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task: classify
mode: train
model: yolov8n-cls.pt
data: /home/slava/Downloads/module_v/dataset
epochs: 80
time: null
patience: 15
batch: 32
imgsz: 224
save: true
save_period: -1
cache: false
device: cpu
workers: 8
project: /home/slava/Downloads/module_v/runs
name: module_v_cls
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/slava/Downloads/module_v/runs/module_v_cls

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epoch,time,train/loss,metrics/accuracy_top1,metrics/accuracy_top5,val/loss,lr/pg0,lr/pg1,lr/pg2
1,8.74789,0.76806,1,1,0.0141,0.00040012,0.00040012,0.00040012
2,17.4699,0.16207,1,1,0.00044,0.00080445,0.00080445,0.00080445
3,26.0215,0.16387,0.99444,1,0.01007,0.00119852,0.00119852,0.00119852
4,34.6053,0.15253,1,1,0.00185,0.00137595,0.00137595,0.00137595
5,43.214,0.14246,1,1,0.0001,0.00135826,0.00135826,0.00135826
6,51.7567,0.10745,0.99444,1,0.00751,0.00134058,0.00134058,0.00134058
7,60.2915,0.08484,1,1,0.00076,0.0013229,0.0013229,0.0013229
8,68.9783,0.08449,1,1,6e-05,0.00130521,0.00130521,0.00130521
9,77.5995,0.11456,1,1,0,0.00128753,0.00128753,0.00128753
10,86.1496,0.09928,0.99444,1,0.00774,0.00126985,0.00126985,0.00126985
11,94.8016,0.07465,1,1,0.00041,0.00125216,0.00125216,0.00125216
12,103.488,0.07094,1,1,0.00049,0.00123448,0.00123448,0.00123448
13,112.053,0.07769,1,1,0.0001,0.00121679,0.00121679,0.00121679
14,120.644,0.11552,1,1,4e-05,0.00119911,0.00119911,0.00119911
15,129.334,0.06071,1,1,0.00265,0.00118143,0.00118143,0.00118143
16,137.944,0.07039,1,1,0.0001,0.00116374,0.00116374,0.00116374
1 epoch time train/loss metrics/accuracy_top1 metrics/accuracy_top5 val/loss lr/pg0 lr/pg1 lr/pg2
2 1 8.74789 0.76806 1 1 0.0141 0.00040012 0.00040012 0.00040012
3 2 17.4699 0.16207 1 1 0.00044 0.00080445 0.00080445 0.00080445
4 3 26.0215 0.16387 0.99444 1 0.01007 0.00119852 0.00119852 0.00119852
5 4 34.6053 0.15253 1 1 0.00185 0.00137595 0.00137595 0.00137595
6 5 43.214 0.14246 1 1 0.0001 0.00135826 0.00135826 0.00135826
7 6 51.7567 0.10745 0.99444 1 0.00751 0.00134058 0.00134058 0.00134058
8 7 60.2915 0.08484 1 1 0.00076 0.0013229 0.0013229 0.0013229
9 8 68.9783 0.08449 1 1 6e-05 0.00130521 0.00130521 0.00130521
10 9 77.5995 0.11456 1 1 0 0.00128753 0.00128753 0.00128753
11 10 86.1496 0.09928 0.99444 1 0.00774 0.00126985 0.00126985 0.00126985
12 11 94.8016 0.07465 1 1 0.00041 0.00125216 0.00125216 0.00125216
13 12 103.488 0.07094 1 1 0.00049 0.00123448 0.00123448 0.00123448
14 13 112.053 0.07769 1 1 0.0001 0.00121679 0.00121679 0.00121679
15 14 120.644 0.11552 1 1 4e-05 0.00119911 0.00119911 0.00119911
16 15 129.334 0.06071 1 1 0.00265 0.00118143 0.00118143 0.00118143
17 16 137.944 0.07039 1 1 0.0001 0.00116374 0.00116374 0.00116374

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