feat(Isolators-Detection): 添加训练和验证集标签

- 在 train/labels 和 valid/labels目录下添加了多个标签文件
- 标签文件包含物体检测的坐标信息
- 此次添加的标签主要用于绝缘子检测任务
This commit is contained in:
fly6516 2025-03-25 10:26:19 +08:00
commit c33f35d0de
541 changed files with 6047 additions and 0 deletions

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# isolators > 2024-06-17 7:53am
https://universe.roboflow.com/flarndan/isolators-abzgn
Provided by a Roboflow user
License: CC BY 4.0

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isolators - v1 2024-06-17 7:53am
==============================
This dataset was exported via roboflow.com on March 24, 2025 at 2:03 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 130 images.
Isolators are annotated in YOLOv12 format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x480 (Stretch)
No image augmentation techniques were applied.

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train: ../train/images
val: ../valid/images
test: ../test/images
nc: 1
names: ['isolators']
roboflow:
workspace: flarndan
project: isolators-abzgn
version: 1
license: CC BY 4.0
url: https://universe.roboflow.com/flarndan/isolators-abzgn/dataset/1

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task: detect
mode: train
model: yolo12x.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: 0
workers: 8
project: null
name: cable_detection
exist_ok: false
pretrained: true
optimizer: auto
verbose: true
seed: 0
deterministic: true
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rect: false
cos_lr: false
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amp: true
fraction: 1.0
profile: false
freeze: null
multi_scale: false
overlap_mask: true
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dropout: 0.0
val: true
split: val
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save_hybrid: false
conf: null
iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
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vid_stride: 1
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format: torchscript
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int8: false
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simplify: true
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erasing: 0.4
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs\detect\cable_detection

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task: detect
mode: train
model: yolo12x.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
save: true
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iou: 0.7
max_det: 300
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plots: true
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show_labels: true
show_conf: true
show_boxes: true
line_width: null
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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
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs\detect\cable_detection10

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task: detect
mode: train
model: yolo12x.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: 0
workers: 8
project: null
name: cable_detection11
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single_cls: false
rect: false
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close_mosaic: 10
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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
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visualize: false
augment: true
agnostic_nms: false
classes: null
retina_masks: false
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show_labels: true
show_conf: true
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pose: 12.0
kobj: 1.0
nbs: 64
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erasing: 0.4
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs\detect\cable_detection11

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task: detect
mode: train
model: yolo12x.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
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nbs: 64
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hsv_s: 0.7
hsv_v: 0.4
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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\cable_detection12

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task: detect
mode: train
model: yolo12x.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
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workers: 8
project: null
name: cable_detection13
exist_ok: false
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amp: true
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iou: 0.7
max_det: 300
half: false
dnn: false
plots: true
source: null
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format: torchscript
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int8: false
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simplify: true
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kobj: 1.0
nbs: 64
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hsv_s: 0.7
hsv_v: 0.4
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translate: 0.1
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perspective: 0.0
flipud: 0.0
fliplr: 0.5
bgr: 0.0
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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\cable_detection13

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task: detect
mode: train
model: yolo12x.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: 0
workers: 8
project: null
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amp: true
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iou: 0.7
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copy_paste_mode: flip
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erasing: 0.4
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs\detect\cable_detection14

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task: detect
mode: train
model: yolo12x.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
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tracker: botsort.yaml
save_dir: runs\detect\cable_detection15

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task: detect
mode: train
model: yolo12x.yaml
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time: null
patience: 100
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task: detect
mode: train
model: yolo12x.yaml
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time: null
patience: 100
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save_period: -1
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amp: true
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split: val
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iou: 0.7
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dnn: false
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format: torchscript
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weight_decay: 0.0005
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box: 7.5
cls: 0.5
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pose: 12.0
kobj: 1.0
nbs: 64
hsv_h: 0.015
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hsv_v: 0.4
degrees: 0.0
translate: 0.1
scale: 0.5
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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\cable_detection17

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task: detect
mode: train
model: yolo12x.yaml
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time: null
patience: 100
batch: 8
imgsz: 640
save: true
save_period: -1
cache: false
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workers: 0
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name: cable_detection18
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pretrained: true
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verbose: true
seed: 0
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single_cls: false
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resume: false
amp: true
fraction: 1.0
profile: false
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overlap_mask: true
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split: val
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iou: 0.7
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source: null
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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
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs\detect\cable_detection27

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task: detect
mode: train
model: yolo12n.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 4
imgsz: 640
save: true
save_period: -1
cache: false
device: 0
workers: 2
project: null
name: cable_detection28
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: true
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
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs\detect\cable_detection28

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task: detect
mode: train
model: yolo12n.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 4
imgsz: 640
save: true
save_period: -1
cache: false
device: 0
workers: 2
project: null
name: cable_detection29
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: true
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
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs\detect\cable_detection29

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task: detect
mode: train
model: yolo12x.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 16
imgsz: 640
save: true
save_period: -1
cache: false
device: 0
workers: 8
project: null
name: cable_detection3
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: true
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
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs\detect\cable_detection3

