022714, 0.024515, 0.004176, 0.97351, 0.97029, 0.98062, 0.71732, 0.023228, 0.021938, 0.0018586, 0.0010022, 0.0010022, 0.0010022
|
|
||||||
299, 0.022972, 0.026098, 0.0036222, 0.98, 0.97705, 0.98277, 0.70426, 0.023559, 0.022199, 0.0019408, 0.001001, 0.001001, 0.001001
|
|
||||||
|
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|
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|
|||||||
{
|
|
||||||
"algorithm": "random",
|
|
||||||
"parameters": {
|
|
||||||
"anchor_t": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
2,
|
|
||||||
8
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"batch_size": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
16,
|
|
||||||
32,
|
|
||||||
64
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"box": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0.02,
|
|
||||||
0.2
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"cls": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0.2
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"cls_pw": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0.5
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"copy_paste": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
1
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"degrees": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0,
|
|
||||||
45
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"epochs": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
5
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"fl_gamma": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"fliplr": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"flipud": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"hsv_h": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"hsv_s": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"hsv_v": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"iou_t": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0.7
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"lr0": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
1e-05,
|
|
||||||
0.1
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"lrf": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0.01,
|
|
||||||
1
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"mixup": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
1
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"momentum": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0.6
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"mosaic": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"obj": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0.2
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"obj_pw": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0.5
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"optimizer": {
|
|
||||||
"type": "categorical",
|
|
||||||
"values": [
|
|
||||||
"SGD",
|
|
||||||
"Adam",
|
|
||||||
"AdamW"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"perspective": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"scale": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"shear": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"translate": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"warmup_bias_lr": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0,
|
|
||||||
0.2
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"warmup_epochs": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
5
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"warmup_momentum": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0,
|
|
||||||
0.95
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"weight_decay": {
|
|
||||||
"type": "discrete",
|
|
||||||
"values": [
|
|
||||||
0,
|
|
||||||
0.001
|
|
||||||
]
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"spec": {
|
|
||||||
"maxCombo": 0,
|
|
||||||
"metric": "metrics/mAP_0.5",
|
|
||||||
"objective": "maximize"
|
|
||||||
},
|
|
||||||
"trials": 1
|
|
||||||
}
|
|
||||||
61
yolov3/Dockerfile
Normal file
61
yolov3/Dockerfile
Normal file
@ -0,0 +1,61 @@
|
|||||||
|
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||||
|
|
||||||
|
# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
|
||||||
|
FROM nvcr.io/nvidia/pytorch:21.10-py3
