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main ... train2

Author SHA1 Message Date
Hunter Halloran 787f5a5f23 training for JC deployment 2025-05-14 16:32:37 +00:00
Hunter Halloran 6011c5005a add weights 2025-05-12 20:24:20 +00:00
Hunter Halloran 66660c5507 training 10 weights 2025-05-12 20:16:48 +00:00
42 changed files with 373 additions and 0 deletions

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README.md Normal file
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pyproject.toml Normal file
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[project]
name = "usda-throughput-control"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
"ipykernel>=6.29.5",
"paho-mqtt>=2.1.0",
]

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task: detect
mode: train
model: yolo11s.pt
data: ./yolov11-pecan/data.yaml
epochs: 25
time: null
patience: 100
batch: 20
imgsz: 1280
save: true
save_period: -1
cache: false
device: cuda
workers: 16
project: null
name: train10
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
save_hybrid: 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: 4
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
label_smoothing: 0.0
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
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: /home/engr-ugaif/USDA-throughput-control/runs/detect/train10

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task: detect
mode: train
model: yolo11s.pt
data: ./yolov11-pecan/data.yaml
epochs: 25
time: null
patience: 100
batch: 20
imgsz: 1280
save: true
save_period: -1
cache: false
device: cuda
workers: 16
project: null
name: train12
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
save_hybrid: 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: 4
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
label_smoothing: 0.0
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
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: /home/engr-ugaif/USDA-throughput-control/runs/detect/train12