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task: detect
mode: train
model: yolo12n.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 4
imgsz: 640
save: true
save_period: -1
cache: false
device: 0
workers: 0
project: null
name: cable_detection30
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: true
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
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@ -0,0 +1,51 @@
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
1,7.55108,3.86718,4.03491,4.2951,0.00227,0.375,0.00974,0.00578,3.8812,3.99622,4.15869,0.00056,0.00056,0.00056
2,13.8479,3.849,3.95573,4.21698,0.00233,0.375,0.00552,0.00287,3.88792,3.95583,4.14719,0.00111743,0.00111743,0.00111743
3,20.0392,3.82315,3.89682,4.14241,0.00226,0.375,0.00465,0.00231,3.86413,3.98223,4.1467,0.00165189,0.00165189,0.00165189
4,25.8265,3.88628,3.91017,4.09585,0.00232,0.375,0.00581,0.00241,3.7991,4.02746,4.15074,0.0018812,0.0018812,0.0018812
5,32.2414,3.81224,3.74397,4.02627,0.00229,0.375,0.00965,0.00302,3.85,3.98862,4.04111,0.0018416,0.0018416,0.0018416
6,38.4267,3.84657,3.71527,3.91758,0.00216,0.375,0.03558,0.01089,3.65209,3.81254,4.01897,0.001802,0.001802,0.001802
7,44.3504,3.45534,3.61028,3.87833,0.00286,0.5,0.00559,0.00187,3.49262,3.74319,3.79219,0.0017624,0.0017624,0.0017624
8,50.3002,3.41512,3.64168,3.66016,0.00286,0.5,0.0053,0.00159,3.51966,3.93021,4.9429,0.0017228,0.0017228,0.0017228
9,56.4227,3.43039,3.5065,3.56448,0.00357,0.625,0.00426,0.00128,3.47188,3.97152,3.90089,0.0016832,0.0016832,0.0016832
10,62.4968,3.2681,3.48006,3.49726,0.00214,0.375,0.00217,0.00069,3.29854,4.34628,3.6408,0.0016436,0.0016436,0.0016436
11,68.7422,3.24858,3.47816,3.42633,0.03068,0.04167,0.00647,0.00131,3.21512,4.40411,3.71788,0.001604,0.001604,0.001604
12,75.0563,3.0272,3.49522,3.34452,0.00357,0.625,0.00563,0.00107,3.15748,4.16911,3.89962,0.0015644,0.0015644,0.0015644
13,81.5374,3.10525,3.44024,3.30269,0.00405,0.70833,0.00558,0.00151,3.03737,4.12775,3.68458,0.0015248,0.0015248,0.0015248
14,88.0714,3.05656,3.33744,3.22003,0.09945,0.04167,0.0116,0.00189,3.10771,4.80671,3.51149,0.0014852,0.0014852,0.0014852
15,94.1096,3.08564,3.37526,3.1946,0.02845,0.04167,0.00762,0.00147,3.13182,4.63245,3.47293,0.0014456,0.0014456,0.0014456
16,100.168,2.96126,3.17122,3.04857,0.00378,0.08333,0.00537,0.00125,3.04,4.223,3.31258,0.001406,0.001406,0.001406
17,106.14,2.93419,3.26308,3.06405,0.00381,0.625,0.00477,0.00135,2.94965,4.15937,3.24958,0.0013664,0.0013664,0.0013664
18,112.151,2.86612,3.28121,3.03611,0.00361,0.625,0.00526,0.00138,2.90519,4.03629,3.18737,0.0013268,0.0013268,0.0013268
19,118.261,2.96207,3.11173,2.92585,0.00381,0.66667,0.00704,0.00183,2.94075,3.83422,3.18178,0.0012872,0.0012872,0.0012872
20,124.292,2.92356,3.08375,2.88589,0.00429,0.75,0.0088,0.00221,2.93886,3.85541,3.18366,0.0012476,0.0012476,0.0012476
21,130.617,2.79487,3.20655,2.96298,0.00333,0.58333,0.0065,0.00201,2.96157,3.91003,3.18291,0.001208,0.001208,0.001208
22,136.541,2.82011,3.11523,2.92622,0.00874,0.41667,0.00706,0.00223,3.00108,3.7678,3.24255,0.0011684,0.0011684,0.0011684
23,142.691,2.83503,3.09436,2.86661,0.00429,0.75,0.00905,0.00263,2.97787,3.4654,3.2307,0.0011288,0.0011288,0.0011288
24,148.828,2.91659,3.10609,2.87657,0.00381,0.66667,0.01044,0.00277,3.02385,3.5523,3.12061,0.0010892,0.0010892,0.0010892
25,154.767,2.7855,3.12779,2.8576,0.00939,0.54167,0.01572,0.00384,3.03516,3.65973,3.08936,0.0010496,0.0010496,0.0010496
26,160.505,2.82788,3.13899,2.87672,0.01879,0.25,0.01416,0.00363,2.99057,3.79445,3.03352,0.00101,0.00101,0.00101
27,166.311,2.80575,3.04321,2.83544,0.00462,0.79167,0.01905,0.00477,2.94286,3.44584,3.04008,0.0009704,0.0009704,0.0009704
28,172.282,2.81548,3.01366,2.79826,0.019,0.54167,0.0164,0.00394,2.93703,3.58425,3.01829,0.0009308,0.0009308,0.0009308
29,178.154,2.84591,3.10678,2.7572,0.00633,0.75,0.01348,0.00352,3.0056,3.57983,3.02221,0.0008912,0.0008912,0.0008912
30,183.997,2.8423,3.07794,2.87758,0.01975,0.08333,0.01592,0.0049,2.99829,3.54598,3.00152,0.0008516,0.0008516,0.0008516
31,189.736,2.68857,3.00799,2.77236,0.04058,0.0602,0.01862,0.0061,2.99313,3.54737,2.98195,0.000812,0.000812,0.000812
