Initial commit: Recycling object detection project

- Added Fast api web application for recycling object detection
- Included YOLOv8 model training notebook
- Set up project structure with datasets and training directories
- Added requirements.txt for dependencies
- Configured .gitignore for Python virtual environment and cache files
This commit is contained in:
boladeE
2025-04-23 19:18:05 +01:00
commit a6e52f8ad0
454 changed files with 2649 additions and 0 deletions
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task: detect
mode: train
model: yolov8n.pt
data: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\data.yaml
epochs: 10
time: null
patience: 100
batch: 16
imgsz: 640
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: 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
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
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
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\runs\detect\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
1,5.84441,2.52572,4.05767,2.33015,0.01111,0.83333,0.01397,0.00503,1.80865,3.21124,1.88138,0,0,0
2,14.1917,2.49251,3.90367,2.29723,0.01222,0.91667,0.01909,0.00577,1.75159,3.20293,1.83773,1.802e-05,1.802e-05,1.802e-05
3,21.6495,2.35908,3.84944,2.26983,0.01222,0.91667,0.02178,0.00655,1.70747,3.19626,1.79613,3.208e-05,3.208e-05,3.208e-05
4,28.189,2.00446,3.79535,1.99834,0.01222,0.91667,0.03132,0.00904,1.64821,3.16944,1.75366,4.218e-05,4.218e-05,4.218e-05
5,34.8909,1.99184,3.67331,1.94502,0.01333,1,0.0291,0.01058,1.58662,3.16606,1.70011,4.832e-05,4.832e-05,4.832e-05
6,41.335,1.87066,3.55622,1.81437,0.01333,1,0.04292,0.01751,1.53459,3.13911,1.66568,5.05e-05,5.05e-05,5.05e-05
7,49.1966,1.7327,3.25049,1.64998,0.01333,1,0.04882,0.02294,1.50023,3.0825,1.61293,4.872e-05,4.872e-05,4.872e-05
8,56.5369,1.69186,3.23316,1.53494,0.01278,0.95833,0.06212,0.02922,1.45621,3.04023,1.56708,4.298e-05,4.298e-05,4.298e-05
9,63.2091,1.53858,3.09146,1.50261,0.01278,0.95833,0.08811,0.03331,1.39336,3.0391,1.51951,3.328e-05,3.328e-05,3.328e-05
10,69.7537,1.67113,3.06553,1.46777,0.01278,0.95833,0.08706,0.03455,1.38625,3.03201,1.50917,1.962e-05,1.962e-05,1.962e-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 5.84441 2.52572 4.05767 2.33015 0.01111 0.83333 0.01397 0.00503 1.80865 3.21124 1.88138 0 0 0
3 2 14.1917 2.49251 3.90367 2.29723 0.01222 0.91667 0.01909 0.00577 1.75159 3.20293 1.83773 1.802e-05 1.802e-05 1.802e-05
4 3 21.6495 2.35908 3.84944 2.26983 0.01222 0.91667 0.02178 0.00655 1.70747 3.19626 1.79613 3.208e-05 3.208e-05 3.208e-05
5 4 28.189 2.00446 3.79535 1.99834 0.01222 0.91667 0.03132 0.00904 1.64821 3.16944 1.75366 4.218e-05 4.218e-05 4.218e-05
6 5 34.8909 1.99184 3.67331 1.94502 0.01333 1 0.0291 0.01058 1.58662 3.16606 1.70011 4.832e-05 4.832e-05 4.832e-05
7 6 41.335 1.87066 3.55622 1.81437 0.01333 1 0.04292 0.01751 1.53459 3.13911 1.66568 5.05e-05 5.05e-05 5.05e-05
8 7 49.1966 1.7327 3.25049 1.64998 0.01333 1 0.04882 0.02294 1.50023 3.0825 1.61293 4.872e-05 4.872e-05 4.872e-05
9 8 56.5369 1.69186 3.23316 1.53494 0.01278 0.95833 0.06212 0.02922 1.45621 3.04023 1.56708 4.298e-05 4.298e-05 4.298e-05
10 9 63.2091 1.53858 3.09146 1.50261 0.01278 0.95833 0.08811 0.03331 1.39336 3.0391 1.51951 3.328e-05 3.328e-05 3.328e-05
11 10 69.7537 1.67113 3.06553 1.46777 0.01278 0.95833 0.08706 0.03455 1.38625 3.03201 1.50917 1.962e-05 1.962e-05 1.962e-05
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task: detect
mode: train
model: yolov8n.pt
data: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\data.yaml
epochs: 10
time: null
patience: 100
batch: 16
imgsz: 640
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: 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
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
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
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\runs\detect\train2
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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
1,4.65814,2.52572,4.05767,2.33015,0.01111,0.83333,0.01397,0.00503,1.80865,3.21124,1.88138,0,0,0
2,8.95584,2.49251,3.90367,2.29723,0.01222,0.91667,0.01909,0.00577,1.75159,3.20293,1.83773,1.802e-05,1.802e-05,1.802e-05
3,15.7381,2.35908,3.84944,2.26983,0.01222,0.91667,0.02178,0.00655,1.70747,3.19626,1.79613,3.208e-05,3.208e-05,3.208e-05
4,23.6223,2.00446,3.79535,1.99834,0.01222,0.91667,0.03132,0.00904,1.64821,3.16944,1.75366,4.218e-05,4.218e-05,4.218e-05
5,30.5386,1.99184,3.67331,1.94502,0.01333,1,0.0291,0.01058,1.58662,3.16606,1.70011,4.832e-05,4.832e-05,4.832e-05
6,37.4086,1.87066,3.55622,1.81437,0.01333,1,0.04292,0.01751,1.53459,3.13911,1.66568,5.05e-05,5.05e-05,5.05e-05
7,44.0415,1.7327,3.25049,1.64998,0.01333,1,0.04882,0.02294,1.50023,3.0825,1.61293,4.872e-05,4.872e-05,4.872e-05
8,50.527,1.69186,3.23316,1.53494,0.01278,0.95833,0.06212,0.02922,1.45621,3.04023,1.56708,4.298e-05,4.298e-05,4.298e-05
9,57.2902,1.53858,3.09146,1.50261,0.01278,0.95833,0.08811,0.03331,1.39336,3.0391,1.51951,3.328e-05,3.328e-05,3.328e-05
10,63.788,1.67113,3.06553,1.46777,0.01278,0.95833,0.08706,0.03455,1.38625,3.03201,1.50917,1.962e-05,1.962e-05,1.962e-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 4.65814 2.52572 4.05767 2.33015 0.01111 0.83333 0.01397 0.00503 1.80865 3.21124 1.88138 0 0 0
3 2 8.95584 2.49251 3.90367 2.29723 0.01222 0.91667 0.01909 0.00577 1.75159 3.20293 1.83773 1.802e-05 1.802e-05 1.802e-05
4 3 15.7381 2.35908 3.84944 2.26983 0.01222 0.91667 0.02178 0.00655 1.70747 3.19626 1.79613 3.208e-05 3.208e-05 3.208e-05
