paddleDetection train log
[04/02 14:10:27] ppdet.engine INFO: Epoch: [99] [14520/14658] learning_rate: 0.000001 loss_xy: 12.273930 loss_wh: 3.473294 loss_obj: 20.448202 loss_cls: 8.252794 loss: 44.945595 eta: 0:01:04 batch_cost: 0.4589 data_cost: 0.0002 ips: 17.4342 images/s [04/02 14:10:36] ppdet.engine INFO: Epoch: [99] [14540/14658] learning_rate: 0.000001 loss_xy: 14.349242 loss_wh: 3.617645 loss_obj: 24.267742 loss_cls: 7.921866 loss: 47.407486 eta: 0:00:55 batch_cost: 0.4359 data_cost: 0.0002 ips: 18.3538 images/s [04/02 14:10:46] ppdet.engine INFO: Epoch: [99] [14560/14658] learning_rate: 0.000001 loss_xy: 11.139051 loss_wh: 3.144288 loss_obj: 16.946981 loss_cls: 7.191289 loss: 39.561420 eta: 0:00:45 batch_cost: 0.5152 data_cost: 0.0002 ips: 15.5265 images/s [04/02 14:10:55] ppdet.engine INFO: Epoch: [99] [14580/14658] learning_rate: 0.000001 loss_xy: 10.631355 loss_wh: 2.776225 loss_obj: 15.628025 loss_cls: 6.776411 loss: 37.904350 eta: 0:00:36 batch_cost: 0.4477 data_cost: 0.0002 ips: 17.8697 images/s [04/02 14:11:04] ppdet.engine INFO: Epoch: [99] [14600/14658] learning_rate: 0.000001 loss_xy: 12.825715 loss_wh: 3.540399 loss_obj: 21.935436 loss_cls: 8.111532 loss: 46.578396 eta: 0:00:27 batch_cost: 0.4439 data_cost: 0.0002 ips: 18.0203 images/s [04/02 14:11:15] ppdet.engine INFO: Epoch: [99] [14620/14658] learning_rate: 0.000001 loss_xy: 12.241335 loss_wh: 3.509616 loss_obj: 20.464123 loss_cls: 6.314967 loss: 43.114769 eta: 0:00:17 batch_cost: 0.5226 data_cost: 0.0002 ips: 15.3086 images/s [04/02 14:11:24] ppdet.engine INFO: Epoch: [99] [14640/14658] learning_rate: 0.000001 loss_xy: 11.870386 loss_wh: 3.170647 loss_obj: 19.741116 loss_cls: 7.042663 loss: 39.897247 eta: 0:00:08 batch_cost: 0.4427 data_cost: 0.0002 ips: 18.0702 images/s [04/02 14:11:32] ppdet.utils.checkpoint INFO: Save checkpoint: output/yolov3_darknet53_100e_coco [04/02 14:11:32] ppdet.engine INFO: Eval iter: 0 [04/02 14:11:36] ppdet.engine INFO: Eval iter: 100 [04/02 14:11:41] ppdet.engine INFO: Eval iter: 200 [04/02 14:11:46] ppdet.engine INFO: Eval iter: 300 [04/02 14:11:50] ppdet.engine INFO: Eval iter: 400 [04/02 14:11:55] ppdet.engine INFO: Eval iter: 500 [04/02 14:11:59] ppdet.engine INFO: Eval iter: 600 [04/02 14:12:04] ppdet.engine INFO: Eval iter: 700 [04/02 14:12:08] ppdet.engine INFO: Eval iter: 800 [04/02 14:12:13] ppdet.engine INFO: Eval iter: 900 [04/02 14:12:18] ppdet.engine INFO: Eval iter: 1000 [04/02 14:12:22] ppdet.engine INFO: Eval iter: 1100 [04/02 14:12:27] ppdet.engine INFO: Eval iter: 1200 [04/02 14:12:31] ppdet.engine INFO: Eval iter: 1300 [04/02 14:12:36] ppdet.engine INFO: Eval iter: 1400 [04/02 14:12:40] ppdet.engine INFO: Eval iter: 1500 [04/02 14:12:45] ppdet.engine INFO: Eval iter: 1600 [04/02 14:12:49] ppdet.engine INFO: Eval iter: 1700 [04/02 14:12:54] ppdet.engine INFO: Eval iter: 1800 [04/02 14:12:59] ppdet.engine INFO: Eval iter: 1900 [04/02 14:13:03] ppdet.engine INFO: Eval iter: 2000 [04/02 14:13:08] ppdet.engine