32,195.482,2.69466,2.98715,2.769,0.07122,0.04167,0.03248,0.01245,3.03598,3.63531,2.97361,0.0007724,0.0007724,0.0007724
33,201.833,2.69134,2.85102,2.6358,0.0602,0.04167,0.01602,0.00576,3.04381,3.67514,3.01228,0.0007328,0.0007328,0.0007328
34,208.057,2.78069,2.88744,2.72012,0.07983,0.08333,0.03027,0.00669,3.01735,3.70257,3.00679,0.0006932,0.0006932,0.0006932
35,214.707,2.79637,2.94962,2.78716,0.06493,0.08333,0.02726,0.0067,2.9504,3.4496,2.98057,0.0006536,0.0006536,0.0006536
36,221.142,2.75393,2.97841,2.71883,0.04742,0.08333,0.02458,0.00715,2.92524,3.3971,2.98439,0.000614,0.000614,0.000614
37,227.618,2.75194,2.98667,2.72664,0.13687,0.04167,0.06811,0.02311,2.902,3.3592,2.96795,0.0005744,0.0005744,0.0005744
38,233.743,2.69401,2.92402,2.68427,0.13445,0.08333,0.04445,0.0109,2.87136,3.30877,2.99419,0.0005348,0.0005348,0.0005348
39,239.867,2.68986,2.95112,2.68211,0.15856,0.08333,0.04748,0.01168,2.90474,3.32216,2.99818,0.0004952,0.0004952,0.0004952
40,246.058,2.6644,2.87815,2.72102,0.06495,0.08333,0.03027,0.00752,2.96901,3.38199,2.9842,0.0004556,0.0004556,0.0004556
41,252.039,2.56502,3.09199,2.58815,0.06074,0.09994,0.0333,0.00712,2.98147,3.5289,2.97084,0.000416,0.000416,0.000416
42,258.047,2.56732,3.11408,2.70098,0.41082,0.04167,0.06154,0.01104,2.99519,3.58982,2.98807,0.0003764,0.0003764,0.0003764
43,264.043,2.54064,3.06575,2.61591,0.45473,0.04167,0.0654,0.01038,2.96907,3.49957,2.98158,0.0003368,0.0003368,0.0003368
44,270.064,2.50966,2.99116,2.54798,0.73561,0.04167,0.06495,0.00989,2.98304,3.46385,2.98988,0.0002972,0.0002972,0.0002972
45,276.189,2.58969,3.13199,2.71361,0.77585,0.04167,0.06961,0.01214,2.97349,3.36213,2.98217,0.0002576,0.0002576,0.0002576
46,282.345,2.48391,2.97711,2.63014,0.1787,0.08333,0.07374,0.01277,2.96075,3.34562,2.96329,0.000218,0.000218,0.000218
47,288.258,2.48827,2.99982,2.60596,0.09865,0.08333,0.07892,0.01395,2.95352,3.33109,2.96578,0.0001784,0.0001784,0.0001784
48,294.33,2.46829,3.06381,2.60389,0.07403,0.08333,0.07883,0.01353,2.93099,3.30325,2.92394,0.0001388,0.0001388,0.0001388
49,300.335,2.4932,3.02598,2.58753,0.26274,0.08333,0.06295,0.01324,2.92988,3.25111,2.94379,9.92e-05,9.92e-05,9.92e-05
50,306.454,2.50308,3.09065,2.66328,0.3687,0.08333,0.08813,0.01945,2.91605,3.20766,2.95926,5.96e-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 7.55108 3.86718 4.03491 4.2951 0.00227 0.375 0.00974 0.00578 3.8812 3.99622 4.15869 0.00056 0.00056 0.00056
3 2 13.8479 3.849 3.95573 4.21698 0.00233 0.375 0.00552 0.00287 3.88792 3.95583 4.14719 0.00111743 0.00111743 0.00111743
4 3 20.0392 3.82315 3.89682 4.14241 0.00226 0.375 0.00465 0.00231 3.86413 3.98223 4.1467 0.00165189 0.00165189 0.00165189
5 4 25.8265 3.88628 3.91017 4.09585 0.00232 0.375 0.00581 0.00241 3.7991 4.02746 4.15074 0.0018812 0.0018812 0.0018812
6 5 32.2414 3.81224 3.74397 4.02627 0.00229 0.375 0.00965 0.00302 3.85 3.98862 4.04111 0.0018416 0.0018416 0.0018416
7 6 38.4267 3.84657 3.71527 3.91758 0.00216 0.375 0.03558 0.01089 3.65209 3.81254 4.01897 0.001802 0.001802 0.001802
8 7 44.3504 3.45534 3.61028 3.87833 0.00286 0.5 0.00559 0.00187 3.49262 3.74319 3.79219 0.0017624 0.0017624 0.0017624
9 8 50.3002 3.41512 3.64168 3.66016 0.00286 0.5 0.0053 0.00159 3.51966 3.93021 4.9429 0.0017228 0.0017228 0.0017228
10 9 56.4227 3.43039 3.5065 3.56448 0.00357 0.625 0.00426 0.00128 3.47188 3.97152 3.90089 0.0016832 0.0016832 0.0016832
11 10 62.4968 3.2681 3.48006 3.49726 0.00214 0.375 0.00217 0.00069 3.29854 4.34628 3.6408 0.0016436 0.0016436 0.0016436
12 11 68.7422 3.24858 3.47816 3.42633 0.03068 0.04167 0.00647 0.00131 3.21512 4.40411 3.71788 0.001604 0.001604 0.001604
13 12 75.0563 3.0272 3.49522 3.34452 0.00357 0.625 0.00563 0.00107 3.15748 4.16911 3.89962 0.0015644 0.0015644 0.0015644
14 13 81.5374 3.10525 3.44024 3.30269 0.00405 0.70833 0.00558 0.00151 3.03737 4.12775 3.68458 0.0015248 0.0015248 0.0015248
15 14 88.0714 3.05656 3.33744 3.22003 0.09945 0.04167 0.0116 0.00189 3.10771 4.80671 3.51149 0.0014852 0.0014852 0.0014852
16 15 94.1096 3.08564 3.37526 3.1946 0.02845 0.04167 0.00762 0.00147 3.13182 4.63245 3.47293 0.0014456 0.0014456 0.0014456
17 16 100.168 2.96126 3.17122 3.04857 0.00378 0.08333 0.00537 0.00125 3.04 4.223 3.31258 0.001406 0.001406 0.001406
