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hasil train yolo

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.gitignore vendored Normal file
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runs/*
Test*
dataset*
botol*
bottle*
kaleng*
can*
others*
plastik*
train*/
val*
desktop.ini
!runs/classify/
!runs/classify/train/
!runs/classify/train*/

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runs/classify/train/args.yaml Normal file
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task: classify
mode: train
model: yolo11n-cls.pt
data: dataset1
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 224
save: true
save_period: -1
cache: false
device: null
workers: 8
project: null
name: train
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: 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: G:\My Drive\Smart-Bin\NewDataset\runs\classify\train

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task: classify
mode: train
model: yolo11n-cls.pt
data: dataset1
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 224
save: true
save_period: -1
cache: false
device: null
workers: 8
project: null
name: train2
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: 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: G:\My Drive\Smart-Bin\NewDataset\runs\classify\train2

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epoch,time,train/loss,metrics/accuracy_top1,metrics/accuracy_top5,val/loss,lr/pg0,lr/pg1,lr/pg2
1,17.196,0.7232,0.93333,1,0.21564,0.000468394,0.000468394,0.000468394
2,25.6802,0.30348,0.94667,1,0.13853,0.000926022,0.000926022,0.000926022
3,32.4317,0.26099,0.88,1,0.30312,0.00136479,0.00136479,0.00136479
4,37.5739,0.26635,0.85333,1,1.27915,0.00134412,0.00134412,0.00134412
5,42.8118,0.28538,0.89333,1,0.2562,0.00131582,0.00131582,0.00131582
6,48.2115,0.253,0.78,1,1.39121,0.00128753,0.00128753,0.00128753
7,53.8989,0.26497,0.91333,1,0.34656,0.00125923,0.00125923,0.00125923
8,58.7079,0.19846,0.88,1,0.36226,0.00123094,0.00123094,0.00123094
9,63.4706,0.22324,0.88,1,0.32712,0.00120265,0.00120265,0.00120265
10,68.4538,0.20243,0.95333,1,0.16115,0.00117435,0.00117435,0.00117435
11,72.7846,0.16266,0.9,1,0.20503,0.00114606,0.00114606,0.00114606
12,77.4347,0.183,0.96,1,0.09489,0.00111776,0.00111776,0.00111776
13,82.2759,0.11574,0.94,1,0.15847,0.00108947,0.00108947,0.00108947
14,86.9616,0.13162,0.9,1,0.31243,0.00106118,0.00106118,0.00106118
15,91.4902,0.14912,0.96,1,0.11051,0.00103288,0.00103288,0.00103288
16,95.8611,0.12044,0.96667,1,0.07873,0.00100459,0.00100459,0.00100459
17,100.182,0.11352,0.92667,1,0.23683,0.000976293,0.000976293,0.000976293
18,104.719,0.11058,0.96,1,0.10991,0.000947999,0.000947999,0.000947999
19,109.18,0.07388,0.96667,1,0.08041,0.000919704,0.000919704,0.000919704
20,113.724,0.0676,0.95333,1,0.15167,0.00089141,0.00089141,0.00089141
21,118.328,0.10912,0.95333,1,0.09731,0.000863116,0.000863116,0.000863116
22,123.003,0.09131,0.93333,1,0.11433,0.000834822,0.000834822,0.000834822