5 4 23.6223 2.00446 3.79535 1.99834 0.01222 0.91667 0.03132 0.00904 1.64821 3.16944 1.75366 4.218e-05 4.218e-05 4.218e-05
6 5 30.5386 1.99184 3.67331 1.94502 0.01333 1 0.0291 0.01058 1.58662 3.16606 1.70011 4.832e-05 4.832e-05 4.832e-05
7 6 37.4086 1.87066 3.55622 1.81437 0.01333 1 0.04292 0.01751 1.53459 3.13911 1.66568 5.05e-05 5.05e-05 5.05e-05
8 7 44.0415 1.7327 3.25049 1.64998 0.01333 1 0.04882 0.02294 1.50023 3.0825 1.61293 4.872e-05 4.872e-05 4.872e-05
9 8 50.527 1.69186 3.23316 1.53494 0.01278 0.95833 0.06212 0.02922 1.45621 3.04023 1.56708 4.298e-05 4.298e-05 4.298e-05
10 9 57.2902 1.53858 3.09146 1.50261 0.01278 0.95833 0.08811 0.03331 1.39336 3.0391 1.51951 3.328e-05 3.328e-05 3.328e-05
11 10 63.788 1.67113 3.06553 1.46777 0.01278 0.95833 0.08706 0.03455 1.38625 3.03201 1.50917 1.962e-05 1.962e-05 1.962e-05
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task: detect
mode: train
model: yolov8n.pt
data: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
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: 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
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
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
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\runs\detect\train3
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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
1,5.67823,2.39502,3.74233,2.28906,0.01111,0.83333,0.01388,0.00519,1.8298,3.20789,1.89118,0,0,0
2,16.288,2.55022,3.75033,2.33099,0.01167,0.875,0.01903,0.00579,1.73516,3.21125,1.83719,1.9604e-05,1.9604e-05,1.9604e-05
3,28.6503,2.38719,3.79239,2.28421,0.01167,0.875,0.01923,0.00684,1.62975,3.19063,1.76571,3.8416e-05,3.8416e-05,3.8416e-05
4,37.485,2.09943,3.48603,2.07603,0.01222,0.91667,0.02303,0.00844,1.57125,3.15184,1.69627,5.6436e-05,5.6436e-05,5.6436e-05
5,44.7992,2.12103,3.59398,2.07602,0.01278,0.95833,0.02936,0.01108,1.42444,3.1487,1.60115,7.3664e-05,7.3664e-05,7.3664e-05
6,52.1135,1.95239,3.41935,1.81496,0.01278,0.95833,0.04007,0.01562,1.38235,3.06553,1.50544,9.01e-05,9.01e-05,9.01e-05
7,59.9676,1.67583,3.28295,1.73853,0.01333,1,0.05015,0.02544,1.30467,3.02673,1.44765,0.000105744,0.000105744,0.000105744
8,67.8769,1.62765,3.02834,1.56366,0.01333,1,0.10045,0.04397,1.26542,2.94541,1.40131,0.000120596,0.000120596,0.000120596
9,74.6889,1.81498,3.01087,1.61769,0.01333,1,0.17666,0.07956,1.2281,2.94119,1.38457,0.000134656,0.000134656,0.000134656
10,82.6138,1.55792,2.75754,1.52268,0.01333,1,0.21511,0.11338,1.29935,2.89992,1.3765,0.000147924,0.000147924,0.000147924
11,89.8498,1.51641,2.49421,1.47669,0.01333,1,0.26881,0.13278,1.27645,2.85958,1.32994,0.0001604,0.0001604,0.0001604
12,96.4754,1.51184,2.30713,1.41096,0.01333,1,0.31061,0.1674,1.25121,2.8077,1.28717,0.000172084,0.000172084,0.000172084
13,103.321,1.51602,2.00888,1.40129,0.01333,1,0.4757,0.24692,1.2561,2.66198,1.25275,0.000182976,0.000182976,0.000182976
14,109.856,1.37113,1.66197,1.25801,0.01333,1,0.41803,0.22915,1.33355,2.60968,1.30747,0.000193076,0.000193076,0.000193076
15,116.622,1.45391,1.59786,1.37029,0.03362,0.875,0.40816,0.21489,1.36121,2.51034,1.30125,0.000202384,0.000202384,0.000202384
16,123.203,1.39235,1.5949,1.29785,0.33903,0.5,0.4044,0.23843,1.30273,2.48562,1.29281,0.0002109,0.0002109,0.0002109
17,129.845,1.31418,1.49078,1.21216,0.38404,0.375,0.40322,0.23121,1.30583,2.48418,1.32533,0.000218624,0.000218624,0.000218624
18,136.557,1.5303,1.67169,1.35183,0.38404,0.375,0.40322,0.23121,1.30583,2.48418,1.32533,0.000225556,0.000225556,0.000225556
19,143.939,1.39993,1.74236,1.39831,0.0363,0.83333,0.50162,0.2658,1.29758,2.55769,1.43345,0.000231696,0.000231696,0.000231696
20,152.008,1.39252,1.54489,1.34648,0.0363,0.83333,0.50162,0.2658,1.29758,2.55769,1.43345,0.000237044,0.000237044,0.000237044
21,159.595,1.30629,1.43423,1.31285,0.01333,1,0.41838,0.21308,1.26632,2.73525,1.48731,0.0002416,0.0002416,0.0002416
22,167.285,1.38586,1.38663,1.28934,0.01333,1,0.41838,0.21308,1.26632,2.73525,1.48731,0.000245364,0.000245364,0.000245364
23,174.209,1.46137,1.52123,1.29602,0.01333,1,0.33787,0.17736,1.27755,2.86772,1.51195,0.000248336,0.000248336,0.000248336
24,180.8,1.40314,1.38524,1.25211,0.01333,1,0.33787,0.17736,1.27755,2.86772,1.51195,0.000250516,0.000250516,0.000250516
25,188.171,1.52506,1.46222,1.28968,0.01333,1,0.26607,0.17632,1.26241,2.92442,1.52792,0.000251904,0.000251904,0.000251904
26,194.513,1.33243,1.39899,1.15744,0.01333,1,0.26607,0.17632,1.26241,2.92442,1.52792,0.0002525,0.0002525,0.0002525
27,201.199,1.2929,1.33615,1.17835,0.01333,1,0.36076,0.23671,1.21156,2.89447,1.50917,0.000252304,0.000252304,0.000252304
28,207.796,1.38689,1.49024,1.3356,0.01333,1,0.36076,0.23671,1.21156,2.89447,1.50917,0.000251316,0.000251316,0.000251316
29,214.328,1.37361,1.46786,1.18527,0.01333,1,0.39021,0.23898,1.23804,2.90968,1.55469,0.000249536,0.000249536,0.000249536
30,221.711,1.27135,1.33555,1.16232,0.01333,1,0.39021,0.23898,1.23804,2.90968,1.55469,0.000246964,0.000246964,0.000246964
31,228.398,1.29587,1.38724,1.15838,0.01333,1,0.2754,0.18092,1.34405,2.90785,1.58436,0.0002436,0.0002436,0.0002436
32,235.104,1.2612,1.30351,1.13205,0.01333,1,0.2754,0.18092,1.34405,2.90785,1.58436,0.000239444,0.000239444,0.000239444
33,242.491,1.31623,1.30861,1.13295,0.01278,0.95833,0.32768,0.17477,1.41444,2.90286,1.59092,0.000234496,0.000234496,0.000234496
34,249.752,1.28537,1.25581,1.22603,0.01278,0.95833,0.32768,0.17477,1.41444,2.90286,1.59092,0.000228756,0.000228756,0.000228756
35,256.439,1.27605,1.22113,1.18731,0.01278,0.95833,0.30914,0.15393,1.44597,2.95759,1.62449,0.000222224,0.000222224,0.000222224