18 17 106.14 2.93419 3.26308 3.06405 0.00381 0.625 0.00477 0.00135 2.94965 4.15937 3.24958 0.0013664 0.0013664 0.0013664
19 18 112.151 2.86612 3.28121 3.03611 0.00361 0.625 0.00526 0.00138 2.90519 4.03629 3.18737 0.0013268 0.0013268 0.0013268
20 19 118.261 2.96207 3.11173 2.92585 0.00381 0.66667 0.00704 0.00183 2.94075 3.83422 3.18178 0.0012872 0.0012872 0.0012872
21 20 124.292 2.92356 3.08375 2.88589 0.00429 0.75 0.0088 0.00221 2.93886 3.85541 3.18366 0.0012476 0.0012476 0.0012476
22 21 130.617 2.79487 3.20655 2.96298 0.00333 0.58333 0.0065 0.00201 2.96157 3.91003 3.18291 0.001208 0.001208 0.001208
23 22 136.541 2.82011 3.11523 2.92622 0.00874 0.41667 0.00706 0.00223 3.00108 3.7678 3.24255 0.0011684 0.0011684 0.0011684
24 23 142.691 2.83503 3.09436 2.86661 0.00429 0.75 0.00905 0.00263 2.97787 3.4654 3.2307 0.0011288 0.0011288 0.0011288
25 24 148.828 2.91659 3.10609 2.87657 0.00381 0.66667 0.01044 0.00277 3.02385 3.5523 3.12061 0.0010892 0.0010892 0.0010892
26 25 154.767 2.7855 3.12779 2.8576 0.00939 0.54167 0.01572 0.00384 3.03516 3.65973 3.08936 0.0010496 0.0010496 0.0010496
27 26 160.505 2.82788 3.13899 2.87672 0.01879 0.25 0.01416 0.00363 2.99057 3.79445 3.03352 0.00101 0.00101 0.00101
28 27 166.311 2.80575 3.04321 2.83544 0.00462 0.79167 0.01905 0.00477 2.94286 3.44584 3.04008 0.0009704 0.0009704 0.0009704
29 28 172.282 2.81548 3.01366 2.79826 0.019 0.54167 0.0164 0.00394 2.93703 3.58425 3.01829 0.0009308 0.0009308 0.0009308
30 29 178.154 2.84591 3.10678 2.7572 0.00633 0.75 0.01348 0.00352 3.0056 3.57983 3.02221 0.0008912 0.0008912 0.0008912
31 30 183.997 2.8423 3.07794 2.87758 0.01975 0.08333 0.01592 0.0049 2.99829 3.54598 3.00152 0.0008516 0.0008516 0.0008516
32 31 189.736 2.68857 3.00799 2.77236 0.04058 0.0602 0.01862 0.0061 2.99313 3.54737 2.98195 0.000812 0.000812 0.000812
33 32 195.482 2.69466 2.98715 2.769 0.07122 0.04167 0.03248 0.01245 3.03598 3.63531 2.97361 0.0007724 0.0007724 0.0007724
34 33 201.833 2.69134 2.85102 2.6358 0.0602 0.04167 0.01602 0.00576 3.04381 3.67514 3.01228 0.0007328 0.0007328 0.0007328
35 34 208.057 2.78069 2.88744 2.72012 0.07983 0.08333 0.03027 0.00669 3.01735 3.70257 3.00679 0.0006932 0.0006932 0.0006932
36 35 214.707 2.79637 2.94962 2.78716 0.06493 0.08333 0.02726 0.0067 2.9504 3.4496 2.98057 0.0006536 0.0006536 0.0006536
37 36 221.142 2.75393 2.97841 2.71883 0.04742 0.08333 0.02458 0.00715 2.92524 3.3971 2.98439 0.000614 0.000614 0.000614
38 37 227.618 2.75194 2.98667 2.72664 0.13687 0.04167 0.06811 0.02311 2.902 3.3592 2.96795 0.0005744 0.0005744 0.0005744
39 38 233.743 2.69401 2.92402 2.68427 0.13445 0.08333 0.04445 0.0109 2.87136 3.30877 2.99419 0.0005348 0.0005348 0.0005348
40 39 239.867 2.68986 2.95112 2.68211 0.15856 0.08333 0.04748 0.01168 2.90474 3.32216 2.99818 0.0004952 0.0004952 0.0004952
41 40 246.058 2.6644 2.87815 2.72102 0.06495 0.08333 0.03027 0.00752 2.96901 3.38199 2.9842 0.0004556 0.0004556 0.0004556
42 41 252.039 2.56502 3.09199 2.58815 0.06074 0.09994 0.0333 0.00712 2.98147 3.5289 2.97084 0.000416 0.000416 0.000416
43 42 258.047 2.56732 3.11408 2.70098 0.41082 0.04167 0.06154 0.01104 2.99519 3.58982 2.98807 0.0003764 0.0003764 0.0003764
44 43 264.043 2.54064 3.06575 2.61591 0.45473 0.04167 0.0654 0.01038 2.96907 3.49957 2.98158 0.0003368 0.0003368 0.0003368
45 44 270.064 2.50966 2.99116 2.54798 0.73561 0.04167 0.06495 0.00989 2.98304 3.46385 2.98988 0.0002972 0.0002972 0.0002972
46 45 276.189 2.58969 3.13199 2.71361 0.77585 0.04167 0.06961 0.01214 2.97349 3.36213 2.98217 0.0002576 0.0002576 0.0002576
47 46 282.345 2.48391 2.97711 2.63014 0.1787 0.08333 0.07374 0.01277 2.96075 3.34562 2.96329 0.000218 0.000218 0.000218
48 47 288.258 2.48827 2.99982 2.60596 0.09865 0.08333 0.07892 0.01395 2.95352 3.33109 2.96578 0.0001784 0.0001784 0.0001784
49 48 294.33 2.46829 3.06381 2.60389 0.07403 0.08333 0.07883 0.01353 2.93099 3.30325 2.92394 0.0001388 0.0001388 0.0001388
50 49 300.335 2.4932 3.02598 2.58753 0.26274 0.08333 0.06295 0.01324 2.92988 3.25111 2.94379 9.92e-05 9.92e-05 9.92e-05
51 50 306.454 2.50308 3.09065 2.66328 0.3687 0.08333 0.08813 0.01945 2.91605 3.20766 2.95926 5.96e-05 5.96e-05 5.96e-05

