Milestone YOLO11 navigation planning baseline

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administrator
2026-05-15 16:52:54 +02:00
parent 6a18517743
commit a92dcf2659
212 changed files with 1691 additions and 63 deletions

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task: detect
mode: train
model: C:\devel\flywms\yolo11n.pt
data: C:\devel\flywms\dataset_yolo\data.yaml
epochs: 20
time: null
patience: 100
batch: 4
imgsz: 640
save: true
save_period: -1
cache: false
device: cpu
workers: 0
project: C:\devel\flywms\runs
name: flywms_yolo11n_quick20
exist_ok: true
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: 0.0
compile: 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
end2end: null
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
cls_pw: 0.0
dfl: 1.5
pose: 12.0
kobj: 1.0
rle: 1.0
angle: 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: C:\devel\flywms\runs\flywms_yolo11n_quick20

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@@ -0,0 +1,21 @@
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
1,51.2151,0.88849,3.04479,1.08039,0.01871,0.87698,0.33412,0.29073,0.6261,3.08633,0.81923,0.00025005,0.00025005,0.00025005
2,101.332,0.69312,2.40144,0.94566,0.01879,0.90023,0.37032,0.33453,0.65828,2.97117,0.82994,0.00049119,0.00049119,0.00049119
3,151.427,0.71463,1.81817,0.92521,0.01813,0.91186,0.7598,0.62923,0.73465,2.60716,0.8333,0.000705924,0.000705924,0.000705924
4,201.872,0.79288,1.59143,0.9471,0.96051,0.71276,0.80155,0.65319,0.71983,2.08344,0.82502,0.000894254,0.000894254,0.000894254
5,251.321,0.77401,1.42126,0.92999,0.94858,0.7625,0.84101,0.67063,0.74182,1.86154,0.84867,0.00105618,0.00105618,0.00105618
6,301.702,0.74178,1.34433,0.92154,0.97332,0.74092,0.85263,0.7078,0.76636,1.70358,0.87237,0.0011917,0.0011917,0.0011917
7,352.638,0.74744,1.2363,0.9384,0.971,0.76381,0.86072,0.70977,0.72111,1.38135,0.84374,0.0011719,0.0011719,0.0011719
8,403.04,0.68641,1.09602,0.93281,0.94469,0.79075,0.88458,0.71879,0.72967,1.25264,0.8701,0.00108938,0.00108938,0.00108938
9,452.017,0.72809,1.10366,0.94587,0.93226,0.8386,0.90331,0.74031,0.7194,1.12748,0.87413,0.00100687,0.00100687,0.00100687
10,502.551,0.7019,1.00921,0.92408,0.89639,0.80002,0.90622,0.72821,0.72233,1.08835,0.86153,0.000924352,0.000924352,0.000924352
11,553.611,0.65562,1.10138,0.91495,0.85793,0.82164,0.89744,0.70137,0.77106,1.11163,0.87059,0.000841835,0.000841835,0.000841835
12,602.999,0.64027,0.9959,0.91561,0.95089,0.83254,0.91032,0.71336,0.72627,1.05903,0.85517,0.000759318,0.000759318,0.000759318
13,653.387,0.61253,0.96051,0.88927,0.94831,0.84919,0.91059,0.73189,0.7031,0.97285,0.8519,0.000676802,0.000676802,0.000676802
14,700.996,0.61113,0.94359,0.89706,0.95557,0.82642,0.90477,0.73034,0.67461,0.97665,0.84954,0.000594286,0.000594286,0.000594286
15,746.698,0.61377,0.94243,0.89531,0.95608,0.81608,0.90121,0.75121,0.63197,0.91285,0.83643,0.000511769,0.000511769,0.000511769
16,793.381,0.59259,0.89758,0.8953,0.98474,0.7951,0.90025,0.75738,0.62957,0.86628,0.83671,0.000429253,0.000429253,0.000429253
