Zenn
Open4

cell-phone

PINTOPINTO
  • Normal dataset

score threshold >= 0.65

python train_dual.py \
--workers 8 \
--device 0 \
--batch 16 \
--data data/original.yaml \
--img 640 \
--cfg models/detect/yolov9-n_original.yaml \
--weights best-n.pt \
--name yolov9-cellphone-n \
--hyp hyp.scratch-high_original.yaml \
--min-items 0 \
--epochs 200 \
--close-mosaic 15

python train_dual.py \
--workers 8 \
--device 0 \
--batch 16 \
--data data/original.yaml \
--img 640 \
--cfg models/detect/yolov9-n_original.yaml \
--weights best-n-phone.pt \
--name yolov9-cellphone-n \
--hyp hyp.scratch-high_original.yaml \
--min-items 0 \
--epochs 100 \
--close-mosaic 15


##################

python train_dual.py \
--workers 8 \
--device 0 \
--batch 16 \
--data data/original.yaml \
--img 640 \
--cfg models/detect/yolov9-t_original.yaml \
--weights yolov9-t-converted.pt \
--name yolov9-cellphone-t \
--hyp hyp.scratch-high_original.yaml \
--min-items 0 \
--epochs 100 \
--close-mosaic 15

python train_dual.py \
--workers 8 \
--device 0 \
--batch 16 \
--data data/original.yaml \
--img 640 \
--cfg models/detect/yolov9-t_original.yaml \
--weights best-t-phone.pt \
--name yolov9-cellphone-t \
--hyp hyp.scratch-high_original.yaml \
--min-items 0 \
--epochs 100 \
--close-mosaic 15

##################

python train_dual.py \
--workers 8 \
--device 0 \
--batch 8 \
--data data/original.yaml \
--img 640 \
--cfg models/detect/yolov9-s_original.yaml \
--weights yolov9-s-converted.pt \
--name yolov9-cellphone-s \
--hyp hyp.scratch-high_original.yaml \
--min-items 0 \
--epochs 100 \
--close-mosaic 15

python train_dual.py \
--workers 8 \
--device 0 \
--batch 16 \
--data data/original.yaml \
--img 640 \
--cfg models/detect/yolov9-s_original.yaml \
--weights best-s-phone.pt \
--name yolov9-cellphone-s \
--hyp hyp.scratch-high_original.yaml \
--min-items 0 \
--epochs 100 \
--close-mosaic 15

##################

python train_dual.py \
--workers 8 \
--device 0 \
--batch 4 \
--data data/original.yaml \
--img 640 \
--cfg models/detect/yolov9-e_original.yaml \
--weights yolov9-e-converted.pt \
--name yolov9-cellphone-e \
--hyp hyp.scratch-high_original.yaml \
--min-items 0 \
--epochs 100 \
--close-mosaic 15

python train_dual.py \
--workers 8 \
--device 0 \
--batch 4 \
--data data/original.yaml \
--img 640 \
--cfg models/detect/yolov9-e_original.yaml \
--weights best-e-phone.pt \
--name yolov9-cellphone-e \
--hyp hyp.scratch-high_original.yaml \
--min-items 0 \
--epochs 100 \
--close-mosaic 15
PINTOPINTO
  • N - No-dist
Class     Images  Instances          P          R      mAP50   mAP50-95
  all       1004       1245      0.647      0.471      0.521      0.329
  • T - No-dist
Class     Images  Instances          P          R      mAP50   mAP50-95
  all       1004       1245      0.742      0.547      0.615      0.412
  • S - No-dist
Class     Images  Instances          P          R      mAP50   mAP50-95
  all       1004       1245      0.779      0.618      0.686      0.478
PINTOPINTO
  • N - Dist
Class     Images  Instances          P          R      mAP50   mAP50-95
  all       2007       2676      0.706      0.517      0.582      0.389
  • T - Dist
Class     Images  Instances          P          R      mAP50   mAP50-95
  all       2007       2676      0.792      0.647      0.711      0.505
  • S - Dist
Class     Images  Instances          P          R      mAP50   mAP50-95
  all       2007       2676      0.885      0.704      0.792      0.590
  • E - Dist - First
Class     Images  Instances          P          R      mAP50   mAP50-95
  all       2007       2676      0.896      0.732      0.805      0.612