Implementation of "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors"
This implimentation is based on yolov5.
All of installation, data preparation, and usage are as same as yolov5.
python segment/train.py --data coco.yaml --batch 16 --weights '' --cfg yolov7-seg.yaml --epochs 300 --name yolov7-seg --img 640 --hyp hyp.scratch-high.yamlObject detection:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.49629
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.67746
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.53842
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.32679
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55475
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.63948
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.37569
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.61747
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.66796
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.49381
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.72859
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.81632
Instance segmentation:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.40531
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.64003
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.42996
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.22329
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.46102
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.56453
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.32220
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.51069
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.54511
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.34929
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.61070
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.72177
Object detection:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51650
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.69517
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.56113
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.34761
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.57255
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66059
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38840
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.63697
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.68754
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.51488
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.74506
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83554
Instance segmentation:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.41901
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.65833
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.44483
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.23123
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.47414
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.58149
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.33164
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.52327
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.55738
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35367
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.62091
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.74205
Object detection:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51100
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.68832
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.55593
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.34518
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.56570
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.65318
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38180
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.63630
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.68619
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.52338
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.74392
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.82744
Instance segmentation:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.41422
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.65154
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.44169
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.23393
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.46379
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.57722
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.32766
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.52457
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.55851
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35866
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.61833
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.74352
update yolor modling
Object detection:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51384
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.69383
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.55794
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.34061
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.56557
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66566
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38459
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.63617
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.68621
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.52662
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73997
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83435
Instance segmentation:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.41483
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.65499
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.43665
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.22629
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.46021
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.58898
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.32963
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.52450
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.55855
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.36392
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.61302
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.74983