|
||||||
|
|
||||||
|
# Install linux packages
|
||||||
|
RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
|
||||||
|
|
||||||
|
# Install python dependencies
|
||||||
|
COPY requirements.txt .
|
||||||
|
RUN python -m pip install --upgrade pip
|
||||||
|
RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof
|
||||||
|
RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook wandb>=0.12.2
|
||||||
|
RUN pip install --no-cache -U torch torchvision numpy Pillow
|
||||||
|
# RUN pip install --no-cache torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
|
||||||
|
|
||||||
|
# Create working directory
|
||||||
|
RUN mkdir -p /usr/src/app
|
||||||
|
WORKDIR /usr/src/app
|
||||||
|
|
||||||
|
# Copy contents
|
||||||
|
COPY . /usr/src/app
|
||||||
|
|
||||||
|
# Downloads to user config dir
|
||||||
|
ADD https://ultralytics.com/assets/Arial.ttf /root/.config/Ultralytics/
|
||||||
|
|
||||||
|
# Set environment variables
|
||||||
|
# ENV HOME=/usr/src/app
|
||||||
|
|
||||||
|
|
||||||
|
# Usage Examples -------------------------------------------------------------------------------------------------------
|
||||||
|
|
||||||
|
# Build and Push
|
||||||
|
# t=ultralytics/yolov3:latest && sudo docker build -t $t . && sudo docker push $t
|
||||||
|
|
||||||
|
# Pull and Run
|
||||||
|
# t=ultralytics/yolov3:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
|
||||||
|
|
||||||
|
# Pull and Run with local directory access
|
||||||
|
# t=ultralytics/yolov3:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
|
||||||
|
|
||||||
|
# Kill all
|
||||||
|
# sudo docker kill $(sudo docker ps -q)
|
||||||
|
|
||||||
|
# Kill all image-based
|
||||||
|
# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov3:latest)
|
||||||
|
|
||||||
|
# Bash into running container
|
||||||
|
# sudo docker exec -it 5a9b5863d93d bash
|
||||||
|
|
||||||
|
# Bash into stopped container
|
||||||
|
# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash
|
||||||
|
|
||||||
|
# Clean up
|
||||||
|
# docker system prune -a --volumes
|
||||||
|
|
||||||
|
# Update Ubuntu drivers
|
||||||
|
# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
|
||||||
|
|
||||||
|
# DDP test
|
||||||
|
# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
|
||||||
33
yolov3/convert_pt_to_weight.py
Normal file
33
yolov3/convert_pt_to_weight.py
Normal file
@ -0,0 +1,33 @@
|
|||||||
|
import torch
|
||||||
|
from models.yolo import *
|
||||||
|
|
||||||
|
# Define the YOLOv3 model
|
||||||
|
# model = YOLO(num_classes=80)
|
||||||
|
model = Model('models/yolov3.yaml')
|
||||||
|
|
||||||
|
# Load the PyTorch .pt file
|
||||||
|
model.load_state_dict(torch.load('/home/parallels/ros2_ws/src/darknet_ros_fp16/darknet_ros/darknet_ros/yolo_network_config/weights/pipe_yolo3.pt'))
|
||||||
|
|
||||||
|
# Create a dictionary of layer names and weights
|
||||||
|
layer_weights = {}
|
||||||
|
for name, param in model.named_parameters():
|
||||||
|
if name.endswith('.bias'):
|
||||||
|
continue
|
||||||
|
layer_name = name.rsplit('.', 1)[0]
|
||||||
|
if layer_name not in layer_weights:
|
||||||
|
layer_weights[layer_name] = []
|
||||||
|
layer_weights[layer_name].append(param.detach().cpu().numpy())
|
||||||
|
|
||||||
|
# Write the weights to a binary file in Darknet's .weights format
|
||||||
|
with open('yolov3_pipe.weights', 'wb') as f:
|
||||||
|
for layer_name, weights in layer_weights.items():
|
||||||
|
header = [0, 0, 0, 0]
|
||||||
|
header[0] = weights[0].shape[0] # Number of filters
|
||||||
|
header[1] = weights[0].shape[1] # Number of channels
|
||||||
|
header[2] = weights[0].shape[2] # Filter height
|
||||||
|
header[3] = weights[0].shape[3] # Filter width
|
||||||
|
f.write(bytes(header))
|
||||||
|
|
||||||
|
for w in weights:
|
||||||
|
w = w.flatten()
|
||||||
|
f.write(w.tobytes())
|
||||||
|
Before Width: | Height: | Size: 476 KiB After Width: | Height: | Size: 476 KiB |
|
Before Width: | Height: | Size: 165 KiB After Width: | Height: | Size: 165 KiB |
248
yolov3/detect_oj.py
Normal file
248
yolov3/detect_oj.py
Normal file
@ -0,0 +1,248 @@
|
|||||||
|
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||||
|
"""
|
||||||
|
Run inference on images, videos, directories, streams, etc.