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@ -0,0 +1,106 @@
task: detect
mode: train
model: yolo12n.yaml
data: data.yaml
epochs: 50
time: null
patience: 100
batch: 4
imgsz: 640
save: true
save_period: -1
cache: false
device: 0
workers: 0
project: null
name: cable_detection31
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: true
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
crop_fraction: 1.0
cfg: null
tracker: botsort.yaml
save_dir: runs\detect\cable_detection31

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@ -0,0 +1,51 @@
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
1,7.58248,3.86718,4.03491,4.2951,0.00227,0.375,0.00974,0.00578,3.8812,3.99622,4.15869,0.00056,0.00056,0.00056
2,13.9887,3.849,3.95573,4.21698,0.00233,0.375,0.00552,0.00287,3.88792,3.95583,4.14719,0.00111743,0.00111743,0.00111743
3,20.1963,3.82315,3.89682,4.14241,0.00226,0.375,0.00465,0.00231,3.86413,3.98223,4.1467,0.00165189,0.00165189,0.00165189
4,25.9975,3.88628,3.91017,4.09585,0.00232,0.375,0.00581,0.00241,3.7991,4.02746,4.15074,0.0018812,0.0018812,0.0018812
5,32.3808,3.81224,3.74397,4.02627,0.00229,0.375,0.00965,0.00302,3.85,3.98862,4.04111,0.0018416,0.0018416,0.0018416
6,38.5823,3.84657,3.71527,3.91758,0.00216,0.375,0.03558,0.01089,3.65209,3.81254,4.01897,0.001802,0.001802,0.001802
7,44.5661,3.45534,3.61028,3.87833,0.00286,0.5,0.00559,0.00187,3.49262,3.74319,3.79219,0.0017624,0.0017624,0.0017624
8,50.5986,3.41512,3.64168,3.66016,0.00286,0.5,0.0053,0.00159,3.51966,3.93021,4.9429,0.0017228,0.0017228,0.0017228
9,56.7493,3.43039,3.5065,3.56448,0.00357,0.625,0.00426,0.00128,3.47188,3.97152,3.90089,0.0016832,0.0016832,0.0016832
10,62.8087,3.2681,3.48006,3.49726,0.00214,0.375,0.00217,0.00069,3.29854,4.34628,3.6408,0.0016436,0.0016436,0.0016436
11,68.9646,3.24858,3.47816,3.42633,0.03068,0.04167,0.00647,0.00131,3.21512,4.40411,3.71788,0.001604,0.001604,0.001604
12,75.1987,3.0272,3.49522,3.34452,0.00357,0.625,0.00563,0.00107,3.15748,4.16911,3.89962,0.0015644,0.0015644,0.0015644
13,81.6663,3.10525,3.44024,3.30269,0.00405,0.70833,0.00558,0.00151,3.03737,4.12775,3.68458,0.0015248,0.0015248,0.0015248
14,88.0817,3.05656,3.33744,3.22003,0.09945,0.04167,0.0116,0.00189,3.10771,4.80671,3.51149,0.0014852,0.0014852,0.0014852
15,94.3324,3.08564,3.37526,3.1946,0.02845,0.04167,0.00762,0.00147,3.13182,4.63245,3.47293,0.0014456,0.0014456,0.0014456
16,100.402,2.96126,3.17122,3.04857,0.00378,0.08333,0.00537,0.00125,3.04,4.223,3.31258,0.001406,0.001406,0.001406
17,106.273,2.93419,3.26308,3.06405,0.00381,0.625,0.00477,0.00135,2.94965,4.15937,3.24958,0.0013664,0.0013664,0.0013664
18,112.262,2.86612,3.28121,3.03611,0.00361,0.625,0.00526,0.00138,2.90519,4.03629,3.18737,0.0013268,0.0013268,0.0013268
19,118.347,2.96207,3.11173,2.92585,0.00381,0.66667,0.00704,0.00183,2.94075,3.83422,3.18178,0.0012872,0.0012872,0.0012872
20,124.434,2.92356,3.08375,2.88589,0.00429,0.75,0.0088,0.00221,2.93886,3.85541,3.18366,0.0012476,0.0012476,0.0012476
21,130.742,2.79487,3.20655,2.96298,0.00333,0.58333,0.0065,0.00201,2.96157,3.91003,3.18291,0.001208,0.001208,0.001208