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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
1,506.754,0.69773,0.6443,0.98677,0.85746,0.90067,0.95602,0.81969,0.57211,0.44531,0.84271,0.000665995,0.000665995,0.000665995
2,775.31,0.67191,0.46142,0.96903,0.89594,0.91124,0.96492,0.83347,0.56171,0.43418,0.84033,0.00127989,0.00127989,0.00127989
3,1042.43,0.64879,0.43295,0.95724,0.89927,0.92376,0.97455,0.84793,0.53096,0.3909,0.82332,0.00184098,0.00184098,0.00184098
4,1309.29,0.63499,0.41484,0.94974,0.8901,0.90711,0.96519,0.83708,0.53559,0.41364,0.82635,0.0017624,0.0017624,0.0017624
5,1576.24,0.6194,0.39346,0.94417,0.92455,0.92984,0.97784,0.85866,0.51774,0.35537,0.81741,0.0016832,0.0016832,0.0016832
6,1843.45,0.60151,0.37385,0.93784,0.89506,0.93742,0.96631,0.83846,0.55381,0.38597,0.82555,0.001604,0.001604,0.001604
7,2110.63,0.58751,0.35579,0.93003,0.90571,0.9501,0.97622,0.86446,0.51076,0.3642,0.8146,0.0015248,0.0015248,0.0015248
8,2377.57,0.57208,0.34024,0.92507,0.90235,0.93492,0.9714,0.86352,0.50438,0.35819,0.81116,0.0014456,0.0014456,0.0014456
9,2644.65,0.55947,0.32402,0.91929,0.91572,0.94436,0.97664,0.87084,0.49431,0.34051,0.80756,0.0013664,0.0013664,0.0013664
10,2911.65,0.54281,0.31078,0.91366,0.91424,0.93562,0.9652,0.86171,0.4887,0.38716,0.80484,0.0012872,0.0012872,0.0012872
11,3178.42,0.52475,0.29805,0.90675,0.90982,0.9442,0.96658,0.86221,0.49884,0.37786,0.80951,0.001208,0.001208,0.001208
12,3445.37,0.50654,0.28378,0.90022,0.91695,0.94206,0.96273,0.85841,0.50642,0.37648,0.81476,0.0011288,0.0011288,0.0011288
13,3712.44,0.48918,0.27473,0.89372,0.91413,0.9375,0.961,0.85692,0.51257,0.38685,0.81386,0.0010496,0.0010496,0.0010496
14,3979.6,0.47069,0.26422,0.88725,0.91369,0.94444,0.96014,0.85136,0.51508,0.37853,0.81735,0.0009704,0.0009704,0.0009704
15,4246.8,0.45515,0.25584,0.88293,0.89485,0.94321,0.94822,0.84115,0.51803,0.40433,0.81875,0.0008912,0.0008912,0.0008912
16,4509.67,0.40004,0.21119,0.8626,0.91392,0.93884,0.94297,0.83674,0.5208,0.42094,0.8231,0.000812,0.000812,0.000812
17,4771.34,0.37703,0.19988,0.85195,0.90937,0.94359,0.94351,0.83788,0.52937,0.41134,0.82689,0.0007328,0.0007328,0.0007328
18,5032.84,0.35892,0.19194,0.84558,0.90822,0.93263,0.93177,0.82919,0.52888,0.46293,0.8317,0.0006536,0.0006536,0.0006536
19,5294.47,0.34436,0.18312,0.84155,0.91178,0.93976,0.9361,0.83365,0.52819,0.43623,0.83142,0.0005744,0.0005744,0.0005744
20,5556.06,0.3276,0.17687,0.83577,0.9108,0.9393,0.9371,0.83403,0.52633,0.4396,0.83187,0.0004952,0.0004952,0.0004952
21,5817.69,0.31108,0.16883,0.83093,0.91184,0.93631,0.93389,0.83055,0.52741,0.45089,0.83429,0.000416,0.000416,0.000416
22,6079.32,0.29764,0.1618,0.8259,0.91338,0.93828,0.93298,0.83033,0.52668,0.4425,0.83452,0.0003368,0.0003368,0.0003368
23,6340.95,0.28511,0.15486,0.82229,0.91827,0.93003,0.9306,0.82773,0.52781,0.47135,0.8384,0.0002576,0.0002576,0.0002576
24,6602.43,0.27296,0.14872,0.81929,0.91692,0.92886,0.93197,0.82858,0.52829,0.48239,0.83885,0.0001784,0.0001784,0.0001784
25,6864.01,0.26224,0.14304,0.81588,0.91716,0.93172,0.93469,0.83199,0.52728,0.48326,0.84022,9.92e-05,9.92e-05,9.92e-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 1 506.754 0.69773 0.6443 0.98677 0.85746 0.90067 0.95602 0.81969 0.57211 0.44531 0.84271 0.000665995 0.000665995 0.000665995
3 2 775.31 0.67191 0.46142 0.96903 0.89594 0.91124 0.96492 0.83347 0.56171 0.43418 0.84033 0.00127989 0.00127989 0.00127989
4 3 1042.43 0.64879 0.43295 0.95724 0.89927 0.92376 0.97455 0.84793 0.53096 0.3909 0.82332 0.00184098 0.00184098 0.00184098
5 4 1309.29 0.63499 0.41484 0.94974 0.8901 0.90711 0.96519 0.83708 0.53559 0.41364 0.82635 0.0017624 0.0017624 0.0017624
6 5 1576.24 0.6194 0.39346 0.94417 0.92455 0.92984 0.97784 0.85866 0.51774 0.35537 0.81741 0.0016832 0.0016832 0.0016832
7 6 1843.45 0.60151 0.37385 0.93784 0.89506 0.93742 0.96631 0.83846 0.55381 0.38597 0.82555 0.001604 0.001604 0.001604
8 7 2110.63 0.58751 0.35579 0.93003 0.90571 0.9501 0.97622 0.86446 0.51076 0.3642 0.8146 0.0015248 0.0015248 0.0015248
9 8 2377.57 0.57208 0.34024 0.92507 0.90235 0.93492 0.9714 0.86352 0.50438 0.35819 0.81116 0.0014456 0.0014456 0.0014456
10 9 2644.65 0.55947 0.32402 0.91929 0.91572 0.94436 0.97664 0.87084 0.49431 0.34051 0.80756 0.0013664 0.0013664 0.0013664
11 10 2911.65 0.54281 0.31078 0.91366 0.91424 0.93562 0.9652 0.86171 0.4887 0.38716 0.80484 0.0012872 0.0012872 0.0012872
12 11 3178.42 0.52475 0.29805 0.90675 0.90982 0.9442 0.96658 0.86221 0.49884 0.37786 0.80951 0.001208 0.001208 0.001208
13 12 3445.37 0.50654 0.28378 0.90022 0.91695 0.94206 0.96273 0.85841 0.50642 0.37648 0.81476 0.0011288 0.0011288 0.0011288
14 13 3712.44 0.48918 0.27473 0.89372 0.91413 0.9375 0.961 0.85692 0.51257 0.38685 0.81386 0.0010496 0.0010496 0.0010496
15 14 3979.6 0.47069 0.26422 0.88725 0.91369 0.94444 0.96014 0.85136 0.51508 0.37853 0.81735 0.0009704 0.0009704 0.0009704
16 15 4246.8 0.45515 0.25584 0.88293 0.89485 0.94321 0.94822 0.84115 0.51803 0.40433 0.81875 0.0008912 0.0008912 0.0008912
17 16 4509.67 0.40004 0.21119 0.8626 0.91392 0.93884 0.94297 0.83674 0.5208 0.42094 0.8231 0.000812 0.000812 0.000812
18 17 4771.34 0.37703 0.19988 0.85195 0.90937 0.94359 0.94351 0.83788 0.52937 0.41134 0.82689 0.0007328 0.0007328 0.0007328
19 18 5032.84 0.35892 0.19194 0.84558 0.90822 0.93263 0.93177 0.82919 0.52888 0.46293 0.8317 0.0006536 0.0006536 0.0006536
20 19 5294.47 0.34436 0.18312 0.84155 0.91178 0.93976 0.9361 0.83365 0.52819 0.43623 0.83142 0.0005744 0.0005744 0.0005744
21 20 5556.06 0.3276 0.17687 0.83577 0.9108 0.9393 0.9371 0.83403 0.52633 0.4396 0.83187 0.0004952 0.0004952 0.0004952
22 21 5817.69 0.31108 0.16883 0.83093 0.91184 0.93631 0.93389 0.83055 0.52741 0.45089 0.83429 0.000416 0.000416 0.000416
23 22 6079.32 0.29764 0.1618 0.8259 0.91338 0.93828 0.93298 0.83033 0.52668 0.4425 0.83452 0.0003368 0.0003368 0.0003368
24 23 6340.95 0.28511 0.15486 0.82229 0.91827 0.93003 0.9306 0.82773 0.52781 0.47135 0.8384 0.0002576 0.0002576 0.0002576
25 24 6602.43 0.27296 0.14872 0.81929 0.91692 0.92886 0.93197 0.82858 0.52829 0.48239 0.83885 0.0001784 0.0001784 0.0001784
26 25 6864.01 0.26224 0.14304 0.81588 0.91716 0.93172 0.93469 0.83199 0.52728 0.48326 0.84022 9.92e-05 9.92e-05 9.92e-05