|
||||||
|
|
||||||
|
Usage:
|
||||||
|
$ python path/to/detect.py --weights yolov3.pt --source 0 # webcam
|
||||||
|
img.jpg # image
|
||||||
|
vid.mp4 # video
|
||||||
|
path/ # directory
|
||||||
|
path/*.jpg # glob
|
||||||
|
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
||||||
|
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||||
|
"""
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
import cv2
|
||||||
|
import torch
|
||||||
|
import torch.backends.cudnn as cudnn
|
||||||
|
|
||||||
|
FILE = Path(__file__).resolve()
|
||||||
|
ROOT = FILE.parents[0] # root directory
|
||||||
|
if str(ROOT) not in sys.path:
|
||||||
|
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||||
|
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||||
|
|
||||||
|
from models.common import DetectMultiBackend
|
||||||
|
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
|
||||||
|
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
|
||||||
|
increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
|
||||||
|
from utils.plots import Annotator, colors, save_one_box
|
||||||
|
from utils.torch_utils import select_device, time_sync
|
||||||
|
|
||||||
|
|
||||||
|
@torch.no_grad()
|
||||||
|
def run(weights=ROOT / 'yolov3.pt', # model.pt path(s)
|
||||||
|
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
|
||||||
|
imgsz=1280, # inference size (pixels)
|
||||||
|
conf_thres=0.25, # confidence threshold
|
||||||
|
iou_thres=0.45, # NMS IOU threshold
|
||||||
|
max_det=1000, # maximum detections per image
|
||||||
|
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||||
|
view_img=True, # show results
|
||||||
|
save_txt=False, # save results to *.txt
|
||||||
|
save_conf=False, # save confidences in --save-txt labels
|
||||||
|
save_crop=False, # save cropped prediction boxes
|
||||||
|
nosave=False, # do not save images/videos
|
||||||
|
classes=None, # filter by class: --class 0, or --class 0 2 3
|
||||||
|
agnostic_nms=False, # class-agnostic NMS
|
||||||
|
augment=False, # augmented inference
|
||||||
|
visualize=False, # visualize features
|
||||||
|
update=False, # update all models
|
||||||
|
project=ROOT / 'runs/detect', # save results to project/name
|
||||||
|
name='exp', # save results to project/name
|
||||||
|
exist_ok=False, # existing project/name ok, do not increment
|
||||||
|
line_thickness=3, # bounding box thickness (pixels)
|
||||||
|
hide_labels=False, # hide labels
|
||||||
|
hide_conf=False, # hide confidences
|
||||||
|
half=False, # use FP16 half-precision inference
|
||||||
|
dnn=False, # use OpenCV DNN for ONNX inference
|
||||||
|
):
|
||||||
|
|
||||||
|
source = str(source)
|
||||||
|
save_img = not nosave and not source.endswith('.txt') # save inference images
|
||||||
|
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||||||
|
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
|
||||||
|
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
|
||||||
|
if is_url and is_file:
|
||||||
|
source = check_file(source) # download
|
||||||
|
|
||||||
|
# Directories
|
||||||
|
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
||||||
|
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||||
|
|
||||||
|
# Load model
|
||||||
|
device = select_device(device)
|
||||||
|
model = DetectMultiBackend(weights, device=device, dnn=dnn)
|
||||||
|
stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
|
||||||
|
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||||
|
|
||||||
|
# Half
|
||||||
|
half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
|
||||||
|
if pt:
|
||||||
|
model.model.half() if half else model.model.float()
|
||||||
|
# Dataloader
|
||||||
|
if webcam:
|
||||||
|
view_img = check_imshow()
|
||||||
|
cudnn.benchmark = True # set True to speed up constant image size inference
|
||||||
|
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
|
||||||
|
bs = len(dataset) # batch_size
|
||||||
|
else:
|
||||||
|
print("in load images")
|
||||||
|
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
|
||||||
|
bs = 1 # batch_size
|
||||||
|
vid_path, vid_writer = [None] * bs, [None] * bs
|
||||||
|
|
||||||
|
# Run inference
|
||||||
|
if pt and device.type != 'cpu':
|
||||||
|