22,136.633,2.82011,3.11523,2.92622,0.00874,0.41667,0.00706,0.00223,3.00108,3.7678,3.24255,0.0011684,0.0011684,0.0011684
23,142.815,2.83503,3.09436,2.86661,0.00429,0.75,0.00905,0.00263,2.97787,3.4654,3.2307,0.0011288,0.0011288,0.0011288
24,148.965,2.91659,3.10609,2.87657,0.00381,0.66667,0.01044,0.00277,3.02385,3.5523,3.12061,0.0010892,0.0010892,0.0010892
25,154.925,2.7855,3.12779,2.8576,0.00939,0.54167,0.01572,0.00384,3.03516,3.65973,3.08936,0.0010496,0.0010496,0.0010496
26,160.715,2.82788,3.13899,2.87672,0.01879,0.25,0.01416,0.00363,2.99057,3.79445,3.03352,0.00101,0.00101,0.00101
27,166.542,2.80575,3.04321,2.83544,0.00462,0.79167,0.01905,0.00477,2.94286,3.44584,3.04008,0.0009704,0.0009704,0.0009704
28,172.497,2.81548,3.01366,2.79826,0.019,0.54167,0.0164,0.00394,2.93703,3.58425,3.01829,0.0009308,0.0009308,0.0009308
29,178.371,2.84591,3.10678,2.7572,0.00633,0.75,0.01348,0.00352,3.0056,3.57983,3.02221,0.0008912,0.0008912,0.0008912
30,184.207,2.8423,3.07794,2.87758,0.01975,0.08333,0.01592,0.0049,2.99829,3.54598,3.00152,0.0008516,0.0008516,0.0008516
31,189.95,2.68857,3.00799,2.77236,0.04058,0.0602,0.01862,0.0061,2.99313,3.54737,2.98195,0.000812,0.000812,0.000812
32,195.691,2.69466,2.98715,2.769,0.07122,0.04167,0.03248,0.01245,3.03598,3.63531,2.97361,0.0007724,0.0007724,0.0007724
33,201.99,2.69134,2.85102,2.6358,0.0602,0.04167,0.01602,0.00576,3.04381,3.67514,3.01228,0.0007328,0.0007328,0.0007328
34,208.323,2.78069,2.88744,2.72012,0.07983,0.08333,0.03027,0.00669,3.01735,3.70257,3.00679,0.0006932,0.0006932,0.0006932
35,214.875,2.79637,2.94962,2.78716,0.06493,0.08333,0.02726,0.0067,2.9504,3.4496,2.98057,0.0006536,0.0006536,0.0006536
36,221.286,2.75393,2.97841,2.71883,0.04742,0.08333,0.02458,0.00715,2.92524,3.3971,2.98439,0.000614,0.000614,0.000614
37,227.757,2.75194,2.98667,2.72664,0.13687,0.04167,0.06811,0.02311,2.902,3.3592,2.96795,0.0005744,0.0005744,0.0005744
38,233.858,2.69401,2.92402,2.68427,0.13445,0.08333,0.04445,0.0109,2.87136,3.30877,2.99419,0.0005348,0.0005348,0.0005348
39,239.989,2.68986,2.95112,2.68211,0.15856,0.08333,0.04748,0.01168,2.90474,3.32216,2.99818,0.0004952,0.0004952,0.0004952
40,246.189,2.6644,2.87815,2.72102,0.06495,0.08333,0.03027,0.00752,2.96901,3.38199,2.9842,0.0004556,0.0004556,0.0004556
41,252.163,2.56502,3.09199,2.58815,0.06074,0.09994,0.0333,0.00712,2.98147,3.5289,2.97084,0.000416,0.000416,0.000416
42,258.167,2.56732,3.11408,2.70098,0.41082,0.04167,0.06154,0.01104,2.99519,3.58982,2.98807,0.0003764,0.0003764,0.0003764
43,264.166,2.54064,3.06575,2.61591,0.45473,0.04167,0.0654,0.01038,2.96907,3.49957,2.98158,0.0003368,0.0003368,0.0003368
44,270.192,2.50966,2.99116,2.54798,0.73561,0.04167,0.06495,0.00989,2.98304,3.46385,2.98988,0.0002972,0.0002972,0.0002972
45,276.191,2.58969,3.13199,2.71361,0.77585,0.04167,0.06961,0.01214,2.97349,3.36213,2.98217,0.0002576,0.0002576,0.0002576
46,282.359,2.48391,2.97711,2.63014,0.1787,0.08333,0.07374,0.01277,2.96075,3.34562,2.96329,0.000218,0.000218,0.000218
47,288.265,2.48827,2.99982,2.60596,0.09865,0.08333,0.07892,0.01395,2.95352,3.33109,2.96578,0.0001784,0.0001784,0.0001784
48,294.331,2.46829,3.06381,2.60389,0.07403,0.08333,0.07883,0.01353,2.93099,3.30325,2.92394,0.0001388,0.0001388,0.0001388