36,263.426,1.14524,1.16995,1.09065,0.01278,0.95833,0.30914,0.15393,1.44597,2.95759,1.62449,0.0002149,0.0002149,0.0002149
37,270.316,1.13138,1.10565,1.16992,0.01333,1,0.31294,0.14219,1.42966,2.99147,1.65039,0.000206784,0.000206784,0.000206784
38,277.747,1.18014,1.23762,1.17946,0.01333,1,0.31294,0.14219,1.42966,2.99147,1.65039,0.000197876,0.000197876,0.000197876
39,285.394,1.15402,1.25401,1.1634,0.01278,0.95833,0.29555,0.15313,1.38078,2.9779,1.64063,0.000188176,0.000188176,0.000188176
40,293.396,1.14982,1.1206,1.17784,0.01278,0.95833,0.29555,0.15313,1.38078,2.9779,1.64063,0.000177684,0.000177684,0.000177684
41,300.699,1.11075,1.43055,1.11889,0.01333,1,0.30395,0.17321,1.3201,2.86638,1.56272,0.0001664,0.0001664,0.0001664
42,307.108,1.01372,1.31437,1.10605,0.01333,1,0.30395,0.17321,1.3201,2.86638,1.56272,0.000154324,0.000154324,0.000154324
43,313.482,1.04451,1.35932,1.13138,1,0.36287,0.48233,0.25656,1.25244,2.78164,1.51545,0.000141456,0.000141456,0.000141456
44,319.675,1.01778,1.20135,1.09701,1,0.36287,0.48233,0.25656,1.25244,2.78164,1.51545,0.000127796,0.000127796,0.000127796
45,326.458,0.98534,1.24686,1.10526,1,0.22952,0.49823,0.28839,1.20318,2.69778,1.47186,0.000113344,0.000113344,0.000113344
46,334.542,1.09942,1.26626,1.06372,1,0.22952,0.49823,0.28839,1.20318,2.69778,1.47186,9.81e-05,9.81e-05,9.81e-05
47,341.23,1.01347,1.19968,1.07937,1,0.16199,0.55856,0.31857,1.15047,2.61299,1.42365,8.2064e-05,8.2064e-05,8.2064e-05
48,347.914,1.02152,1.2012,1.07213,1,0.16199,0.55856,0.31857,1.15047,2.61299,1.42365,6.5236e-05,6.5236e-05,6.5236e-05
49,354.903,0.92922,1.16594,1.03896,1,0.16688,0.65621,0.38721,1.1464,2.54556,1.38359,4.7616e-05,4.7616e-05,4.7616e-05
50,361.006,0.97048,1.14546,1.05849,1,0.16688,0.65621,0.38721,1.1464,2.54556,1.38359,2.9204e-05,2.9204e-05,2.9204e-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 5.67823 2.39502 3.74233 2.28906 0.01111 0.83333 0.01388 0.00519 1.8298 3.20789 1.89118 0 0 0
3 2 16.288 2.55022 3.75033 2.33099 0.01167 0.875 0.01903 0.00579 1.73516 3.21125 1.83719 1.9604e-05 1.9604e-05 1.9604e-05
4 3 28.6503 2.38719 3.79239 2.28421 0.01167 0.875 0.01923 0.00684 1.62975 3.19063 1.76571 3.8416e-05 3.8416e-05 3.8416e-05
5 4 37.485 2.09943 3.48603 2.07603 0.01222 0.91667 0.02303 0.00844 1.57125 3.15184 1.69627 5.6436e-05 5.6436e-05 5.6436e-05
6 5 44.7992 2.12103 3.59398 2.07602 0.01278 0.95833 0.02936 0.01108 1.42444 3.1487 1.60115 7.3664e-05 7.3664e-05 7.3664e-05
7 6 52.1135 1.95239 3.41935 1.81496 0.01278 0.95833 0.04007 0.01562 1.38235 3.06553 1.50544 9.01e-05 9.01e-05 9.01e-05
8 7 59.9676 1.67583 3.28295 1.73853 0.01333 1 0.05015 0.02544 1.30467 3.02673 1.44765 0.000105744 0.000105744 0.000105744
9 8 67.8769 1.62765 3.02834 1.56366 0.01333 1 0.10045 0.04397 1.26542 2.94541 1.40131 0.000120596 0.000120596 0.000120596
10 9 74.6889 1.81498 3.01087 1.61769 0.01333 1 0.17666 0.07956 1.2281 2.94119 1.38457 0.000134656 0.000134656 0.000134656
11 10 82.6138 1.55792 2.75754 1.52268 0.01333 1 0.21511 0.11338 1.29935 2.89992 1.3765 0.000147924 0.000147924 0.000147924
12 11 89.8498 1.51641 2.49421 1.47669 0.01333 1 0.26881 0.13278 1.27645 2.85958 1.32994 0.0001604 0.0001604 0.0001604
13 12 96.4754 1.51184 2.30713 1.41096 0.01333 1 0.31061 0.1674 1.25121 2.8077 1.28717 0.000172084 0.000172084 0.000172084
14 13 103.321 1.51602 2.00888 1.40129 0.01333 1 0.4757 0.24692 1.2561 2.66198 1.25275 0.000182976 0.000182976 0.000182976
15 14 109.856 1.37113 1.66197 1.25801 0.01333 1 0.41803 0.22915 1.33355 2.60968 1.30747 0.000193076 0.000193076 0.000193076
16 15 116.622 1.45391 1.59786 1.37029 0.03362 0.875 0.40816 0.21489 1.36121 2.51034 1.30125 0.000202384 0.000202384 0.000202384
17 16 123.203 1.39235 1.5949 1.29785 0.33903 0.5 0.4044 0.23843 1.30273 2.48562 1.29281 0.0002109 0.0002109 0.0002109
18 17 129.845 1.31418 1.49078 1.21216 0.38404 0.375 0.40322 0.23121 1.30583 2.48418 1.32533 0.000218624 0.000218624 0.000218624
19 18 136.557 1.5303 1.67169 1.35183 0.38404 0.375 0.40322 0.23121 1.30583 2.48418 1.32533 0.000225556 0.000225556 0.000225556
20 19 143.939 1.39993 1.74236 1.39831 0.0363 0.83333 0.50162 0.2658 1.29758 2.55769 1.43345 0.000231696 0.000231696 0.000231696
21 20 152.008 1.39252 1.54489 1.34648 0.0363 0.83333 0.50162 0.2658 1.29758 2.55769 1.43345 0.000237044 0.000237044 0.000237044
22 21 159.595 1.30629 1.43423 1.31285 0.01333 1 0.41838 0.21308 1.26632 2.73525 1.48731 0.0002416 0.0002416 0.0002416
23 22 167.285 1.38586 1.38663 1.28934 0.01333 1 0.41838 0.21308 1.26632 2.73525 1.48731 0.000245364 0.000245364 0.000245364
24 23 174.209 1.46137 1.52123 1.29602 0.01333 1 0.33787 0.17736 1.27755 2.86772 1.51195 0.000248336 0.000248336 0.000248336
25 24 180.8 1.40314 1.38524 1.25211 0.01333 1 0.33787 0.17736 1.27755 2.86772 1.51195 0.000250516 0.000250516 0.000250516
26 25 188.171 1.52506 1.46222 1.28968 0.01333 1 0.26607 0.17632 1.26241 2.92442 1.52792 0.000251904 0.000251904 0.000251904
27 26 194.513 1.33243 1.39899 1.15744 0.01333 1 0.26607 0.17632 1.26241 2.92442 1.52792 0.0002525 0.0002525 0.0002525
28 27 201.199 1.2929 1.33615 1.17835 0.01333 1 0.36076 0.23671 1.21156 2.89447 1.50917 0.000252304 0.000252304 0.000252304
29 28 207.796 1.38689 1.49024 1.3356 0.01333 1 0.36076 0.23671 1.21156 2.89447 1.50917 0.000251316 0.000251316 0.000251316
30 29 214.328 1.37361 1.46786 1.18527 0.01333 1 0.39021 0.23898 1.23804 2.90968 1.55469 0.000249536 0.000249536 0.000249536
31 30 221.711 1.27135 1.33555 1.16232 0.01333 1 0.39021 0.23898 1.23804 2.90968 1.55469 0.000246964 0.000246964 0.000246964
32 31 228.398 1.29587 1.38724 1.15838 0.01333 1 0.2754 0.18092 1.34405 2.90785 1.58436 0.0002436 0.0002436 0.0002436
