Closed sure7018 closed 2 years ago
If I do not have the above modifications, an error will be reported: [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5000/5000, 5.1 task/s, elapsed: 978s, ETA: 0s Evaluating bbox... Loading and preparing results... The testing results of the whole dataset is empty.
Object instance annotations in COCO format should contain the area
field.
Please modify your json files by calculating width * height
to assign area
.
the following errors
What do the errors mean?
Low AP and nan (loss: inf
) may be caused by training instability (e.g., too high learning rate).
I recommend using simple detectors (e.g., retinanet_r50_fpn_1x_coco.py
) to debug your dataset before trying recent detectors.
Especially, detectors not supported in the original mmdetection have not been verified by many users.
Hello, if the dataset I use is marked in Chinese, how can I modify the dataset to read Chinese text?
I haven't tried category names written in Chinese. Are there any errors?
I just changed Chinese into English, and found or reported the same error, as shown below. Should it be the problem of my data set? What do you think?
2021-10-09 16:31:16,491 - mmdet - INFO - Saving checkpoint at 1 epochs [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 7320/7313, 65.4 task/s, elapsed: 112s, ETA: 0s
2021-10-09 16:37:00,650 - mmdet - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.52s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type bbox
Traceback (most recent call last):
File "tools/train.py", line 189, in
Thank you for your reply. I think I have found the error
Hello, when I was training my own data set in coco format, because my data set did not have a mask, I put if Ann ['area '] < = 0 or W < 1 or H < 1 in line 133 of mmdet.datasets.coco.py change to if w < 1 or H < 1, but there are the following errors:
2021-10-03 16:08:17,352 - mmcv - INFO - Reducer buckets have been rebuilt in this iteration. 2021-10-03 16:11:31,063 - mmdet - INFO - Epoch [1][50/367] lr: 9.890e-04, eta: 8:37:53, time: 4.262, data_time: 0.331, memory: 9512, kpt_loss_point_cls: 1.1301, kpt_loss_point_offset: 0.0854, bbox_loss_cls: 1.1355, bbox_loss_bbox: 0.6738, loss: 3.0248 2021-10-03 16:14:54,468 - mmdet - INFO - Epoch [1][100/367] lr: 1.988e-03, eta: 8:22:37, time: 4.068, data_time: 0.006, memory: 9512, kpt_loss_point_cls: 0.9700, kpt_loss_point_offset: 0.0889, bbox_loss_cls: 1.1190, bbox_loss_bbox: 0.6012, loss: 2.7791 2021-10-03 16:18:09,565 - mmdet - INFO - Epoch [1][150/367] lr: 2.987e-03, eta: 8:08:37, time: 3.902, data_time: 0.007, memory: 9512, kpt_loss_point_cls: 0.8648, kpt_loss_point_offset: 0.0878, bbox_loss_cls: 1.1331, bbox_loss_bbox: 0.5795, loss: 2.6652 2021-10-03 16:21:27,164 - mmdet - INFO - Epoch [1][200/367] lr: 3.986e-03, eta: 8:01:29, time: 3.952, data_time: 0.007, memory: 9541, kpt_loss_point_cls: 0.7960, kpt_loss_point_offset: 0.0863, bbox_loss_cls: 1.1200, bbox_loss_bbox: 0.5794, loss: 2.5817 2021-10-03 16:24:47,569 - mmdet - INFO - Epoch [1][250/367] lr: 4.985e-03, eta: 7:57:13, time: 4.008, data_time: 0.007, memory: 9541, kpt_loss_point_cls: 0.8372, kpt_loss_point_offset: 0.0843, bbox_loss_cls: 1.1260, bbox_loss_bbox: 0.5690, loss: 2.6165 2021-10-03 16:28:09,630 - mmdet - INFO - Epoch [1][300/367] lr: 5.984e-03, eta: 7:53:54, time: 4.041, data_time: 0.007, memory: 9541, kpt_loss_point_cls: 0.6968, kpt_loss_point_offset: 0.0834, bbox_loss_cls: 1.1199, bbox_loss_bbox: 0.5709, loss: 2.4710 2021-10-03 16:31:30,102 - mmdet - INFO - Epoch [1][350/367] lr: 6.983e-03, eta: 7:50:03, time: 4.009, data_time: 0.006, memory: 10288, kpt_loss_point_cls: 0.8081, kpt_loss_point_offset: 0.0854, bbox_loss_cls: 1.1074, bbox_loss_bbox: inf, loss: inf 2021-10-03 16:32:38,249 - mmdet - INFO - Saving checkpoint at 1 epochs [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 420/420, 5.1 task/s, elapsed: 83s, ETA: 0s
2021-10-03 16:34:11,594 - mmdet - INFO - Evaluating bbox... Loading and preparing results... DONE (t=0.14s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=2.98s). Accumulating evaluation results... DONE (t=1.01s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.002 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.002 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.002 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.001 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.004 2021-10-03 16:34:15,975 - mmdet - INFO - Exp name: bvr_retinanet_x101_fpn_dcn_mstrain_400_1200_20e_coco.py 2021-10-03 16:34:15,975 - mmdet - INFO - Epoch(val) [1][210] bbox_mAP: 0.0000, bbox_mAP_50: 0.0000, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0000, bbox_mAP_copypaste: 0.000 0.000 0.000 0.000 0.000 0.000
How should I modify it, My training model is configs/bvr/bvr_retinanet_x101_fpn_dcn_mstrain_400_1200_20e_coco.py.