23,127.448,0.07576,0.94667,1,0.13943,0.000806528,0.000806528,0.000806528
24,131.712,0.10455,0.91333,1,0.21007,0.000778233,0.000778233,0.000778233
25,136.113,0.08877,0.86667,1,0.50044,0.000749939,0.000749939,0.000749939
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27,145.467,0.05855,0.91333,1,0.25338,0.000693351,0.000693351,0.000693351
28,149.992,0.06557,0.94,1,0.14813,0.000665057,0.000665057,0.000665057
29,154.282,0.05799,0.94,1,0.15513,0.000636762,0.000636762,0.000636762
30,158.984,0.05786,0.98,1,0.06441,0.000608468,0.000608468,0.000608468
31,163.284,0.04731,0.98,1,0.04349,0.000580174,0.000580174,0.000580174
32,167.855,0.04791,0.90667,1,0.24226,0.00055188,0.00055188,0.00055188
33,172.75,0.05605,0.96667,1,0.0923,0.000523586,0.000523586,0.000523586
34,177.572,0.04981,0.94667,1,0.10938,0.000495291,0.000495291,0.000495291
35,181.972,0.04335,0.87333,1,0.25908,0.000466997,0.000466997,0.000466997
36,186.349,0.04659,0.90667,1,0.28723,0.000438703,0.000438703,0.000438703
37,190.814,0.04157,0.92667,1,0.14376,0.000410409,0.000410409,0.000410409
38,195.483,0.04647,0.93333,1,0.15111,0.000382115,0.000382115,0.000382115
39,199.81,0.0404,0.93333,1,0.14298,0.00035382,0.00035382,0.00035382
40,205.35,0.03053,0.96,1,0.08349,0.000325526,0.000325526,0.000325526
41,255.616,0.0345,0.93333,1,0.11085,0.000297232,0.000297232,0.000297232
42,261.384,0.02784,0.92667,1,0.18352,0.000268938,0.000268938,0.000268938
43,266.176,0.03198,0.98,1,0.05693,0.000240644,0.000240644,0.000240644
44,271.048,0.0281,0.95333,1,0.08433,0.000212349,0.000212349,0.000212349
45,276.01,0.02883,0.94667,1,0.10117,0.000184055,0.000184055,0.000184055
46,280.37,0.03334,0.98,1,0.0608,0.000155761,0.000155761,0.000155761
47,284.756,0.03015,0.96,1,0.07358,0.000127467,0.000127467,0.000127467
48,289.108,0.03091,0.94667,1,0.09654,9.91726e-05,9.91726e-05,9.91726e-05
49,293.298,0.02845,0.94667,1,0.09857,7.08784e-05,7.08784e-05,7.08784e-05
50,297.472,0.02004,0.96,1,0.09198,4.25842e-05,4.25842e-05,4.25842e-05
1 epoch time train/loss metrics/accuracy_top1 metrics/accuracy_top5 val/loss lr/pg0 lr/pg1 lr/pg2
2 1 17.196 0.7232 0.93333 1 0.21564 0.000468394 0.000468394 0.000468394
3 2 25.6802 0.30348 0.94667 1 0.13853 0.000926022 0.000926022 0.000926022
4 3 32.4317 0.26099 0.88 1 0.30312 0.00136479 0.00136479 0.00136479
5 4 37.5739 0.26635 0.85333 1 1.27915 0.00134412 0.00134412 0.00134412
6 5 42.8118 0.28538 0.89333 1 0.2562 0.00131582 0.00131582 0.00131582
7 6 48.2115 0.253 0.78 1 1.39121 0.00128753 0.00128753 0.00128753
8 7 53.8989 0.26497 0.91333 1 0.34656 0.00125923 0.00125923 0.00125923
9 8 58.7079 0.19846 0.88 1 0.36226 0.00123094 0.00123094 0.00123094
10 9 63.4706 0.22324 0.88 1 0.32712 0.00120265 0.00120265 0.00120265
11 10 68.4538 0.20243 0.95333 1 0.16115 0.00117435 0.00117435 0.00117435
12 11 72.7846 0.16266 0.9 1 0.20503 0.00114606 0.00114606 0.00114606
13 12 77.4347 0.183 0.96 1 0.09489 0.00111776 0.00111776 0.00111776
