Initial commit for push folder

This commit is contained in:
Nguyen Hong Van 2026-09-18 09:13:53 +09:00
commit c4af9fcbb5
82 changed files with 3294 additions and 0 deletions

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task: detect
mode: train
model: yolo26n.pt
data: /Users/nguyenhongvan/recongnize/data/SKU/data_kaggle.yaml
epochs: 10
time: null
patience: 5
batch: 8
imgsz: 640
save: true
save_period: -1
cache: true
device: ''
workers: 2
project: null
name: train
exist_ok: true
pretrained: true
cls_remap: true
optimizer: AdamW
verbose: true
seed: 42
deterministic: true
single_cls: false
rect: false
cos_lr: true
close_mosaic: 10
resume: false
amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: 0.0
compile: false
channels_last: null
overlap_mask: true
mask_ratio: 4
dropout: 0.0
val: true
split: val
save_json: false
conf: null
iou: 0.7
max_det: 300
quantize: null
dnn: false
plots: true
nms: 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
dynamic: false
simplify: true
opset: null
workspace: null
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
distill_model: null
dis: 6.0
box: 7.5
cls: 0.5
cls_pw: 0.0
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pose: 12.0
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auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: tracktrack.yaml
save_dir: /Users/nguyenhongvan/recongnize/runs/detect/train

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epoch,time,train/box_loss,train/cls_loss,train/l1_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/l1_loss,lr/pg0,lr/pg1,lr/pg2
1,1565.97,1.87842,1.31062,0.00473,0.77594,0.66625,0.7326,0.37541,1.82395,1.09779,0.00496,0.00333008,0.00333008,0.0700293
2,3114.04,1.84885,1.03949,0.00464,0.82083,0.71551,0.78275,0.40906,1.71736,0.96438,0.00463,0.00650198,0.00650198,0.0398679
3,4713.02,1.79522,0.94506,0.0044,0.83292,0.73762,0.80286,0.44416,1.73388,0.90931,0.00467,0.00905169,0.00905169,0.00908424
4,6295.49,1.74914,0.88703,0.00428,0.85104,0.7522,0.82324,0.46497,1.64389,0.80414,0.00435,0.00795954,0.00795954,0.00795954
5,7858.73,1.7071,0.84335,0.00412,0.85919,0.7726,0.84039,0.48024,1.65108,0.78838,0.00438,0.00657963,0.00657963,0.00657963
6,9442.04,1.68114,0.80819,0.004,0.87317,0.78189,0.8509,0.49609,1.59614,0.74834,0.00414,0.00505,0.00505,0.00505
7,11003.4,1.64466,0.77151,0.00386,0.87628,0.79337,0.85823,0.50336,1.5728,0.70957,0.0041,0.00352037,0.00352037,0.00352037
8,12622.9,1.63356,0.75494,0.00384,0.88156,0.79801,0.86493,0.50986,1.56183,0.69742,0.00404,0.00214046,0.00214046,0.00214046
9,14222.6,1.60481,0.73602,0.00376,0.88322,0.8031,0.86886,0.51655,1.53246,0.69223,0.00395,0.00104537,0.00104537,0.00104537
10,15812.1,1.59291,0.72805,0.0037,0.88426,0.80407,0.8692,0.518,1.51821,0.68363,0.00389,0.00034227,0.00034227,0.00034227
1 epoch time train/box_loss train/cls_loss train/l1_loss metrics/precision(B) metrics/recall(B) metrics/mAP50(B) metrics/mAP50-95(B) val/box_loss val/cls_loss val/l1_loss lr/pg0 lr/pg1 lr/pg2
2 1 1565.97 1.87842 1.31062 0.00473 0.77594 0.66625 0.7326 0.37541 1.82395 1.09779 0.00496 0.00333008 0.00333008 0.0700293
3 2 3114.04 1.84885 1.03949 0.00464 0.82083 0.71551 0.78275 0.40906 1.71736 0.96438 0.00463 0.00650198 0.00650198 0.0398679
4 3 4713.02 1.79522 0.94506 0.0044 0.83292 0.73762 0.80286 0.44416 1.73388 0.90931 0.00467 0.00905169 0.00905169 0.00908424
5 4 6295.49 1.74914 0.88703 0.00428 0.85104 0.7522 0.82324 0.46497 1.64389 0.80414 0.00435 0.00795954 0.00795954 0.00795954
6 5 7858.73 1.7071 0.84335 0.00412 0.85919 0.7726 0.84039 0.48024 1.65108 0.78838 0.00438 0.00657963 0.00657963 0.00657963
7 6 9442.04 1.68114 0.80819 0.004 0.87317 0.78189 0.8509 0.49609 1.59614 0.74834 0.00414 0.00505 0.00505 0.00505
8 7 11003.4 1.64466 0.77151 0.00386 0.87628 0.79337 0.85823 0.50336 1.5728 0.70957 0.0041 0.00352037 0.00352037 0.00352037
9 8 12622.9 1.63356 0.75494 0.00384 0.88156 0.79801 0.86493 0.50986 1.56183 0.69742 0.00404 0.00214046 0.00214046 0.00214046
10 9 14222.6 1.60481 0.73602 0.00376 0.88322 0.8031 0.86886 0.51655 1.53246 0.69223 0.00395 0.00104537 0.00104537 0.00104537
11 10 15812.1 1.59291 0.72805 0.0037 0.88426 0.80407 0.8692 0.518 1.51821 0.68363 0.00389 0.00034227 0.00034227 0.00034227

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