Add model.onnx to yolo-onnx root

This commit is contained in:
leejaewoo 2026-02-02 04:52:44 +00:00
parent 3d570982ff
commit 03948467d4
44 changed files with 0 additions and 310 deletions

35
.gitattributes vendored

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*.7z filter=lfs diff=lfs merge=lfs -text
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best.pt (Stored with Git LFS)

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model.onnx Normal file

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task: detect
mode: train
model: /home/jovyan/model/leejaewoo/yolo11/yolo11m.pt
data: data.yaml
epochs: 20
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: null
workers: 8
project: /home/jovyan/model/leejaewoo/yolo11/results-yolo-trained
name: train
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
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
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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: /home/jovyan/model/leejaewoo/yolo11/results-yolo-trained/train

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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
1,2.79184,1.20329,2.63505,1.60228,0.00426,0.33333,0.33801,0.23587,1.68014,2.95409,1.18407,0,0,0
2,3.5579,1.71571,3.1574,2.06722,0.00432,0.33333,0.33804,0.23588,1.68943,2.93518,1.18411,1.901e-05,1.901e-05,1.901e-05
3,4.45514,1.28664,2.76427,1.80032,0.00394,0.33333,0.338,0.23586,1.68377,2.92535,1.18199,3.604e-05,3.604e-05,3.604e-05
4,5.21857,2.02959,2.78247,2.12608,0.00384,0.33333,0.33802,0.23588,1.68336,2.91587,1.18185,5.109e-05,5.109e-05,5.109e-05
5,6.09583,1.29589,2.4997,1.52708,0.00378,0.33333,0.33801,0.23587,1.62366,2.8887,1.17599,6.416e-05,6.416e-05,6.416e-05
6,6.85697,1.51277,3.06007,1.79636,0.00367,0.33333,0.3393,0.27007,1.62356,2.88391,1.17534,7.525e-05,7.525e-05,7.525e-05
7,7.76471,1.16295,2.88673,1.43502,0.00701,0.66667,0.34071,0.27069,1.61951,2.85114,1.17647,8.436e-05,8.436e-05,8.436e-05
8,8.65314,1.80226,3.3897,2.19002,0.005,1,0.34149,0.27104,1.61407,2.82281,1.17371,9.149e-05,9.149e-05,9.149e-05
9,9.67341,1.36534,2.83636,1.59469,1,0.92064,0.995,0.66472,1.44301,1.35205,1.08568,9.664e-05,9.664e-05,9.664e-05
10,11.4543,0.83717,1.591,1.14281,0.97649,1,0.995,0.73056,0.99007,1.32467,0.99136,9.981e-05,9.981e-05,9.981e-05
11,14.0589,1.13175,2.83905,1.40507,0.96256,1,0.995,0.50029,1.23615,1.68106,1.01363,0.000101,0.000101,0.000101
12,15.4923,0.61373,2.15461,1.21324,0.96159,1,0.995,0.69789,1.21433,1.15714,0.97342,0.00010021,0.00010021,0.00010021
13,16.9859,1.13271,2.03106,1.14791,0.9764,0.66667,0.83,0.47642,1.27881,1.4907,1.08621,9.744e-05,9.744e-05,9.744e-05
14,18.5319,1.0798,2.2862,1.60101,0.74727,0.98909,0.83,0.61522,1.12672,1.55495,1.0186,9.269e-05,9.269e-05,9.269e-05
15,20.0473,0.69968,1.19794,1.06678,0.66158,0.66667,0.56147,0.50237,1.08498,1.72119,1.02719,8.596e-05,8.596e-05,8.596e-05
16,21.4505,0.58534,1.12073,0.99467,0.66516,0.66366,0.44389,0.32753,1.57405,2.30618,1.12398,7.725e-05,7.725e-05,7.725e-05