14 13 82.2759 0.11574 0.94 1 0.15847 0.00108947 0.00108947 0.00108947
15 14 86.9616 0.13162 0.9 1 0.31243 0.00106118 0.00106118 0.00106118
16 15 91.4902 0.14912 0.96 1 0.11051 0.00103288 0.00103288 0.00103288
17 16 95.8611 0.12044 0.96667 1 0.07873 0.00100459 0.00100459 0.00100459
18 17 100.182 0.11352 0.92667 1 0.23683 0.000976293 0.000976293 0.000976293
19 18 104.719 0.11058 0.96 1 0.10991 0.000947999 0.000947999 0.000947999
20 19 109.18 0.07388 0.96667 1 0.08041 0.000919704 0.000919704 0.000919704
21 20 113.724 0.0676 0.95333 1 0.15167 0.00089141 0.00089141 0.00089141
22 21 118.328 0.10912 0.95333 1 0.09731 0.000863116 0.000863116 0.000863116
23 22 123.003 0.09131 0.93333 1 0.11433 0.000834822 0.000834822 0.000834822
24 23 127.448 0.07576 0.94667 1 0.13943 0.000806528 0.000806528 0.000806528
25 24 131.712 0.10455 0.91333 1 0.21007 0.000778233 0.000778233 0.000778233
26 25 136.113 0.08877 0.86667 1 0.50044 0.000749939 0.000749939 0.000749939
27 26 140.704 0.08691 0.9 1 0.34083 0.000721645 0.000721645 0.000721645
28 27 145.467 0.05855 0.91333 1 0.25338 0.000693351 0.000693351 0.000693351
29 28 149.992 0.06557 0.94 1 0.14813 0.000665057 0.000665057 0.000665057
30 29 154.282 0.05799 0.94 1 0.15513 0.000636762 0.000636762 0.000636762
31 30 158.984 0.05786 0.98 1 0.06441 0.000608468 0.000608468 0.000608468
32 31 163.284 0.04731 0.98 1 0.04349 0.000580174 0.000580174 0.000580174
33 32 167.855 0.04791 0.90667 1 0.24226 0.00055188 0.00055188 0.00055188
34 33 172.75 0.05605 0.96667 1 0.0923 0.000523586 0.000523586 0.000523586
35 34 177.572 0.04981 0.94667 1 0.10938 0.000495291 0.000495291 0.000495291
36 35 181.972 0.04335 0.87333 1 0.25908 0.000466997 0.000466997 0.000466997
37 36 186.349 0.04659 0.90667 1 0.28723 0.000438703 0.000438703 0.000438703
38 37 190.814 0.04157 0.92667 1 0.14376 0.000410409 0.000410409 0.000410409
39 38 195.483 0.04647 0.93333 1 0.15111 0.000382115 0.000382115 0.000382115
40 39 199.81 0.0404 0.93333 1 0.14298 0.00035382 0.00035382 0.00035382
41 40 205.35 0.03053 0.96 1 0.08349 0.000325526 0.000325526 0.000325526
42 41 255.616 0.0345 0.93333 1 0.11085 0.000297232 0.000297232 0.000297232
43 42 261.384 0.02784 0.92667 1 0.18352 0.000268938 0.000268938 0.000268938
44 43 266.176 0.03198 0.98 1 0.05693 0.000240644 0.000240644 0.000240644
45 44 271.048 0.0281 0.95333 1 0.08433 0.000212349 0.000212349 0.000212349
46 45 276.01 0.02883 0.94667 1 0.10117 0.000184055 0.000184055 0.000184055
47 46 280.37 0.03334 0.98 1 0.0608 0.000155761 0.000155761 0.000155761
48 47 284.756 0.03015 0.96 1 0.07358 0.000127467 0.000127467 0.000127467
49 48 289.108 0.03091 0.94667 1 0.09654 9.91726e-05 9.91726e-05 9.91726e-05
50 49 293.298 0.02845 0.94667 1 0.09857 7.08784e-05 7.08784e-05 7.08784e-05
51 50 297.472 0.02004 0.96 1 0.09198 4.25842e-05 4.25842e-05 4.25842e-05

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runs/classify/train3/args.yaml Normal file
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task: classify