model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters()))) # warmup
|
||||||
|
dt, seen = [0.0, 0.0, 0.0], 0
|
||||||
|
|
||||||
|
# Inference
|
||||||
|
for path, im, im0s, vid_cap, s in dataset:
|
||||||
|
t1 = time_sync()
|
||||||
|
im = torch.from_numpy(im).to(device)
|
||||||
|
im = im.half() if half else im.float() # uint8 to fp16/32
|
||||||
|
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||||
|
if len(im.shape) == 3:
|
||||||
|
im = im[None] # expand for batch dim
|
||||||
|
t2 = time_sync()
|
||||||
|
dt[0] += t2 - t1
|
||||||
|
|
||||||
|
# Inference
|
||||||
|
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||||||
|
pred = model(im, augment=augment, visualize=visualize)
|
||||||
|
t3 = time_sync()
|
||||||
|
dt[1] += t3 - t2
|
||||||
|
|
||||||
|
# NMS
|
||||||
|
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
|
||||||
|
dt[2] += time_sync() - t3
|
||||||
|
|
||||||
|
# Second-stage classifier (optional)
|
||||||
|
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
|
||||||
|
|
||||||
|
# Process predictions
|
||||||
|
for i, det in enumerate(pred): # per image
|
||||||
|
seen += 1
|
||||||
|
if webcam: # batch_size >= 1
|
||||||
|
p, im0, frame = path[i], im0s[i].copy(), dataset.count
|
||||||
|
s += f'{i}: '
|
||||||
|
else:
|
||||||
|
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
|
||||||
|
|
||||||
|
p = Path(p) # to Path
|
||||||
|
save_path = str(save_dir / p.name) # im.jpg
|
||||||
|
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
|
||||||
|
s += '%gx%g ' % im.shape[2:] # print string
|
||||||
|
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||||
|
imc = im0.copy() if save_crop else im0 # for save_crop
|
||||||
|
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
|
||||||
|
if len(det):
|
||||||
|
# Rescale boxes from img_size to im0 size
|
||||||
|
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
|
||||||
|
|
||||||
|
# Print results
|
||||||
|
for c in det[:, -1].unique():
|
||||||
|
n = (det[:, -1] == c).sum() # detections per class
|
||||||
|
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||||
|
|
||||||
|
# Write results
|
||||||
|
for *xyxy, conf, cls in reversed(det):
|
||||||
|
if save_txt: # Write to file
|
||||||
|
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||||||
|
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
|
||||||
|
with open(txt_path + '.txt', 'a') as f:
|
||||||
|
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||||
|
|
||||||
|
if save_img or save_crop or view_img: # Add bbox to image
|
||||||
|
c = int(cls) # integer class
|
||||||
|
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
|
||||||
|
annotator.box_label(xyxy, label, color=colors(c, True))
|
||||||
|
if save_crop:
|
||||||
|
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
|
||||||
|
|
||||||
|
# Print time (inference-only)
|
||||||
|
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
|
||||||
|
import pdb; pdb.set_trace()
|
||||||
|
|
||||||
|
# Stream results
|
||||||
|
im0 = annotator.result()
|
||||||
|
if True:
|
||||||
|
cv2.imshow(str(p), im0)
|
||||||
|
cv2.waitKey(1) # 1 millisecond
|
||||||
|
|
||||||
|
# Save results (image with detections)
|
||||||
|
if save_img:
|
||||||
|
if dataset.mode == 'image':
|
||||||
|
cv2.imwrite(save_path, im0)
|
||||||
|
else: # 'video' or 'stream'
|
||||||
|
if vid_path[i] != save_path: # new video
|
||||||
|
vid_path[i] = save_path
|
||||||
|
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||||||
|
vid_writer[i].release() # release previous video writer
|
||||||
|
if vid_cap: # video
|
||||||
|
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||||||
|
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||||
|
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||||
|
else: # stream
|
||||||
|
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||||
|
save_path += '.mp4'
|
||||||
|
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||||
|
vid_writer[i].write(im0)
|
||||||
|
|
||||||
|
# Print results
|
||||||
|
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
|
||||||
|
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
|
||||||
|
if save_txt or save_img:
|
||||||
|
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
|
||||||
|