17,22.8327,0.62205,0.87617,0.97014,0.66389,0.66112,0.55556,0.36689,1.89155,2.11945,1.19579,6.656e-05,6.656e-05,6.656e-05
18,24.1643,0.53723,0.9658,0.69231,0.66389,0.66112,0.55556,0.36689,1.89155,2.11945,1.19579,5.389e-05,5.389e-05,5.389e-05
19,25.5556,0.70819,0.95772,1.17249,0.66353,0.66039,0.56738,0.36925,1.6714,1.91917,1.13025,3.924e-05,3.924e-05,3.924e-05
20,26.9367,0.67334,0.98455,0.87524,0.66353,0.66039,0.56738,0.36925,1.6714,1.91917,1.13025,2.261e-05,2.261e-05,2.261e-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 2.79184 1.20329 2.63505 1.60228 0.00426 0.33333 0.33801 0.23587 1.68014 2.95409 1.18407 0 0 0
3 2 3.5579 1.71571 3.1574 2.06722 0.00432 0.33333 0.33804 0.23588 1.68943 2.93518 1.18411 1.901e-05 1.901e-05 1.901e-05
4 3 4.45514 1.28664 2.76427 1.80032 0.00394 0.33333 0.338 0.23586 1.68377 2.92535 1.18199 3.604e-05 3.604e-05 3.604e-05
5 4 5.21857 2.02959 2.78247 2.12608 0.00384 0.33333 0.33802 0.23588 1.68336 2.91587 1.18185 5.109e-05 5.109e-05 5.109e-05
6 5 6.09583 1.29589 2.4997 1.52708 0.00378 0.33333 0.33801 0.23587 1.62366 2.8887 1.17599 6.416e-05 6.416e-05 6.416e-05
7 6 6.85697 1.51277 3.06007 1.79636 0.00367 0.33333 0.3393 0.27007 1.62356 2.88391 1.17534 7.525e-05 7.525e-05 7.525e-05
8 7 7.76471 1.16295 2.88673 1.43502 0.00701 0.66667 0.34071 0.27069 1.61951 2.85114 1.17647 8.436e-05 8.436e-05 8.436e-05
9 8 8.65314 1.80226 3.3897 2.19002 0.005 1 0.34149 0.27104 1.61407 2.82281 1.17371 9.149e-05 9.149e-05 9.149e-05
10 9 9.67341 1.36534 2.83636 1.59469 1 0.92064 0.995 0.66472 1.44301 1.35205 1.08568 9.664e-05 9.664e-05 9.664e-05
11 10 11.4543 0.83717 1.591 1.14281 0.97649 1 0.995 0.73056 0.99007 1.32467 0.99136 9.981e-05 9.981e-05 9.981e-05
12 11 14.0589 1.13175 2.83905 1.40507 0.96256 1 0.995 0.50029 1.23615 1.68106 1.01363 0.000101 0.000101 0.000101
13 12 15.4923 0.61373 2.15461 1.21324 0.96159 1 0.995 0.69789 1.21433 1.15714 0.97342 0.00010021 0.00010021 0.00010021
14 13 16.9859 1.13271 2.03106 1.14791 0.9764 0.66667 0.83 0.47642 1.27881 1.4907 1.08621 9.744e-05 9.744e-05 9.744e-05
15 14 18.5319 1.0798 2.2862 1.60101 0.74727 0.98909 0.83 0.61522 1.12672 1.55495 1.0186 9.269e-05 9.269e-05 9.269e-05
16 15 20.0473 0.69968 1.19794 1.06678 0.66158 0.66667 0.56147 0.50237 1.08498 1.72119 1.02719 8.596e-05 8.596e-05 8.596e-05
17 16 21.4505 0.58534 1.12073 0.99467 0.66516 0.66366 0.44389 0.32753 1.57405 2.30618 1.12398 7.725e-05 7.725e-05 7.725e-05
18 17 22.8327 0.62205 0.87617 0.97014 0.66389 0.66112 0.55556 0.36689 1.89155 2.11945 1.19579 6.656e-05 6.656e-05 6.656e-05
19 18 24.1643 0.53723 0.9658 0.69231 0.66389 0.66112 0.55556 0.36689 1.89155 2.11945 1.19579 5.389e-05 5.389e-05 5.389e-05
20 19 25.5556 0.70819 0.95772 1.17249 0.66353 0.66039 0.56738 0.36925 1.6714 1.91917 1.13025 3.924e-05 3.924e-05 3.924e-05
21 20 26.9367 0.67334 0.98455 0.87524 0.66353 0.66039 0.56738 0.36925 1.6714 1.91917 1.13025 2.261e-05 2.261e-05 2.261e-05

