Open SuGoe-Young opened 1 year ago
I have uesd 2_train.sh to training and eval, but the output show down below is very abnormal.
INFO json_dataset_evaluator.py: 243: ~~~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~~~ INFO json_dataset_evaluator.py: 244: 13.5 INFO json_dataset_evaluator.py: 254: person: 7.5 INFO json_dataset_evaluator.py: 254: bicylce: 0.8 INFO json_dataset_evaluator.py: 254: car: 33.0 INFO json_dataset_evaluator.py: 254: motorcycle: 7.0 INFO json_dataset_evaluator.py: 254: bus: 20.3 INFO json_dataset_evaluator.py: 254: truck: 12.3 INFO json_dataset_evaluator.py: 255: ~~~~ Summary metrics ~~~~ Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.135 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.288 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.114 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.097 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.258 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.270 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.061 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.227 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.387 Average Precision (AP) @[ IoU=0.50 | area= small | maxDets=100 ] = 0.054 Average Precision (AP) @[ IoU=0.50 | area=medium | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50 | area= large | maxDets=100 ] = 0.445 Average Precision (AP) @[ IoU=0.75 | area= small | maxDets=100 ] = 0.007 Average Precision (AP) @[ IoU=0.75 | area=medium | maxDets=100 ] = 0.065 Average Precision (AP) @[ IoU=0.75 | area= large | maxDets=100 ] = 0.193 INFO json_dataset_evaluator.py: 220: Wrote json eval results to: /RRPN/data/models/X_101_32x8d_FPN_1x_nucoco/test/nucoco_val/generalized_rcnn/detection_results.pkl INFO task_evaluation.py: 62: Evaluating bounding boxes is done! INFO task_evaluation.py: 181: copypaste: Dataset: nucoco_val INFO task_evaluation.py: 183: copypaste: Task: box INFO task_evaluation.py: 186: copypaste: AP,AP50,AP75,APs,APm,APl INFO task_evaluation.py: 187: copypaste: 0.1350,0.2879,0.1144,0.0186,0.0968,0.2173 INFO: Done!
I have uesd 2_train.sh to training and eval, but the output show down below is very abnormal.
INFO json_dataset_evaluator.py: 243: ~~~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~~~ INFO json_dataset_evaluator.py: 244: 13.5 INFO json_dataset_evaluator.py: 254: person: 7.5 INFO json_dataset_evaluator.py: 254: bicylce: 0.8 INFO json_dataset_evaluator.py: 254: car: 33.0 INFO json_dataset_evaluator.py: 254: motorcycle: 7.0 INFO json_dataset_evaluator.py: 254: bus: 20.3 INFO json_dataset_evaluator.py: 254: truck: 12.3 INFO json_dataset_evaluator.py: 255: ~~~~ Summary metrics ~~~~ Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.135 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.288 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.114 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.019 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.097 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.217 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.147 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.258 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.270 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.061 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.227 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.387 Average Precision (AP) @[ IoU=0.50 | area= small | maxDets=100 ] = 0.054 Average Precision (AP) @[ IoU=0.50 | area=medium | maxDets=100 ] = 0.233 Average Precision (AP) @[ IoU=0.50 | area= large | maxDets=100 ] = 0.445 Average Precision (AP) @[ IoU=0.75 | area= small | maxDets=100 ] = 0.007 Average Precision (AP) @[ IoU=0.75 | area=medium | maxDets=100 ] = 0.065 Average Precision (AP) @[ IoU=0.75 | area= large | maxDets=100 ] = 0.193 INFO json_dataset_evaluator.py: 220: Wrote json eval results to: /RRPN/data/models/X_101_32x8d_FPN_1x_nucoco/test/nucoco_val/generalized_rcnn/detection_results.pkl INFO task_evaluation.py: 62: Evaluating bounding boxes is done! INFO task_evaluation.py: 181: copypaste: Dataset: nucoco_val INFO task_evaluation.py: 183: copypaste: Task: box INFO task_evaluation.py: 186: copypaste: AP,AP50,AP75,APs,APm,APl INFO task_evaluation.py: 187: copypaste: 0.1350,0.2879,0.1144,0.0186,0.0968,0.2173 INFO: Done!