INFO: Eval iter: 2100 [04/02 14:13:12] ppdet.engine INFO: Eval iter: 2200 [04/02 14:13:17] ppdet.engine INFO: Eval iter: 2300 [04/02 14:13:21] ppdet.engine INFO: Eval iter: 2400 [04/02 14:13:26] ppdet.engine INFO: Eval iter: 2500 [04/02 14:13:30] ppdet.engine INFO: Eval iter: 2600 [04/02 14:13:35] ppdet.engine INFO: Eval iter: 2700 [04/02 14:13:39] ppdet.engine INFO: Eval iter: 2800 [04/02 14:13:44] ppdet.engine INFO: Eval iter: 2900 [04/02 14:13:48] ppdet.engine INFO: Eval iter: 3000 [04/02 14:13:53] ppdet.engine INFO: Eval iter: 3100 [04/02 14:13:57] ppdet.engine INFO: Eval iter: 3200 [04/02 14:14:02] ppdet.engine INFO: Eval iter: 3300 [04/02 14:14:07] ppdet.engine INFO: Eval iter: 3400 [04/02 14:14:11] ppdet.engine INFO: Eval iter: 3500 [04/02 14:14:16] ppdet.engine INFO: Eval iter: 3600 [04/02 14:14:20] ppdet.engine INFO: Eval iter: 3700 [04/02 14:14:24] ppdet.engine INFO: Eval iter: 3800 [04/02 14:14:29] ppdet.engine INFO: Eval iter: 3900 [04/02 14:14:33] ppdet.engine INFO: Eval iter: 4000 [04/02 14:14:38] ppdet.engine INFO: Eval iter: 4100 [04/02 14:14:42] ppdet.engine INFO: Eval iter: 4200 [04/02 14:14:47] ppdet.engine INFO: Eval iter: 4300 [04/02 14:14:52] ppdet.engine INFO: Eval iter: 4400 [04/02 14:14:56] ppdet.engine INFO: Eval iter: 4500 [04/02 14:15:01] ppdet.engine INFO: Eval iter: 4600 [04/02 14:15:05] ppdet.engine INFO: Eval iter: 4700 [04/02 14:15:10] ppdet.engine INFO: Eval iter: 4800 [04/02 14:15:14] ppdet.engine INFO: Eval iter: 4900 [04/02 14:15:19] ppdet.metrics.metrics INFO: The bbox result is saved to bbox.json. loading annotations into memory... Done (t=0.66s) creating index... index created! [04/02 14:15:20] ppdet.metrics.coco_utils INFO: Start evaluate... Loading and preparing results... DONE (t=1.19s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=32.16s). Accumulating evaluation results... DONE (t=5.02s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.558 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.345 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.186 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.358 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.426 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.277 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.434 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.286 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.482 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572 [04/02 14:15:59] ppdet.engine INFO: Total sample number: 4952, averge FPS: 21.99071245254403 [04/02 14:15:59] ppdet.engine INFO: Best test bbox ap is 0.329. (wind_paddle) bim@bim-PowerEdge-R730:~/Desktop/PaddlePaddle_Project/PaddleDetection-release-2.3$ (wind_paddle) bim@bim-PowerEdge-R730:~/Desktop/PaddlePaddle_Project/PaddleDetection-release-2.3$ (wind_paddle) bim@bim-PowerEdge-R730:~/Desktop/PaddlePaddle_Project/PaddleDetection-release-2.3$
output model:
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