49,300.34,2.4932,3.02598,2.58753,0.26274,0.08333,0.06295,0.01324,2.92988,3.25111,2.94379,9.92e-05,9.92e-05,9.92e-05
50,306.464,2.50308,3.09065,2.66328,0.3687,0.08333,0.08813,0.01945,2.91605,3.20766,2.95926,5.96e-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 7.58248 3.86718 4.03491 4.2951 0.00227 0.375 0.00974 0.00578 3.8812 3.99622 4.15869 0.00056 0.00056 0.00056
3 2 13.9887 3.849 3.95573 4.21698 0.00233 0.375 0.00552 0.00287 3.88792 3.95583 4.14719 0.00111743 0.00111743 0.00111743
4 3 20.1963 3.82315 3.89682 4.14241 0.00226 0.375 0.00465 0.00231 3.86413 3.98223 4.1467 0.00165189 0.00165189 0.00165189
5 4 25.9975 3.88628 3.91017 4.09585 0.00232 0.375 0.00581 0.00241 3.7991 4.02746 4.15074 0.0018812 0.0018812 0.0018812
6 5 32.3808 3.81224 3.74397 4.02627 0.00229 0.375 0.00965 0.00302 3.85 3.98862 4.04111 0.0018416 0.0018416 0.0018416
7 6 38.5823 3.84657 3.71527 3.91758 0.00216 0.375 0.03558 0.01089 3.65209 3.81254 4.01897 0.001802 0.001802 0.001802
8 7 44.5661 3.45534 3.61028 3.87833 0.00286 0.5 0.00559 0.00187 3.49262 3.74319 3.79219 0.0017624 0.0017624 0.0017624
9 8 50.5986 3.41512 3.64168 3.66016 0.00286 0.5 0.0053 0.00159 3.51966 3.93021 4.9429 0.0017228 0.0017228 0.0017228
10 9 56.7493 3.43039 3.5065 3.56448 0.00357 0.625 0.00426 0.00128 3.47188 3.97152 3.90089 0.0016832 0.0016832 0.0016832
11 10 62.8087 3.2681 3.48006 3.49726 0.00214 0.375 0.00217 0.00069 3.29854 4.34628 3.6408 0.0016436 0.0016436 0.0016436
12 11 68.9646 3.24858 3.47816 3.42633 0.03068 0.04167 0.00647 0.00131 3.21512 4.40411 3.71788 0.001604 0.001604 0.001604
13 12 75.1987 3.0272 3.49522 3.34452 0.00357 0.625 0.00563 0.00107 3.15748 4.16911 3.89962 0.0015644 0.0015644 0.0015644
14 13 81.6663 3.10525 3.44024 3.30269 0.00405 0.70833 0.00558 0.00151 3.03737 4.12775 3.68458 0.0015248 0.0015248 0.0015248
15 14 88.0817 3.05656 3.33744 3.22003 0.09945 0.04167 0.0116 0.00189 3.10771 4.80671 3.51149 0.0014852 0.0014852 0.0014852
16 15 94.3324 3.08564 3.37526 3.1946 0.02845 0.04167 0.00762 0.00147 3.13182 4.63245 3.47293 0.0014456 0.0014456 0.0014456
17 16 100.402 2.96126 3.17122 3.04857 0.00378 0.08333 0.00537 0.00125 3.04 4.223 3.31258 0.001406 0.001406 0.001406
18 17 106.273 2.93419 3.26308 3.06405 0.00381 0.625 0.00477 0.00135 2.94965 4.15937 3.24958 0.0013664 0.0013664 0.0013664
19 18 112.262 2.86612 3.28121 3.03611 0.00361 0.625 0.00526 0.00138 2.90519 4.03629 3.18737 0.0013268 0.0013268 0.0013268
20 19 118.347 2.96207 3.11173 2.92585 0.00381 0.66667 0.00704 0.00183 2.94075 3.83422 3.18178 0.0012872 0.0012872 0.0012872
21 20 124.434 2.92356 3.08375 2.88589 0.00429 0.75 0.0088 0.00221 2.93886 3.85541 3.18366 0.0012476 0.0012476 0.0012476
22 21 130.742 2.79487 3.20655 2.96298 0.00333 0.58333 0.0065 0.00201 2.96157 3.91003 3.18291 0.001208 0.001208 0.001208
23 22 136.633 2.82011 3.11523 2.92622 0.00874 0.41667 0.00706 0.00223 3.00108 3.7678 3.24255 0.0011684 0.0011684 0.0011684
24 23 142.815 2.83503 3.09436 2.86661 0.00429 0.75 0.00905 0.00263 2.97787 3.4654 3.2307 0.0011288 0.0011288 0.0011288
25 24 148.965 2.91659 3.10609 2.87657 0.00381 0.66667 0.01044 0.00277 3.02385 3.5523 3.12061 0.0010892 0.0010892 0.0010892