33 32 235.104 1.2612 1.30351 1.13205 0.01333 1 0.2754 0.18092 1.34405 2.90785 1.58436 0.000239444 0.000239444 0.000239444
34 33 242.491 1.31623 1.30861 1.13295 0.01278 0.95833 0.32768 0.17477 1.41444 2.90286 1.59092 0.000234496 0.000234496 0.000234496
35 34 249.752 1.28537 1.25581 1.22603 0.01278 0.95833 0.32768 0.17477 1.41444 2.90286 1.59092 0.000228756 0.000228756 0.000228756
36 35 256.439 1.27605 1.22113 1.18731 0.01278 0.95833 0.30914 0.15393 1.44597 2.95759 1.62449 0.000222224 0.000222224 0.000222224
37 36 263.426 1.14524 1.16995 1.09065 0.01278 0.95833 0.30914 0.15393 1.44597 2.95759 1.62449 0.0002149 0.0002149 0.0002149
38 37 270.316 1.13138 1.10565 1.16992 0.01333 1 0.31294 0.14219 1.42966 2.99147 1.65039 0.000206784 0.000206784 0.000206784
39 38 277.747 1.18014 1.23762 1.17946 0.01333 1 0.31294 0.14219 1.42966 2.99147 1.65039 0.000197876 0.000197876 0.000197876
40 39 285.394 1.15402 1.25401 1.1634 0.01278 0.95833 0.29555 0.15313 1.38078 2.9779 1.64063 0.000188176 0.000188176 0.000188176
41 40 293.396 1.14982 1.1206 1.17784 0.01278 0.95833 0.29555 0.15313 1.38078 2.9779 1.64063 0.000177684 0.000177684 0.000177684
42 41 300.699 1.11075 1.43055 1.11889 0.01333 1 0.30395 0.17321 1.3201 2.86638 1.56272 0.0001664 0.0001664 0.0001664
43 42 307.108 1.01372 1.31437 1.10605 0.01333 1 0.30395 0.17321 1.3201 2.86638 1.56272 0.000154324 0.000154324 0.000154324
44 43 313.482 1.04451 1.35932 1.13138 1 0.36287 0.48233 0.25656 1.25244 2.78164 1.51545 0.000141456 0.000141456 0.000141456
45 44 319.675 1.01778 1.20135 1.09701 1 0.36287 0.48233 0.25656 1.25244 2.78164 1.51545 0.000127796 0.000127796 0.000127796
46 45 326.458 0.98534 1.24686 1.10526 1 0.22952 0.49823 0.28839 1.20318 2.69778 1.47186 0.000113344 0.000113344 0.000113344
47 46 334.542 1.09942 1.26626 1.06372 1 0.22952 0.49823 0.28839 1.20318 2.69778 1.47186 9.81e-05 9.81e-05 9.81e-05
48 47 341.23 1.01347 1.19968 1.07937 1 0.16199 0.55856 0.31857 1.15047 2.61299 1.42365 8.2064e-05 8.2064e-05 8.2064e-05
49 48 347.914 1.02152 1.2012 1.07213 1 0.16199 0.55856 0.31857 1.15047 2.61299 1.42365 6.5236e-05 6.5236e-05 6.5236e-05
50 49 354.903 0.92922 1.16594 1.03896 1 0.16688 0.65621 0.38721 1.1464 2.54556 1.38359 4.7616e-05 4.7616e-05 4.7616e-05
51 50 361.006 0.97048 1.14546 1.05849 1 0.16688 0.65621 0.38721 1.1464 2.54556 1.38359 2.9204e-05 2.9204e-05 2.9204e-05
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task: detect
mode: train
model: yolov8n.pt
data: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
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: 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
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
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
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\runs\detect\train4
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+51
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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
1,5.77103,2.39502,3.74233,2.28906,0.01111,0.83333,0.01388,0.00519,1.8298,3.20789,1.89118,0,0,0
2,11.2977,2.55022,3.75033,2.33099,0.01167,0.875,0.01903,0.00579,1.73516,3.21125,1.83719,1.9604e-05,1.9604e-05,1.9604e-05
3,20.2221,2.38719,3.79239,2.28421,0.01167,0.875,0.01923,0.00684,1.62975,3.19063,1.76571,3.8416e-05,3.8416e-05,3.8416e-05
4,27.4798,2.09943,3.48603,2.07603,0.01222,0.91667,0.02303,0.00844,1.57125,3.15184,1.69627,5.6436e-05,5.6436e-05,5.6436e-05
5,34.249,2.12103,3.59398,2.07602,0.01278,0.95833,0.02936,0.01108,1.42444,3.1487,1.60115,7.3664e-05,7.3664e-05,7.3664e-05
6,41.5492,1.95239,3.41935,1.81496,0.01278,0.95833,0.04007,0.01562,1.38235,3.06553,1.50544,9.01e-05,9.01e-05,9.01e-05
7,48.3184,1.67583,3.28295,1.73853,0.01333,1,0.05015,0.02544,1.30467,3.02673,1.44765,0.000105744,0.000105744,0.000105744
8,55.5926,1.62765,3.02834,1.56366,0.01333,1,0.10045,0.04397,1.26542,2.94541,1.40131,0.000120596,0.000120596,0.000120596
9,62.7108,1.81498,3.01087,1.61769,0.01333,1,0.17666,0.07956,1.2281,2.94119,1.38457,0.000134656,0.000134656,0.000134656
10,69.6239,1.55792,2.75754,1.52268,0.01333,1,0.21511,0.11338,1.29935,2.89992,1.3765,0.000147924,0.000147924,0.000147924
11,76.6196,1.51641,2.49421,1.47669,0.01333,1,0.26881,0.13278,1.27645,2.85958,1.32994,0.0001604,0.0001604,0.0001604
12,83.1946,1.51184,2.30713,1.41096,0.01333,1,0.31061,0.1674,1.25121,2.8077,1.28717,0.000172084,0.000172084,0.000172084
13,90.515,1.51602,2.00888,1.40129,0.01333,1,0.4757,0.24692,1.2561,2.66198,1.25275,0.000182976,0.000182976,0.000182976
14,98.1615,1.37113,1.66197,1.25801,0.01333,1,0.41803,0.22915,1.33355,2.60968,1.30747,0.000193076,0.000193076,0.000193076
15,104.684,1.45391,1.59786,1.37029,0.03362,0.875,0.40816,0.21489,1.36121,2.51034,1.30125,0.000202384,0.000202384,0.000202384
16,111.213,1.39235,1.5949,1.29785,0.33903,0.5,0.4044,0.23843,1.30273,2.48562,1.29281,0.0002109,0.0002109,0.0002109
17,118.594,1.31418,1.49078,1.21216,0.38404,0.375,0.40322,0.23121,1.30583,2.48418,1.32533,0.000218624,0.000218624,0.000218624
18,126.405,1.5303,1.67169,1.35183,0.38404,0.375,0.40322,0.23121,1.30583,2.48418,1.32533,0.000225556,0.000225556,0.000225556
19,133.907,1.39993,1.74236,1.39831,0.0363,0.83333,0.50162,0.2658,1.29758,2.55769,1.43345,0.000231696,0.000231696,0.000231696
20,141.029,1.39252,1.54489,1.34648,0.0363,0.83333,0.50162,0.2658,1.29758,2.55769,1.43345,0.000237044,0.000237044,0.000237044
21,147.427,1.30629,1.43423,1.31285,0.01333,1,0.41838,0.21308,1.26632,2.73525,1.48731,0.0002416,0.0002416,0.0002416
22,154.374,1.38586,1.38663,1.28934,0.01333,1,0.41838,0.21308,1.26632,2.73525,1.48731,0.000245364,0.000245364,0.000245364