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results-yolo-trained/train/weights/best.pt (Stored with Git LFS)

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task: detect
mode: train
model: /home/jovyan/model/leejaewoo/yolo11/yolo11m.pt
data: data.yaml
epochs: 20
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: null
workers: 8
project: /home/jovyan/model/leejaewoo/yolo11
name: train_temp
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
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: /home/jovyan/model/leejaewoo/yolo11/train_temp

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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
1,0.367439,1.20329,2.63505,1.60228,0.00426,0.33333,0.33801,0.23587,1.68014,2.95409,1.18407,0,0,0
2,1.21012,1.71571,3.1574,2.06722,0.00432,0.33333,0.33804,0.23588,1.68943,2.93518,1.18411,1.901e-05,1.901e-05,1.901e-05
3,2.16939,1.28664,2.76427,1.80032,0.00394,0.33333,0.338,0.23586,1.68377,2.92535,1.18199,3.604e-05,3.604e-05,3.604e-05
4,2.99032,2.02959,2.78247,2.12608,0.00384,0.33333,0.33802,0.23588,1.68336,2.91587,1.18185,5.109e-05,5.109e-05,5.109e-05
5,3.88049,1.29589,2.4997,1.52708,0.00378,0.33333,0.33801,0.23587,1.62366,2.8887,1.17599,6.416e-05,6.416e-05,6.416e-05
6,4.6614,1.51277,3.06007,1.79636,0.00367,0.33333,0.3393,0.27007,1.62356,2.88391,1.17534,7.525e-05,7.525e-05,7.525e-05
7,5.558,1.16295,2.88673,1.43502,0.00701,0.66667,0.34071,0.27069,1.61951,2.85114,1.17647,8.436e-05,8.436e-05,8.436e-05
8,6.48844,1.80226,3.3897,2.19002,0.005,1,0.34149,0.27104,1.61407,2.82281,1.17371,9.149e-05,9.149e-05,9.149e-05
9,7.452,1.36534,2.83636,1.59469,1,0.92064,0.995,0.66472,1.44301,1.35205,1.08568,9.664e-05,9.664e-05,9.664e-05
10,9.2483,0.83717,1.591,1.14281,0.97649,1,0.995,0.73056,0.99007,1.32467,0.99136,9.981e-05,9.981e-05,9.981e-05
11,11.9825,1.13175,2.83905,1.40507,0.96256,1,0.995,0.50029,1.23615,1.68106,1.01363,0.000101,0.000101,0.000101
12,13.5047,0.61373,2.15461,1.21324,0.96159,1,0.995,0.69789,1.21433,1.15714,0.97342,0.00010021,0.00010021,0.00010021
13,15.8,1.13271,2.03106,1.14791,0.9764,0.66667,0.83,0.47642,1.27881,1.4907,1.08621,9.744e-05,9.744e-05,9.744e-05
14,17.1848,1.0798,2.2862,1.60101,0.74727,0.98909,0.83,0.61522,1.12672,1.55495,1.0186,9.269e-05,9.269e-05,9.269e-05
15,18.5961,0.69968,1.19794,1.06678,0.66158,0.66667,0.56147,0.50237,1.08498,1.72119,1.02719,8.596e-05,8.596e-05,8.596e-05
16,19.9885,0.58534,1.12073,0.99467,0.66516,0.66366,0.44389,0.32753,1.57405,2.30618,1.12398,7.725e-05,7.725e-05,7.725e-05
17,21.3948,0.62205,0.87617,0.97014,0.66389,0.66112,0.55556,0.36689,1.89155,2.11945,1.19579,6.656e-05,6.656e-05,6.656e-05
18,22.7497,0.53723,0.9658,0.69231,0.66389,0.66112,0.55556,0.36689,1.89155,2.11945,1.19579,5.389e-05,5.389e-05,5.389e-05
