init repo

This commit is contained in:
Vincent Hanewinkel
2025-05-30 14:32:49 +02:00
commit 2f1080922f
603 changed files with 1700 additions and 0 deletions

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task: detect
mode: train
model: yolov8n.pt
data: yolo_dataset\dataset.yaml
epochs: 50
time: null
patience: 5
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: cpu
workers: 8
project: yolo_training
name: NAO_detector3
exist_ok: false
pretrained: true
optimizer: auto
verbose: true
seed: 0
deterministic: true
single_cls: false
rect: false
cos_lr: false
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
vid_stride: 1
stream_buffer: false
visualize: false
augment: false
agnostic_nms: false
classes: null
retina_masks: false
embed: null
show: false
save_frames: false
save_txt: false
save_conf: false
save_crop: false
show_labels: true
show_conf: true
show_boxes: true
line_width: null
format: torchscript
keras: false
optimize: false
int8: false
dynamic: false
simplify: true
opset: null
workspace: null
nms: false
lr0: 0.01
lrf: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0
warmup_momentum: 0.8
warmup_bias_lr: 0.1
box: 7.5
cls: 0.5
dfl: 1.5
pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
hsv_s: 0.7
hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
shear: 0.0
perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
mosaic: 1.0
mixup: 0.0
cutmix: 0.0
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: yolo_training\NAO_detector3

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epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
1,140.47,1.11402,2.41714,1.20281,1,0.37601,0.79951,0.55456,0.87553,2.60439,1.01905,0.00026,0.00026,0.00026
2,316.983,0.99575,1.51901,1.10586,1,0.45683,0.90108,0.57564,1.10253,2.54986,1.13344,0.000529308,0.000529308,0.000529308
3,512.153,0.99078,1.43238,1.14146,0.93389,0.35323,0.7206,0.43569,1.22903,2.64606,1.23865,0.000787528,0.000787528,0.000787528
4,644.94,1.0202,1.33932,1.15516,0.61359,0.3,0.34609,0.22426,1.32221,2.85182,1.39251,0.00103466,0.00103466,0.00103466
5,772.698,1.04289,1.31,1.17156,0.475,0.45,0.4433,0.29879,1.17274,2.54752,1.27816,0.0012707,0.0012707,0.0012707
6,899.988,1.0882,1.2972,1.2043,0.7555,0.2875,0.31395,0.19236,1.42954,3.42743,1.67074,0.00149566,0.00149566,0.00149566
7,1025.24,1.05137,1.28863,1.18317,0.4076,0.45,0.36035,0.2224,1.21401,3.12466,1.48008,0.00170953,0.00170953,0.00170953
1 epoch time train/box_loss train/cls_loss train/dfl_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/dfl_loss lr/pg0 lr/pg1 lr/pg2
2 1 140.47 1.11402 2.41714 1.20281 1 0.37601 0.79951 0.55456 0.87553 2.60439 1.01905 0.00026 0.00026 0.00026
3 2 316.983 0.99575 1.51901 1.10586 1 0.45683 0.90108 0.57564 1.10253 2.54986 1.13344 0.000529308 0.000529308 0.000529308
4 3 512.153 0.99078 1.43238 1.14146 0.93389 0.35323 0.7206 0.43569 1.22903 2.64606 1.23865 0.000787528 0.000787528 0.000787528
5 4 644.94 1.0202 1.33932 1.15516 0.61359 0.3 0.34609 0.22426 1.32221 2.85182 1.39251 0.00103466 0.00103466 0.00103466
6 5 772.698 1.04289 1.31 1.17156 0.475 0.45 0.4433 0.29879 1.17274 2.54752 1.27816 0.0012707 0.0012707 0.0012707
7 6 899.988 1.0882 1.2972 1.2043 0.7555 0.2875 0.31395 0.19236 1.42954 3.42743 1.67074 0.00149566 0.00149566 0.00149566
8 7 1025.24 1.05137 1.28863 1.18317 0.4076 0.45 0.36035 0.2224 1.21401 3.12466 1.48008 0.00170953 0.00170953 0.00170953

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