23,160.791,1.46137,1.52123,1.29602,0.01333,1,0.33787,0.17736,1.27755,2.86772,1.51195,0.000248336,0.000248336,0.000248336
24,167.375,1.40314,1.38524,1.25211,0.01333,1,0.33787,0.17736,1.27755,2.86772,1.51195,0.000250516,0.000250516,0.000250516
25,173.764,1.52506,1.46222,1.28968,0.01333,1,0.26607,0.17632,1.26241,2.92442,1.52792,0.000251904,0.000251904,0.000251904
26,181.025,1.33243,1.39899,1.15744,0.01333,1,0.26607,0.17632,1.26241,2.92442,1.52792,0.0002525,0.0002525,0.0002525
27,187.713,1.2929,1.33615,1.17835,0.01333,1,0.36076,0.23671,1.21156,2.89447,1.50917,0.000252304,0.000252304,0.000252304
28,193.907,1.38689,1.49024,1.3356,0.01333,1,0.36076,0.23671,1.21156,2.89447,1.50917,0.000251316,0.000251316,0.000251316
29,200.496,1.37361,1.46786,1.18527,0.01333,1,0.39021,0.23898,1.23804,2.90968,1.55469,0.000249536,0.000249536,0.000249536
30,208.513,1.27135,1.33555,1.16232,0.01333,1,0.39021,0.23898,1.23804,2.90968,1.55469,0.000246964,0.000246964,0.000246964
31,215.615,1.29587,1.38724,1.15838,0.01333,1,0.2754,0.18092,1.34405,2.90785,1.58436,0.0002436,0.0002436,0.0002436
32,223.531,1.2612,1.30351,1.13205,0.01333,1,0.2754,0.18092,1.34405,2.90785,1.58436,0.000239444,0.000239444,0.000239444
33,230.452,1.31623,1.30861,1.13295,0.01278,0.95833,0.32768,0.17477,1.41444,2.90286,1.59092,0.000234496,0.000234496,0.000234496
34,238.213,1.28537,1.25581,1.22603,0.01278,0.95833,0.32768,0.17477,1.41444,2.90286,1.59092,0.000228756,0.000228756,0.000228756
35,244.394,1.27605,1.22113,1.18731,0.01278,0.95833,0.30914,0.15393,1.44597,2.95759,1.62449,0.000222224,0.000222224,0.000222224
36,250.874,1.14524,1.16995,1.09065,0.01278,0.95833,0.30914,0.15393,1.44597,2.95759,1.62449,0.0002149,0.0002149,0.0002149
37,257.244,1.13138,1.10565,1.16992,0.01333,1,0.31294,0.14219,1.42966,2.99147,1.65039,0.000206784,0.000206784,0.000206784
38,263.772,1.18014,1.23762,1.17946,0.01333,1,0.31294,0.14219,1.42966,2.99147,1.65039,0.000197876,0.000197876,0.000197876
39,271.22,1.15402,1.25401,1.1634,0.01278,0.95833,0.29555,0.15313,1.38078,2.9779,1.64063,0.000188176,0.000188176,0.000188176
40,278.293,1.14982,1.1206,1.17784,0.01278,0.95833,0.29555,0.15313,1.38078,2.9779,1.64063,0.000177684,0.000177684,0.000177684
41,285.814,1.11075,1.43055,1.11889,0.01333,1,0.30395,0.17321,1.3201,2.86638,1.56272,0.0001664,0.0001664,0.0001664
42,292.792,1.01372,1.31437,1.10605,0.01333,1,0.30395,0.17321,1.3201,2.86638,1.56272,0.000154324,0.000154324,0.000154324
43,299.236,1.04451,1.35932,1.13138,1,0.36287,0.48233,0.25656,1.25244,2.78164,1.51545,0.000141456,0.000141456,0.000141456
44,306.258,1.01778,1.20135,1.09701,1,0.36287,0.48233,0.25656,1.25244,2.78164,1.51545,0.000127796,0.000127796,0.000127796
45,312.829,0.98534,1.24686,1.10526,1,0.22952,0.49823,0.28839,1.20318,2.69778,1.47186,0.000113344,0.000113344,0.000113344
46,319.193,1.09942,1.26626,1.06372,1,0.22952,0.49823,0.28839,1.20318,2.69778,1.47186,9.81e-05,9.81e-05,9.81e-05
47,326.22,1.01347,1.19968,1.07937,1,0.16199,0.55856,0.31857,1.15047,2.61299,1.42365,8.2064e-05,8.2064e-05,8.2064e-05
48,332.77,1.02152,1.2012,1.07213,1,0.16199,0.55856,0.31857,1.15047,2.61299,1.42365,6.5236e-05,6.5236e-05,6.5236e-05
49,339.411,0.92922,1.16594,1.03896,1,0.16688,0.65621,0.38721,1.1464,2.54556,1.38359,4.7616e-05,4.7616e-05,4.7616e-05
50,345.931,0.97048,1.14546,1.05849,1,0.16688,0.65621,0.38721,1.1464,2.54556,1.38359,2.9204e-05,2.9204e-05,2.9204e-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 5.77103 2.39502 3.74233 2.28906 0.01111 0.83333 0.01388 0.00519 1.8298 3.20789 1.89118 0 0 0
3 2 11.2977 2.55022 3.75033 2.33099 0.01167 0.875 0.01903 0.00579 1.73516 3.21125 1.83719 1.9604e-05 1.9604e-05 1.9604e-05
4 3 20.2221 2.38719 3.79239 2.28421 0.01167 0.875 0.01923 0.00684 1.62975 3.19063 1.76571 3.8416e-05 3.8416e-05 3.8416e-05
5 4 27.4798 2.09943 3.48603 2.07603 0.01222 0.91667 0.02303 0.00844 1.57125 3.15184 1.69627 5.6436e-05 5.6436e-05 5.6436e-05
6 5 34.249 2.12103 3.59398 2.07602 0.01278 0.95833 0.02936 0.01108 1.42444 3.1487 1.60115 7.3664e-05 7.3664e-05 7.3664e-05
7 6 41.5492 1.95239 3.41935 1.81496 0.01278 0.95833 0.04007 0.01562 1.38235 3.06553 1.50544 9.01e-05 9.01e-05 9.01e-05
8 7 48.3184 1.67583 3.28295 1.73853 0.01333 1 0.05015 0.02544 1.30467 3.02673 1.44765 0.000105744 0.000105744 0.000105744
9 8 55.5926 1.62765 3.02834 1.56366 0.01333 1 0.10045 0.04397 1.26542 2.94541 1.40131 0.000120596 0.000120596 0.000120596
10 9 62.7108 1.81498 3.01087 1.61769 0.01333 1 0.17666 0.07956 1.2281 2.94119 1.38457 0.000134656 0.000134656 0.000134656
11 10 69.6239 1.55792 2.75754 1.52268 0.01333 1 0.21511 0.11338 1.29935 2.89992 1.3765 0.000147924 0.000147924 0.000147924
12 11 76.6196 1.51641 2.49421 1.47669 0.01333 1 0.26881 0.13278 1.27645 2.85958 1.32994 0.0001604 0.0001604 0.0001604
13 12 83.1946 1.51184 2.30713 1.41096 0.01333 1 0.31061 0.1674 1.25121 2.8077 1.28717 0.000172084 0.000172084 0.000172084
14 13 90.515 1.51602 2.00888 1.40129 0.01333 1 0.4757 0.24692 1.2561 2.66198 1.25275 0.000182976 0.000182976 0.000182976
15 14 98.1615 1.37113 1.66197 1.25801 0.01333 1 0.41803 0.22915 1.33355 2.60968 1.30747 0.000193076 0.000193076 0.000193076
16 15 104.684 1.45391 1.59786 1.37029 0.03362 0.875 0.40816 0.21489 1.36121 2.51034 1.30125 0.000202384 0.000202384 0.000202384
17 16 111.213 1.39235 1.5949 1.29785 0.33903 0.5 0.4044 0.23843 1.30273 2.48562 1.29281 0.0002109 0.0002109 0.0002109
18 17 118.594 1.31418 1.49078 1.21216 0.38404 0.375 0.40322 0.23121 1.30583 2.48418 1.32533 0.000218624 0.000218624 0.000218624
19 18 126.405 1.5303 1.67169 1.35183 0.38404 0.375 0.40322 0.23121 1.30583 2.48418 1.32533 0.000225556 0.000225556 0.000225556