17,839.343,0.56818,0.85172,0.86904,0.96828,0.8092,0.90115,0.75737,0.63954,0.84695,0.84413,0.000346736,0.000346736,0.000346736
18,887.796,0.58209,0.89463,0.89231,0.97416,0.81431,0.90297,0.75298,0.63761,0.83079,0.84169,0.00026422,0.00026422,0.00026422
19,933.73,0.58816,0.87268,0.87924,0.97418,0.81498,0.90505,0.76205,0.6247,0.81969,0.83871,0.000181703,0.000181703,0.000181703
20,978.856,0.54821,0.86168,0.87146,0.93832,0.82536,0.90508,0.76455,0.61497,0.81882,0.83666,9.91865e-05,9.91865e-05,9.91865e-05
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 51.2151 0.88849 3.04479 1.08039 0.01871 0.87698 0.33412 0.29073 0.6261 3.08633 0.81923 0.00025005 0.00025005 0.00025005
3 2 101.332 0.69312 2.40144 0.94566 0.01879 0.90023 0.37032 0.33453 0.65828 2.97117 0.82994 0.00049119 0.00049119 0.00049119
4 3 151.427 0.71463 1.81817 0.92521 0.01813 0.91186 0.7598 0.62923 0.73465 2.60716 0.8333 0.000705924 0.000705924 0.000705924
5 4 201.872 0.79288 1.59143 0.9471 0.96051 0.71276 0.80155 0.65319 0.71983 2.08344 0.82502 0.000894254 0.000894254 0.000894254
6 5 251.321 0.77401 1.42126 0.92999 0.94858 0.7625 0.84101 0.67063 0.74182 1.86154 0.84867 0.00105618 0.00105618 0.00105618
7 6 301.702 0.74178 1.34433 0.92154 0.97332 0.74092 0.85263 0.7078 0.76636 1.70358 0.87237 0.0011917 0.0011917 0.0011917
8 7 352.638 0.74744 1.2363 0.9384 0.971 0.76381 0.86072 0.70977 0.72111 1.38135 0.84374 0.0011719 0.0011719 0.0011719
9 8 403.04 0.68641 1.09602 0.93281 0.94469 0.79075 0.88458 0.71879 0.72967 1.25264 0.8701 0.00108938 0.00108938 0.00108938
10 9 452.017 0.72809 1.10366 0.94587 0.93226 0.8386 0.90331 0.74031 0.7194 1.12748 0.87413 0.00100687 0.00100687 0.00100687
11 10 502.551 0.7019 1.00921 0.92408 0.89639 0.80002 0.90622 0.72821 0.72233 1.08835 0.86153 0.000924352 0.000924352 0.000924352
12 11 553.611 0.65562 1.10138 0.91495 0.85793 0.82164 0.89744 0.70137 0.77106 1.11163 0.87059 0.000841835 0.000841835 0.000841835
13 12 602.999 0.64027 0.9959 0.91561 0.95089 0.83254 0.91032 0.71336 0.72627 1.05903 0.85517 0.000759318 0.000759318 0.000759318
14 13 653.387 0.61253 0.96051 0.88927 0.94831 0.84919 0.91059 0.73189 0.7031 0.97285 0.8519 0.000676802 0.000676802 0.000676802
15 14 700.996 0.61113 0.94359 0.89706 0.95557 0.82642 0.90477 0.73034 0.67461 0.97665 0.84954 0.000594286 0.000594286 0.000594286
16 15 746.698 0.61377 0.94243 0.89531 0.95608 0.81608 0.90121 0.75121 0.63197 0.91285 0.83643 0.000511769 0.000511769 0.000511769
17 16 793.381 0.59259 0.89758 0.8953 0.98474 0.7951 0.90025 0.75738 0.62957 0.86628 0.83671 0.000429253 0.000429253 0.000429253
18 17 839.343 0.56818 0.85172 0.86904 0.96828 0.8092 0.90115 0.75737 0.63954 0.84695 0.84413 0.000346736 0.000346736 0.000346736
19 18 887.796 0.58209 0.89463 0.89231 0.97416 0.81431 0.90297 0.75298 0.63761 0.83079 0.84169 0.00026422 0.00026422 0.00026422
20 19 933.73 0.58816 0.87268 0.87924 0.97418 0.81498 0.90505 0.76205 0.6247 0.81969 0.83871 0.000181703 0.000181703 0.000181703
21 20 978.856 0.54821 0.86168 0.87146 0.93832 0.82536 0.90508 0.76455 0.61497 0.81882 0.83666 9.91865e-05 9.91865e-05 9.91865e-05

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