mode: train
model: yolo26x-cls.pt
data: dataset1
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 224
save: true
save_period: -1
cache: false
device: null
workers: 8
project: null
name: train3
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: 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: G:\My Drive\Smart-Bin\NewDataset\runs\classify\train3

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epoch,time,train/loss,metrics/accuracy_top1,metrics/accuracy_top5,val/loss,lr/pg0,lr/pg1,lr/pg2
1,29.7214,0.5607,0.89333,1,0.31655,0.000468394,0.000468394,0.000468394
1 epoch time train/loss metrics/accuracy_top1 metrics/accuracy_top5 val/loss lr/pg0 lr/pg1 lr/pg2
2 1 29.7214 0.5607 0.89333 1 0.31655 0.000468394 0.000468394 0.000468394

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runs/classify/train4/args.yaml Normal file
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task: classify
mode: train
model: yolo26n-cls.pt
data: dataset1
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 224
save: true
save_period: -1
cache: false
device: null
workers: 8
project: null
name: train4
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: 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
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task: classify
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epoch,time,train/loss,metrics/accuracy_top1,metrics/accuracy_top5,val/loss,lr/pg0,lr/pg1,lr/pg2
1,19.9711,0.70861,0.92,1,0.31278,0.000468394,0.000468394,0.000468394
2,25.8938,0.28407,0.94667,1,0.1464,0.000926022,0.000926022,0.000926022
3,30.3184,0.20463,0.90667,1,0.26621,0.00136479,0.00136479,0.00136479
4,34.6579,0.18717,0.88,1,0.45156,0.00134412,0.00134412,0.00134412
5,39.071,0.29961,0.88,1,0.45679,0.00131582,0.00131582,0.00131582
6,43.3738,0.30882,0.88667,1,0.35603,0.00128753,0.00128753,0.00128753
7,47.7235,0.21338,0.90667,1,0.38582,0.00125923,0.00125923,0.00125923
8,51.8193,0.17124,0.96,1,0.10041,0.00123094,0.00123094,0.00123094
9,56.1963,0.20463,0.94667,1,0.16027,0.00120265,0.00120265,0.00120265
10,60.5281,0.17107,0.88667,1,0.40242,0.00117435,0.00117435,0.00117435
11,64.8049,0.17208,0.86667,1,0.48505,0.00114606,0.00114606,0.00114606
12,69.016,0.14373,0.96,1,0.12289,0.00111776,0.00111776,0.00111776
13,73.4242,0.15702,0.96667,1,0.07314,0.00108947,0.00108947,0.00108947
14,77.6501,0.13089,0.89333,1,0.23143,0.00106118,0.00106118,0.00106118
15,81.9175,0.11822,0.96,1,0.10379,0.00103288,0.00103288,0.00103288
16,86.127,0.10769,0.96667,1,0.08193,0.00100459,0.00100459,0.00100459
17,90.4547,0.11051,0.93333,1,0.22058,0.000976293,0.000976293,0.000976293
18,95.0927,0.11295,0.94667,1,0.14745,0.000947999,0.000947999,0.000947999
19,99.3322,0.06974,0.95333,1,0.1651,0.000919704,0.000919704,0.000919704
20,103.646,0.06798,0.93333,1,0.20085,0.00089141,0.00089141,0.00089141
21,107.971,0.0855,0.95333,1,0.10671,0.000863116,0.000863116,0.000863116