20 19 133.907 1.39993 1.74236 1.39831 0.0363 0.83333 0.50162 0.2658 1.29758 2.55769 1.43345 0.000231696 0.000231696 0.000231696
21 20 141.029 1.39252 1.54489 1.34648 0.0363 0.83333 0.50162 0.2658 1.29758 2.55769 1.43345 0.000237044 0.000237044 0.000237044
22 21 147.427 1.30629 1.43423 1.31285 0.01333 1 0.41838 0.21308 1.26632 2.73525 1.48731 0.0002416 0.0002416 0.0002416
23 22 154.374 1.38586 1.38663 1.28934 0.01333 1 0.41838 0.21308 1.26632 2.73525 1.48731 0.000245364 0.000245364 0.000245364
24 23 160.791 1.46137 1.52123 1.29602 0.01333 1 0.33787 0.17736 1.27755 2.86772 1.51195 0.000248336 0.000248336 0.000248336
25 24 167.375 1.40314 1.38524 1.25211 0.01333 1 0.33787 0.17736 1.27755 2.86772 1.51195 0.000250516 0.000250516 0.000250516
26 25 173.764 1.52506 1.46222 1.28968 0.01333 1 0.26607 0.17632 1.26241 2.92442 1.52792 0.000251904 0.000251904 0.000251904
27 26 181.025 1.33243 1.39899 1.15744 0.01333 1 0.26607 0.17632 1.26241 2.92442 1.52792 0.0002525 0.0002525 0.0002525
28 27 187.713 1.2929 1.33615 1.17835 0.01333 1 0.36076 0.23671 1.21156 2.89447 1.50917 0.000252304 0.000252304 0.000252304
29 28 193.907 1.38689 1.49024 1.3356 0.01333 1 0.36076 0.23671 1.21156 2.89447 1.50917 0.000251316 0.000251316 0.000251316
30 29 200.496 1.37361 1.46786 1.18527 0.01333 1 0.39021 0.23898 1.23804 2.90968 1.55469 0.000249536 0.000249536 0.000249536
31 30 208.513 1.27135 1.33555 1.16232 0.01333 1 0.39021 0.23898 1.23804 2.90968 1.55469 0.000246964 0.000246964 0.000246964
32 31 215.615 1.29587 1.38724 1.15838 0.01333 1 0.2754 0.18092 1.34405 2.90785 1.58436 0.0002436 0.0002436 0.0002436
33 32 223.531 1.2612 1.30351 1.13205 0.01333 1 0.2754 0.18092 1.34405 2.90785 1.58436 0.000239444 0.000239444 0.000239444
34 33 230.452 1.31623 1.30861 1.13295 0.01278 0.95833 0.32768 0.17477 1.41444 2.90286 1.59092 0.000234496 0.000234496 0.000234496
35 34 238.213 1.28537 1.25581 1.22603 0.01278 0.95833 0.32768 0.17477 1.41444 2.90286 1.59092 0.000228756 0.000228756 0.000228756
36 35 244.394 1.27605 1.22113 1.18731 0.01278 0.95833 0.30914 0.15393 1.44597 2.95759 1.62449 0.000222224 0.000222224 0.000222224
37 36 250.874 1.14524 1.16995 1.09065 0.01278 0.95833 0.30914 0.15393 1.44597 2.95759 1.62449 0.0002149 0.0002149 0.0002149
38 37 257.244 1.13138 1.10565 1.16992 0.01333 1 0.31294 0.14219 1.42966 2.99147 1.65039 0.000206784 0.000206784 0.000206784
39 38 263.772 1.18014 1.23762 1.17946 0.01333 1 0.31294 0.14219 1.42966 2.99147 1.65039 0.000197876 0.000197876 0.000197876
40 39 271.22 1.15402 1.25401 1.1634 0.01278 0.95833 0.29555 0.15313 1.38078 2.9779 1.64063 0.000188176 0.000188176 0.000188176
41 40 278.293 1.14982 1.1206 1.17784 0.01278 0.95833 0.29555 0.15313 1.38078 2.9779 1.64063 0.000177684 0.000177684 0.000177684
42 41 285.814 1.11075 1.43055 1.11889 0.01333 1 0.30395 0.17321 1.3201 2.86638 1.56272 0.0001664 0.0001664 0.0001664
43 42 292.792 1.01372 1.31437 1.10605 0.01333 1 0.30395 0.17321 1.3201 2.86638 1.56272 0.000154324 0.000154324 0.000154324
44 43 299.236 1.04451 1.35932 1.13138 1 0.36287 0.48233 0.25656 1.25244 2.78164 1.51545 0.000141456 0.000141456 0.000141456
45 44 306.258 1.01778 1.20135 1.09701 1 0.36287 0.48233 0.25656 1.25244 2.78164 1.51545 0.000127796 0.000127796 0.000127796
46 45 312.829 0.98534 1.24686 1.10526 1 0.22952 0.49823 0.28839 1.20318 2.69778 1.47186 0.000113344 0.000113344 0.000113344
47 46 319.193 1.09942 1.26626 1.06372 1 0.22952 0.49823 0.28839 1.20318 2.69778 1.47186 9.81e-05 9.81e-05 9.81e-05
48 47 326.22 1.01347 1.19968 1.07937 1 0.16199 0.55856 0.31857 1.15047 2.61299 1.42365 8.2064e-05 8.2064e-05 8.2064e-05
49 48 332.77 1.02152 1.2012 1.07213 1 0.16199 0.55856 0.31857 1.15047 2.61299 1.42365 6.5236e-05 6.5236e-05 6.5236e-05
50 49 339.411 0.92922 1.16594 1.03896 1 0.16688 0.65621 0.38721 1.1464 2.54556 1.38359 4.7616e-05 4.7616e-05 4.7616e-05
51 50 345.931 0.97048 1.14546 1.05849 1 0.16688 0.65621 0.38721 1.1464 2.54556 1.38359 2.9204e-05 2.9204e-05 2.9204e-05
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task: detect
mode: train
model: yolov8n.pt
data: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: null
workers: 8
project: null
name: train7
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: 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
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
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
copy_paste: 0.0
copy_paste_mode: flip
auto_augment: randaugment
erasing: 0.4
cfg: null
tracker: botsort.yaml
save_dir: c:\Users\babaw\Documents\Work\Mana Knight Digital\ds_task_recycling_project\datasets\runs\detect\train7
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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
1,54.2497,2.06475,3.28559,1.98692,0.01333,1,0.33729,0.17014,1.93235,2.90451,1.84283,0.00018,0.00018,0.00018
2,105.789,1.60795,1.73886,1.42903,0.04429,0.70833,0.6133,0.31939,1.85374,2.44436,1.72346,0.000372476,0.000372476,0.000372476
3,157.32,1.48166,1.39392,1.33944,0.01333,1,0.58472,0.19178,1.72844,2.89381,1.78894,0.000557032,0.000557032,0.000557032
4,208.774,1.34582,1.22969,1.27148,0.01333,1,0.6083,0.19769,2.02742,2.67312,1.90923,0.000733668,0.000733668,0.000733668
5,263.416,1.29696,1.1387,1.27776,0.01167,0.875,0.32824,0.12628,1.80957,2.9277,1.93331,0.000902384,0.000902384,0.000902384
6,316.659,1.32609,1.19217,1.29891,1,0.32314,0.55136,0.25631,1.65501,2.36249,1.66467,0.00106318,0.00106318,0.00106318
7,370.237,1.26765,1.09562,1.27424,0.83303,0.41585,0.55229,0.28987,1.64476,2.14156,1.65976,0.00121606,0.00121606,0.00121606