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||||||
|
if update:
|
||||||
|
strip_optimizer(weights) # update model (to fix SourceChangeWarning)
|
||||||
|
|
||||||
|
|
||||||
|
def parse_opt():
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov3.pt', help='model path(s)')
|
||||||
|
parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
|
||||||
|
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
|
||||||
|
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
|
||||||
|
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
|
||||||
|
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
|
||||||
|
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||||
|
parser.add_argument('--view-img', action='store_true', help='show results')
|
||||||
|
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
|
||||||
|
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
|
||||||
|
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
|
||||||
|
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
|
||||||
|
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
|
||||||
|
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
|
||||||
|
parser.add_argument('--augment', action='store_true', help='augmented inference')
|
||||||
|
parser.add_argument('--visualize', action='store_true', help='visualize features')
|
||||||
|
parser.add_argument('--update', action='store_true', help='update all models')
|
||||||
|
parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
|
||||||
|
parser.add_argument('--name', default='exp', help='save results to project/name')
|
||||||
|
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||||
|
parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
|
||||||
|
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
|
||||||
|
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
|
||||||
|
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
|
||||||
|
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
|
||||||
|
opt = parser.parse_args()
|
||||||
|
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||||
|
print_args(FILE.stem, opt)
|
||||||
|
return opt
|
||||||
|
|
||||||
|
|
||||||
|
def main(opt):
|
||||||
|
check_requirements(exclude=('tensorboard', 'thop'))
|
||||||
|
run(**vars(opt))
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
opt = parse_opt()
|
||||||
|
main(opt)
|
||||||
33
yolov3/launch/pipe_detection.launch.py
Normal file
33
yolov3/launch/pipe_detection.launch.py
Normal file
@ -0,0 +1,33 @@
|
|||||||
|
|
||||||
|
from launch import LaunchDescription
|
||||||
|
from launch.actions import DeclareLaunchArgument, SetEnvironmentVariable
|
||||||
|
from launch.substitutions import LaunchConfiguration
|
||||||
|
from launch_ros.actions import Node
|
||||||
|
|
||||||
|
def generate_launch_description():
|
||||||
|
|
||||||
|
return LaunchDescription([
|
||||||
|
|
||||||
|
# SetEnvironmentVariable('RCUTILS_LOGGING_USE_STDOUT', '1'),
|
||||||
|
# SetEnvironmentVariable('RCUTILS_LOGGING_BUFFERED_STREAM', '0'),
|
||||||
|
|
||||||
|
# # Launch arguments
|
||||||
|
# DeclareLaunchArgument('object_prefix', default_value='testingggggg', description='TF prefix of objects.'),
|
||||||
|
|
||||||
|
# DeclareLaunchArgument('rgb_topic', default_value='rgb_img', description='Image topic.'),
|
||||||
|
# DeclareLaunchArgument('depth_topic', default_value='depth_img', description='Registered depth topic.'),
|
||||||
|
# DeclareLaunchArgument('camera_info_topic', default_value='camera_info', description='Camera info topic.'),
|
||||||
|
# DeclareLaunchArgument('bounding_box_topic', default_value='bboxes', description='Bouding box topic.'),
|
||||||
|
|
||||||
|
# Nodes to launch
|
||||||
|
Node(
|
||||||
|
package='yolov3_msg', executable='detect_on_bag.py', output='screen',
|
||||||
|
# parameters=[{
|
||||||
|
# 'object_prefix':LaunchConfiguration('object_prefix'),
|
||||||
|
# }],
|
||||||
|
# remappings=[
|
||||||
|
# ('rgb_img', LaunchConfiguration('rgb_topic')),
|
||||||
|
# ('depth_img', LaunchConfiguration('depth_topic')),
|
||||||
|
# ('camera_info', LaunchConfiguration('camera_info_topic'))]
|
||||||
|
),
|
||||||
|
])
|
||||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
x
Reference in New Issue
Block a user