22,112.457,0.06728,0.93333,1,0.14702,0.000834822,0.000834822,0.000834822
23,116.756,0.05302,0.96,1,0.08877,0.000806528,0.000806528,0.000806528
24,120.999,0.07297,0.94667,1,0.14159,0.000778233,0.000778233,0.000778233
25,125.401,0.07436,0.96,1,0.10724,0.000749939,0.000749939,0.000749939
26,129.629,0.08033,0.94,1,0.10645,0.000721645,0.000721645,0.000721645
27,133.971,0.06174,0.91333,1,0.23418,0.000693351,0.000693351,0.000693351
28,138.3,0.04208,0.94,1,0.15263,0.000665057,0.000665057,0.000665057
29,142.629,0.052,0.92667,1,0.17317,0.000636762,0.000636762,0.000636762
30,146.961,0.0384,0.92667,1,0.19084,0.000608468,0.000608468,0.000608468
31,151.254,0.04774,0.94667,1,0.13362,0.000580174,0.000580174,0.000580174
32,155.57,0.05415,0.95333,1,0.1848,0.00055188,0.00055188,0.00055188
33,159.855,0.05656,0.93333,1,0.14427,0.000523586,0.000523586,0.000523586
34,164.223,0.04054,0.92667,1,0.22511,0.000495291,0.000495291,0.000495291
35,168.523,0.04049,0.9,1,0.26833,0.000466997,0.000466997,0.000466997
36,172.765,0.04174,0.89333,1,0.45552,0.000438703,0.000438703,0.000438703
37,177.091,0.03381,0.94667,1,0.24308,0.000410409,0.000410409,0.000410409
38,181.451,0.04199,0.95333,1,0.08527,0.000382115,0.000382115,0.000382115
39,185.731,0.04864,0.95333,1,0.12592,0.00035382,0.00035382,0.00035382
40,190.064,0.02309,0.96667,1,0.10974,0.000325526,0.000325526,0.000325526
41,237.273,0.03537,0.96,1,0.08711,0.000297232,0.000297232,0.000297232
42,242.139,0.02308,0.95333,1,0.13214,0.000268938,0.000268938,0.000268938
43,246.852,0.01859,0.96667,1,0.10143,0.000240644,0.000240644,0.000240644
44,251.506,0.02491,0.96667,1,0.08824,0.000212349,0.000212349,0.000212349
45,255.817,0.03082,0.96667,1,0.0838,0.000184055,0.000184055,0.000184055
46,260.163,0.03407,0.96667,1,0.08905,0.000155761,0.000155761,0.000155761
47,264.522,0.03065,0.95333,1,0.09718,0.000127467,0.000127467,0.000127467
48,268.939,0.02238,0.95333,1,0.10553,9.91726e-05,9.91726e-05,9.91726e-05
49,273.344,0.02425,0.96,1,0.12025,7.08784e-05,7.08784e-05,7.08784e-05
50,277.599,0.02704,0.95333,1,0.10661,4.25842e-05,4.25842e-05,4.25842e-05
1 epoch time train/loss metrics/accuracy_top1 metrics/accuracy_top5 val/loss lr/pg0 lr/pg1 lr/pg2
2 1 19.9711 0.70861 0.92 1 0.31278 0.000468394 0.000468394 0.000468394
3 2 25.8938 0.28407 0.94667 1 0.1464 0.000926022 0.000926022 0.000926022
4 3 30.3184 0.20463 0.90667 1 0.26621 0.00136479 0.00136479 0.00136479
5 4 34.6579 0.18717 0.88 1 0.45156 0.00134412 0.00134412 0.00134412
6 5 39.071 0.29961 0.88 1 0.45679 0.00131582 0.00131582 0.00131582
7 6 43.3738 0.30882 0.88667 1 0.35603 0.00128753 0.00128753 0.00128753
8 7 47.7235 0.21338 0.90667 1 0.38582 0.00125923 0.00125923 0.00125923
9 8 51.8193 0.17124 0.96 1 0.10041 0.00123094 0.00123094 0.00123094
10 9 56.1963 0.20463 0.94667 1 0.16027 0.00120265 0.00120265 0.00120265
11 10 60.5281 0.17107 0.88667 1 0.40242 0.00117435 0.00117435 0.00117435
12 11 64.8049 0.17208 0.86667 1 0.48505 0.00114606 0.00114606 0.00114606