19,24.15,0.70819,0.95772,1.17249,0.66353,0.66039,0.56738,0.36925,1.6714,1.91917,1.13025,3.924e-05,3.924e-05,3.924e-05
20,25.4947,0.67334,0.98455,0.87524,0.66353,0.66039,0.56738,0.36925,1.6714,1.91917,1.13025,2.261e-05,2.261e-05,2.261e-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 0.367439 1.20329 2.63505 1.60228 0.00426 0.33333 0.33801 0.23587 1.68014 2.95409 1.18407 0 0 0
3 2 1.21012 1.71571 3.1574 2.06722 0.00432 0.33333 0.33804 0.23588 1.68943 2.93518 1.18411 1.901e-05 1.901e-05 1.901e-05
4 3 2.16939 1.28664 2.76427 1.80032 0.00394 0.33333 0.338 0.23586 1.68377 2.92535 1.18199 3.604e-05 3.604e-05 3.604e-05
5 4 2.99032 2.02959 2.78247 2.12608 0.00384 0.33333 0.33802 0.23588 1.68336 2.91587 1.18185 5.109e-05 5.109e-05 5.109e-05
6 5 3.88049 1.29589 2.4997 1.52708 0.00378 0.33333 0.33801 0.23587 1.62366 2.8887 1.17599 6.416e-05 6.416e-05 6.416e-05
7 6 4.6614 1.51277 3.06007 1.79636 0.00367 0.33333 0.3393 0.27007 1.62356 2.88391 1.17534 7.525e-05 7.525e-05 7.525e-05
8 7 5.558 1.16295 2.88673 1.43502 0.00701 0.66667 0.34071 0.27069 1.61951 2.85114 1.17647 8.436e-05 8.436e-05 8.436e-05
9 8 6.48844 1.80226 3.3897 2.19002 0.005 1 0.34149 0.27104 1.61407 2.82281 1.17371 9.149e-05 9.149e-05 9.149e-05
10 9 7.452 1.36534 2.83636 1.59469 1 0.92064 0.995 0.66472 1.44301 1.35205 1.08568 9.664e-05 9.664e-05 9.664e-05
11 10 9.2483 0.83717 1.591 1.14281 0.97649 1 0.995 0.73056 0.99007 1.32467 0.99136 9.981e-05 9.981e-05 9.981e-05
12 11 11.9825 1.13175 2.83905 1.40507 0.96256 1 0.995 0.50029 1.23615 1.68106 1.01363 0.000101 0.000101 0.000101
13 12 13.5047 0.61373 2.15461 1.21324 0.96159 1 0.995 0.69789 1.21433 1.15714 0.97342 0.00010021 0.00010021 0.00010021
14 13 15.8 1.13271 2.03106 1.14791 0.9764 0.66667 0.83 0.47642 1.27881 1.4907 1.08621 9.744e-05 9.744e-05 9.744e-05
15 14 17.1848 1.0798 2.2862 1.60101 0.74727 0.98909 0.83 0.61522 1.12672 1.55495 1.0186 9.269e-05 9.269e-05 9.269e-05
16 15 18.5961 0.69968 1.19794 1.06678 0.66158 0.66667 0.56147 0.50237 1.08498 1.72119 1.02719 8.596e-05 8.596e-05 8.596e-05
17 16 19.9885 0.58534 1.12073 0.99467 0.66516 0.66366 0.44389 0.32753 1.57405 2.30618 1.12398 7.725e-05 7.725e-05 7.725e-05
18 17 21.3948 0.62205 0.87617 0.97014 0.66389 0.66112 0.55556 0.36689 1.89155 2.11945 1.19579 6.656e-05 6.656e-05 6.656e-05
19 18 22.7497 0.53723 0.9658 0.69231 0.66389 0.66112 0.55556 0.36689 1.89155 2.11945 1.19579 5.389e-05 5.389e-05 5.389e-05
20 19 24.15 0.70819 0.95772 1.17249 0.66353 0.66039 0.56738 0.36925 1.6714 1.91917 1.13025 3.924e-05 3.924e-05 3.924e-05
21 20 25.4947 0.67334 0.98455 0.87524 0.66353 0.66039 0.56738 0.36925 1.6714 1.91917 1.13025 2.261e-05 2.261e-05 2.261e-05

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