8,424.139,1.24118,1.02811,1.21346,1,0.23779,0.31152,0.1936,1.53292,2.41104,1.74351,0.00136101,0.00136101,0.00136101
9,479.369,1.2307,1.01492,1.21561,0.87007,0.28016,0.40512,0.23051,1.60375,2.01821,1.85871,0.00149805,0.00149805,0.00149805
10,538.627,1.25632,1.07046,1.25763,0.73801,0.375,0.45316,0.25944,1.55876,2.01301,1.83093,0.00162716,0.00162716,0.00162716
11,596.26,1.19315,1.0017,1.23698,0.94133,0.45833,0.616,0.37669,1.54146,1.88414,1.79534,0.001604,0.001604,0.001604
12,655.016,1.21302,0.95347,1.25888,0.87584,0.41667,0.50074,0.25638,1.75181,2.02174,1.98871,0.0015644,0.0015644,0.0015644
13,715.277,1.15439,0.91539,1.20183,1,0.57733,0.74564,0.37953,1.8085,1.333,1.96653,0.0015248,0.0015248,0.0015248
14,777.134,1.09638,0.85925,1.16466,0.93632,0.70833,0.85861,0.49064,1.49037,0.94339,1.79087,0.0014852,0.0014852,0.0014852
15,843.665,1.05888,0.81842,1.15457,0.76475,0.70833,0.83217,0.52387,1.47243,0.98751,1.76234,0.0014456,0.0014456,0.0014456
16,909.287,1.04644,0.8167,1.14854,0.77532,0.71927,0.79227,0.50756,1.30848,1.29365,1.53777,0.001406,0.001406,0.001406
17,973.886,1.05134,0.8072,1.15606,0.77957,0.58971,0.67932,0.39674,1.43737,1.38893,1.62843,0.0013664,0.0013664,0.0013664
18,1038.19,1.01895,0.76075,1.12363,0.75585,0.75,0.79299,0.54014,1.19492,1.06695,1.43887,0.0013268,0.0013268,0.0013268
19,1102.59,0.99528,0.74277,1.10987,0.84396,0.70833,0.80284,0.5417,1.26264,0.86554,1.50366,0.0012872,0.0012872,0.0012872
20,1169.64,1.02288,0.74384,1.13908,0.92166,0.79167,0.89138,0.42992,1.82014,0.95111,2.05613,0.0012476,0.0012476,0.0012476
21,1235.9,0.97969,0.72155,1.10969,0.87089,0.79167,0.87398,0.49915,1.61727,0.97409,1.8339,0.001208,0.001208,0.001208
22,1301.63,0.96498,0.69427,1.08961,0.82713,0.59862,0.8583,0.44535,1.49811,0.94941,1.6617,0.0011684,0.0011684,0.0011684
23,1375.05,0.97176,0.69995,1.12122,0.98851,0.5,0.70735,0.36377,1.45452,0.90547,1.56864,0.0011288,0.0011288,0.0011288
24,1483,0.93529,0.66614,1.09092,1,0.78404,0.92018,0.52713,1.26578,0.81984,1.39338,0.0010892,0.0010892,0.0010892
25,1570.57,0.94593,0.67634,1.09923,1,0.82488,0.93674,0.51259,1.43898,0.95183,1.44066,0.0010496,0.0010496,0.0010496
26,1640.5,0.9005,0.64656,1.08853,0.85685,0.79167,0.87822,0.59599,1.00997,0.74692,1.25779,0.00101,0.00101,0.00101
27,1716.79,0.88047,0.65839,1.0819,0.98302,0.66667,0.79722,0.47007,1.44935,0.90317,1.58064,0.0009704,0.0009704,0.0009704
28,1790.17,0.92727,0.6491,1.07941,0.96438,0.79167,0.88789,0.51198,1.41082,0.95921,1.55489,0.0009308,0.0009308,0.0009308
29,1863.25,0.86808,0.61314,1.06347,1,0.73943,0.85062,0.53272,1.26679,0.77515,1.5057,0.0008912,0.0008912,0.0008912
30,1935.03,0.85237,0.60318,1.03757,0.86759,0.81945,0.90299,0.56004,1.32215,0.70391,1.68444,0.0008516,0.0008516,0.0008516
31,2006.05,0.85848,0.59069,1.04725,0.91204,0.86446,0.93329,0.61701,1.22052,0.70212,1.53785,0.000812,0.000812,0.000812
32,2074.85,0.8607,0.60328,1.06674,0.99448,0.83333,0.96865,0.58854,1.34406,0.70627,1.61132,0.0007724,0.0007724,0.0007724
33,2146.12,0.81712,0.57815,1.03472,0.94911,0.91667,0.94354,0.57918,1.23064,0.65719,1.46887,0.0007328,0.0007328,0.0007328
34,2215.61,0.82113,0.56747,1.03675,0.98124,0.75,0.8458,0.43565,1.65251,0.69818,1.86693,0.0006932,0.0006932,0.0006932
35,2283.46,0.80104,0.57305,1.02593,0.9542,0.86835,0.90976,0.48068,1.43441,0.64857,1.70736,0.0006536,0.0006536,0.0006536
36,2362.48,0.83103,0.57551,1.03499,0.87273,0.85758,0.9038,0.44998,1.5691,0.69063,1.97723,0.000614,0.000614,0.000614
37,2432.12,0.80527,0.56112,1.02551,0.80806,0.87714,0.90845,0.47423,1.51444,0.64712,1.89872,0.0005744,0.0005744,0.0005744
38,2508.79,0.7726,0.55171,1.01768,0.89864,0.79167,0.89273,0.51093,1.3883,0.59906,1.7624,0.0005348,0.0005348,0.0005348
39,2577.65,0.7675,0.53945,1.00972,0.83538,0.83333,0.91731,0.51633,1.4967,0.65808,1.87353,0.0004952,0.0004952,0.0004952
40,2648.26,0.76832,0.54621,1.01197,0.9061,0.83333,0.92334,0.49587,1.6318,0.72062,1.97779,0.0004556,0.0004556,0.0004556
41,2723.96,0.69593,0.54869,0.98187,0.95342,0.875,0.91278,0.46972,1.66482,0.70379,2.11677,0.000416,0.000416,0.000416
42,2803,0.71207,0.53965,0.99659,0.95001,0.83333,0.85669,0.46264,1.56204,0.65859,2.0364,0.0003764,0.0003764,0.0003764
43,2877.49,0.69033,0.51838,0.98369,0.95411,0.86665,0.92135,0.49602,1.5114,0.65125,1.90967,0.0003368,0.0003368,0.0003368
44,2955.03,0.67106,0.49273,0.97654,0.92953,0.75,0.888,0.47663,1.56378,0.62817,1.96979,0.0002972,0.0002972,0.0002972
45,3030.88,0.66399,0.48501,0.96783,0.86873,0.875,0.94374,0.5493,1.37376,0.62126,1.76597,0.0002576,0.0002576,0.0002576
46,3201.5,0.66717,0.48721,0.9668,0.87544,0.91667,0.9293,0.53737,1.33496,0.61767,1.70695,0.000218,0.000218,0.000218
47,3262.79,0.63017,0.47082,0.96586,0.94224,0.875,0.95991,0.50304,1.41911,0.62492,1.79879,0.0001784,0.0001784,0.0001784
48,3324.62,0.63678,0.47311,0.95304,0.98764,0.79167,0.90331,0.49543,1.4591,0.63498,1.87332,0.0001388,0.0001388,0.0001388
49,3387.48,0.63551,0.46808,0.931,0.88336,0.94704,0.93836,0.53024,1.40884,0.61606,1.80694,9.92e-05,9.92e-05,9.92e-05
50,3457.88,0.60539,0.45653,0.94608,0.91257,0.87003,0.90122,0.50731,1.39162,0.61616,1.78099,5.96e-05,5.96e-05,5.96e-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 54.2497 2.06475 3.28559 1.98692 0.01333 1 0.33729 0.17014 1.93235 2.90451 1.84283 0.00018 0.00018 0.00018
3 2 105.789 1.60795 1.73886 1.42903 0.04429 0.70833 0.6133 0.31939 1.85374 2.44436 1.72346 0.000372476 0.000372476 0.000372476
4 3 157.32 1.48166 1.39392 1.33944 0.01333 1 0.58472 0.19178 1.72844 2.89381 1.78894 0.000557032 0.000557032 0.000557032
5 4 208.774 1.34582 1.22969 1.27148 0.01333 1 0.6083 0.19769 2.02742 2.67312 1.90923 0.000733668 0.000733668 0.000733668