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yolo11n.pt Normal file

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yolo11s.pt Normal file

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task: detect
mode: train
model: yolo11x.pt
data: dataset_pecan.yaml
epochs: 300
time: null
patience: 100
batch: 8
imgsz: 640
save: true
save_period: -1
cache: false
device: mps
workers: 0
project: null
name: train8
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
save_hybrid: 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: 4
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
label_smoothing: 0.0
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
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs/detect/train8

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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
1,1025.83,0.66603,0.70931,1.00467,0.79126,0.62088,0.63578,0.55598,0.53711,1.12546,0.61646,0.0033218,0.0033218,0.0033218
2,2234.46,0.67875,0.56064,0.97946,0.73778,0.22965,0.24951,0.20692,0.70498,1.24004,0.63871,0.00663317,0.00663317,0.00663317
3,3567.29,0.74777,0.60717,1.01042,0.78896,0.28977,0.31317,0.25897,0.68661,1.33028,0.66244,0.00992254,0.00992254,0.00992254
4,4924.91,0.75061,0.5652,1.02254,0.81885,0.49061,0.52847,0.44662,0.60725,1.0302,0.63908,0.009901,0.009901,0.009901
5,6311.84,0.71113,0.53651,1.01557,0.797,0.85082,0.87202,0.72871,0.61414,1.01847,0.63901,0.009868,0.009868,0.009868
6,7752.74,0.69393,0.50972,1.00952,0.83906,0.84134,0.89616,0.7712,0.55075,0.96689,0.61902,0.009835,0.009835,0.009835
7,10111.6,0.67585,0.49999,1.00538,0.85073,0.31173,0.35275,0.30764,0.5542,1.94607,0.62021,0.009802,0.009802,0.009802
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 1025.83 0.66603 0.70931 1.00467 0.79126 0.62088 0.63578 0.55598 0.53711 1.12546 0.61646 0.0033218 0.0033218 0.0033218
3 2 2234.46 0.67875 0.56064 0.97946 0.73778 0.22965 0.24951 0.20692 0.70498 1.24004 0.63871 0.00663317 0.00663317 0.00663317
4 3 3567.29 0.74777 0.60717 1.01042 0.78896 0.28977 0.31317 0.25897 0.68661 1.33028 0.66244 0.00992254 0.00992254 0.00992254
5 4 4924.91 0.75061 0.5652 1.02254 0.81885 0.49061 0.52847 0.44662 0.60725 1.0302 0.63908 0.009901 0.009901 0.009901
6 5 6311.84 0.71113 0.53651 1.01557 0.797 0.85082 0.87202 0.72871 0.61414 1.01847 0.63901 0.009868 0.009868 0.009868
7 6 7752.74 0.69393 0.50972 1.00952 0.83906 0.84134 0.89616 0.7712 0.55075 0.96689 0.61902 0.009835 0.009835 0.009835
8 7 10111.6 0.67585 0.49999 1.00538 0.85073 0.31173 0.35275 0.30764 0.5542 1.94607 0.62021 0.009802 0.009802 0.009802

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yolov11-pecan/train.py Normal file
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from ultralytics import YOLO
import torch
device = torch.device('cuda')
# device = 'cpu'
model = YOLO('yolo11s.pt')
model.train(data='./yolov11-pecan/data.yaml',device=device,batch=20,epochs=25,workers=16, imgsz = 1280)