13 12 69.016 0.14373 0.96 1 0.12289 0.00111776 0.00111776 0.00111776
14 13 73.4242 0.15702 0.96667 1 0.07314 0.00108947 0.00108947 0.00108947
15 14 77.6501 0.13089 0.89333 1 0.23143 0.00106118 0.00106118 0.00106118
16 15 81.9175 0.11822 0.96 1 0.10379 0.00103288 0.00103288 0.00103288
17 16 86.127 0.10769 0.96667 1 0.08193 0.00100459 0.00100459 0.00100459
18 17 90.4547 0.11051 0.93333 1 0.22058 0.000976293 0.000976293 0.000976293
19 18 95.0927 0.11295 0.94667 1 0.14745 0.000947999 0.000947999 0.000947999
20 19 99.3322 0.06974 0.95333 1 0.1651 0.000919704 0.000919704 0.000919704
21 20 103.646 0.06798 0.93333 1 0.20085 0.00089141 0.00089141 0.00089141
22 21 107.971 0.0855 0.95333 1 0.10671 0.000863116 0.000863116 0.000863116
23 22 112.457 0.06728 0.93333 1 0.14702 0.000834822 0.000834822 0.000834822
24 23 116.756 0.05302 0.96 1 0.08877 0.000806528 0.000806528 0.000806528
25 24 120.999 0.07297 0.94667 1 0.14159 0.000778233 0.000778233 0.000778233
26 25 125.401 0.07436 0.96 1 0.10724 0.000749939 0.000749939 0.000749939
27 26 129.629 0.08033 0.94 1 0.10645 0.000721645 0.000721645 0.000721645
28 27 133.971 0.06174 0.91333 1 0.23418 0.000693351 0.000693351 0.000693351
29 28 138.3 0.04208 0.94 1 0.15263 0.000665057 0.000665057 0.000665057
30 29 142.629 0.052 0.92667 1 0.17317 0.000636762 0.000636762 0.000636762
31 30 146.961 0.0384 0.92667 1 0.19084 0.000608468 0.000608468 0.000608468
32 31 151.254 0.04774 0.94667 1 0.13362 0.000580174 0.000580174 0.000580174
33 32 155.57 0.05415 0.95333 1 0.1848 0.00055188 0.00055188 0.00055188
34 33 159.855 0.05656 0.93333 1 0.14427 0.000523586 0.000523586 0.000523586
35 34 164.223 0.04054 0.92667 1 0.22511 0.000495291 0.000495291 0.000495291
36 35 168.523 0.04049 0.9 1 0.26833 0.000466997 0.000466997 0.000466997
37 36 172.765 0.04174 0.89333 1 0.45552 0.000438703 0.000438703 0.000438703
38 37 177.091 0.03381 0.94667 1 0.24308 0.000410409 0.000410409 0.000410409
39 38 181.451 0.04199 0.95333 1 0.08527 0.000382115 0.000382115 0.000382115
40 39 185.731 0.04864 0.95333 1 0.12592 0.00035382 0.00035382 0.00035382
41 40 190.064 0.02309 0.96667 1 0.10974 0.000325526 0.000325526 0.000325526
42 41 237.273 0.03537 0.96 1 0.08711 0.000297232 0.000297232 0.000297232
43 42 242.139 0.02308 0.95333 1 0.13214 0.000268938 0.000268938 0.000268938
44 43 246.852 0.01859 0.96667 1 0.10143 0.000240644 0.000240644 0.000240644
45 44 251.506 0.02491 0.96667 1 0.08824 0.000212349 0.000212349 0.000212349
46 45 255.817 0.03082 0.96667 1 0.0838 0.000184055 0.000184055 0.000184055
47 46 260.163 0.03407 0.96667 1 0.08905 0.000155761 0.000155761 0.000155761
48 47 264.522 0.03065 0.95333 1 0.09718 0.000127467 0.000127467 0.000127467
49 48 268.939 0.02238 0.95333 1 0.10553 9.91726e-05 9.91726e-05 9.91726e-05
50 49 273.344 0.02425 0.96 1 0.12025 7.08784e-05 7.08784e-05 7.08784e-05
51 50 277.599 0.02704 0.95333 1 0.10661 4.25842e-05 4.25842e-05 4.25842e-05

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