6 5 263.416 1.29696 1.1387 1.27776 0.01167 0.875 0.32824 0.12628 1.80957 2.9277 1.93331 0.000902384 0.000902384 0.000902384
7 6 316.659 1.32609 1.19217 1.29891 1 0.32314 0.55136 0.25631 1.65501 2.36249 1.66467 0.00106318 0.00106318 0.00106318
8 7 370.237 1.26765 1.09562 1.27424 0.83303 0.41585 0.55229 0.28987 1.64476 2.14156 1.65976 0.00121606 0.00121606 0.00121606
9 8 424.139 1.24118 1.02811 1.21346 1 0.23779 0.31152 0.1936 1.53292 2.41104 1.74351 0.00136101 0.00136101 0.00136101
10 9 479.369 1.2307 1.01492 1.21561 0.87007 0.28016 0.40512 0.23051 1.60375 2.01821 1.85871 0.00149805 0.00149805 0.00149805
11 10 538.627 1.25632 1.07046 1.25763 0.73801 0.375 0.45316 0.25944 1.55876 2.01301 1.83093 0.00162716 0.00162716 0.00162716
12 11 596.26 1.19315 1.0017 1.23698 0.94133 0.45833 0.616 0.37669 1.54146 1.88414 1.79534 0.001604 0.001604 0.001604
13 12 655.016 1.21302 0.95347 1.25888 0.87584 0.41667 0.50074 0.25638 1.75181 2.02174 1.98871 0.0015644 0.0015644 0.0015644
14 13 715.277 1.15439 0.91539 1.20183 1 0.57733 0.74564 0.37953 1.8085 1.333 1.96653 0.0015248 0.0015248 0.0015248
15 14 777.134 1.09638 0.85925 1.16466 0.93632 0.70833 0.85861 0.49064 1.49037 0.94339 1.79087 0.0014852 0.0014852 0.0014852
16 15 843.665 1.05888 0.81842 1.15457 0.76475 0.70833 0.83217 0.52387 1.47243 0.98751 1.76234 0.0014456 0.0014456 0.0014456
17 16 909.287 1.04644 0.8167 1.14854 0.77532 0.71927 0.79227 0.50756 1.30848 1.29365 1.53777 0.001406 0.001406 0.001406
18 17 973.886 1.05134 0.8072 1.15606 0.77957 0.58971 0.67932 0.39674 1.43737 1.38893 1.62843 0.0013664 0.0013664 0.0013664
19 18 1038.19 1.01895 0.76075 1.12363 0.75585 0.75 0.79299 0.54014 1.19492 1.06695 1.43887 0.0013268 0.0013268 0.0013268
20 19 1102.59 0.99528 0.74277 1.10987 0.84396 0.70833 0.80284 0.5417 1.26264 0.86554 1.50366 0.0012872 0.0012872 0.0012872
21 20 1169.64 1.02288 0.74384 1.13908 0.92166 0.79167 0.89138 0.42992 1.82014 0.95111 2.05613 0.0012476 0.0012476 0.0012476
22 21 1235.9 0.97969 0.72155 1.10969 0.87089 0.79167 0.87398 0.49915 1.61727 0.97409 1.8339 0.001208 0.001208 0.001208
23 22 1301.63 0.96498 0.69427 1.08961 0.82713 0.59862 0.8583 0.44535 1.49811 0.94941 1.6617 0.0011684 0.0011684 0.0011684
24 23 1375.05 0.97176 0.69995 1.12122 0.98851 0.5 0.70735 0.36377 1.45452 0.90547 1.56864 0.0011288 0.0011288 0.0011288
25 24 1483 0.93529 0.66614 1.09092 1 0.78404 0.92018 0.52713 1.26578 0.81984 1.39338 0.0010892 0.0010892 0.0010892
26 25 1570.57 0.94593 0.67634 1.09923 1 0.82488 0.93674 0.51259 1.43898 0.95183 1.44066 0.0010496 0.0010496 0.0010496
27 26 1640.5 0.9005 0.64656 1.08853 0.85685 0.79167 0.87822 0.59599 1.00997 0.74692 1.25779 0.00101 0.00101 0.00101
28 27 1716.79 0.88047 0.65839 1.0819 0.98302 0.66667 0.79722 0.47007 1.44935 0.90317 1.58064 0.0009704 0.0009704 0.0009704
29 28 1790.17 0.92727 0.6491 1.07941 0.96438 0.79167 0.88789 0.51198 1.41082 0.95921 1.55489 0.0009308 0.0009308 0.0009308
30 29 1863.25 0.86808 0.61314 1.06347 1 0.73943 0.85062 0.53272 1.26679 0.77515 1.5057 0.0008912 0.0008912 0.0008912
31 30 1935.03 0.85237 0.60318 1.03757 0.86759 0.81945 0.90299 0.56004 1.32215 0.70391 1.68444 0.0008516 0.0008516 0.0008516
32 31 2006.05 0.85848 0.59069 1.04725 0.91204 0.86446 0.93329 0.61701 1.22052 0.70212 1.53785 0.000812 0.000812 0.000812
33 32 2074.85 0.8607 0.60328 1.06674 0.99448 0.83333 0.96865 0.58854 1.34406 0.70627 1.61132 0.0007724 0.0007724 0.0007724
34 33 2146.12 0.81712 0.57815 1.03472 0.94911 0.91667 0.94354 0.57918 1.23064 0.65719 1.46887 0.0007328 0.0007328 0.0007328
35 34 2215.61 0.82113 0.56747 1.03675 0.98124 0.75 0.8458 0.43565 1.65251 0.69818 1.86693 0.0006932 0.0006932 0.0006932
36 35 2283.46 0.80104 0.57305 1.02593 0.9542 0.86835 0.90976 0.48068 1.43441 0.64857 1.70736 0.0006536 0.0006536 0.0006536
37 36 2362.48 0.83103 0.57551 1.03499 0.87273 0.85758 0.9038 0.44998 1.5691 0.69063 1.97723 0.000614 0.000614 0.000614
38 37 2432.12 0.80527 0.56112 1.02551 0.80806 0.87714 0.90845 0.47423 1.51444 0.64712 1.89872 0.0005744 0.0005744 0.0005744
39 38 2508.79 0.7726 0.55171 1.01768 0.89864 0.79167 0.89273 0.51093 1.3883 0.59906 1.7624 0.0005348 0.0005348 0.0005348
40 39 2577.65 0.7675 0.53945 1.00972 0.83538 0.83333 0.91731 0.51633 1.4967 0.65808 1.87353 0.0004952 0.0004952 0.0004952
41 40 2648.26 0.76832 0.54621 1.01197 0.9061 0.83333 0.92334 0.49587 1.6318 0.72062 1.97779 0.0004556 0.0004556 0.0004556
42 41 2723.96 0.69593 0.54869 0.98187 0.95342 0.875 0.91278 0.46972 1.66482 0.70379 2.11677 0.000416 0.000416 0.000416
43 42 2803 0.71207 0.53965 0.99659 0.95001 0.83333 0.85669 0.46264 1.56204 0.65859 2.0364 0.0003764 0.0003764 0.0003764
44 43 2877.49 0.69033 0.51838 0.98369 0.95411 0.86665 0.92135 0.49602 1.5114 0.65125 1.90967 0.0003368 0.0003368 0.0003368
45 44 2955.03 0.67106 0.49273 0.97654 0.92953 0.75 0.888 0.47663 1.56378 0.62817 1.96979 0.0002972 0.0002972 0.0002972
46 45 3030.88 0.66399 0.48501 0.96783 0.86873 0.875 0.94374 0.5493 1.37376 0.62126 1.76597 0.0002576 0.0002576 0.0002576
47 46 3201.5 0.66717 0.48721 0.9668 0.87544 0.91667 0.9293 0.53737 1.33496 0.61767 1.70695 0.000218 0.000218 0.000218
48 47 3262.79 0.63017 0.47082 0.96586 0.94224 0.875 0.95991 0.50304 1.41911 0.62492 1.79879 0.0001784 0.0001784 0.0001784
49 48 3324.62 0.63678 0.47311 0.95304 0.98764 0.79167 0.90331 0.49543 1.4591 0.63498 1.87332 0.0001388 0.0001388 0.0001388
50 49 3387.48 0.63551 0.46808 0.931 0.88336 0.94704 0.93836 0.53024 1.40884 0.61606 1.80694 9.92e-05 9.92e-05 9.92e-05
51 50 3457.88 0.60539 0.45653 0.94608 0.91257 0.87003 0.90122 0.50731 1.39162 0.61616 1.78099 5.96e-05 5.96e-05 5.96e-05
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