26 25 154.925 2.7855 3.12779 2.8576 0.00939 0.54167 0.01572 0.00384 3.03516 3.65973 3.08936 0.0010496 0.0010496 0.0010496
27 26 160.715 2.82788 3.13899 2.87672 0.01879 0.25 0.01416 0.00363 2.99057 3.79445 3.03352 0.00101 0.00101 0.00101
28 27 166.542 2.80575 3.04321 2.83544 0.00462 0.79167 0.01905 0.00477 2.94286 3.44584 3.04008 0.0009704 0.0009704 0.0009704
29 28 172.497 2.81548 3.01366 2.79826 0.019 0.54167 0.0164 0.00394 2.93703 3.58425 3.01829 0.0009308 0.0009308 0.0009308
30 29 178.371 2.84591 3.10678 2.7572 0.00633 0.75 0.01348 0.00352 3.0056 3.57983 3.02221 0.0008912 0.0008912 0.0008912
31 30 184.207 2.8423 3.07794 2.87758 0.01975 0.08333 0.01592 0.0049 2.99829 3.54598 3.00152 0.0008516 0.0008516 0.0008516
32 31 189.95 2.68857 3.00799 2.77236 0.04058 0.0602 0.01862 0.0061 2.99313 3.54737 2.98195 0.000812 0.000812 0.000812
33 32 195.691 2.69466 2.98715 2.769 0.07122 0.04167 0.03248 0.01245 3.03598 3.63531 2.97361 0.0007724 0.0007724 0.0007724
34 33 201.99 2.69134 2.85102 2.6358 0.0602 0.04167 0.01602 0.00576 3.04381 3.67514 3.01228 0.0007328 0.0007328 0.0007328
35 34 208.323 2.78069 2.88744 2.72012 0.07983 0.08333 0.03027 0.00669 3.01735 3.70257 3.00679 0.0006932 0.0006932 0.0006932
36 35 214.875 2.79637 2.94962 2.78716 0.06493 0.08333 0.02726 0.0067 2.9504 3.4496 2.98057 0.0006536 0.0006536 0.0006536
37 36 221.286 2.75393 2.97841 2.71883 0.04742 0.08333 0.02458 0.00715 2.92524 3.3971 2.98439 0.000614 0.000614 0.000614
38 37 227.757 2.75194 2.98667 2.72664 0.13687 0.04167 0.06811 0.02311 2.902 3.3592 2.96795 0.0005744 0.0005744 0.0005744
39 38 233.858 2.69401 2.92402 2.68427 0.13445 0.08333 0.04445 0.0109 2.87136 3.30877 2.99419 0.0005348 0.0005348 0.0005348
40 39 239.989 2.68986 2.95112 2.68211 0.15856 0.08333 0.04748 0.01168 2.90474 3.32216 2.99818 0.0004952 0.0004952 0.0004952
41 40 246.189 2.6644 2.87815 2.72102 0.06495 0.08333 0.03027 0.00752 2.96901 3.38199 2.9842 0.0004556 0.0004556 0.0004556
42 41 252.163 2.56502 3.09199 2.58815 0.06074 0.09994 0.0333 0.00712 2.98147 3.5289 2.97084 0.000416 0.000416 0.000416
43 42 258.167 2.56732 3.11408 2.70098 0.41082 0.04167 0.06154 0.01104 2.99519 3.58982 2.98807 0.0003764 0.0003764 0.0003764
44 43 264.166 2.54064 3.06575 2.61591 0.45473 0.04167 0.0654 0.01038 2.96907 3.49957 2.98158 0.0003368 0.0003368 0.0003368
45 44 270.192 2.50966 2.99116 2.54798 0.73561 0.04167 0.06495 0.00989 2.98304 3.46385 2.98988 0.0002972 0.0002972 0.0002972
46 45 276.191 2.58969 3.13199 2.71361 0.77585 0.04167 0.06961 0.01214 2.97349 3.36213 2.98217 0.0002576 0.0002576 0.0002576
47 46 282.359 2.48391 2.97711 2.63014 0.1787 0.08333 0.07374 0.01277 2.96075 3.34562 2.96329 0.000218 0.000218 0.000218
48 47 288.265 2.48827 2.99982 2.60596 0.09865 0.08333 0.07892 0.01395 2.95352 3.33109 2.96578 0.0001784 0.0001784 0.0001784
49 48 294.331 2.46829 3.06381 2.60389 0.07403 0.08333 0.07883 0.01353 2.93099 3.30325 2.92394 0.0001388 0.0001388 0.0001388
50 49 300.34 2.4932 3.02598 2.58753 0.26274 0.08333 0.06295 0.01324 2.92988 3.25111 2.94379 9.92e-05 9.92e-05 9.92e-05
51 50 306.464 2.50308 3.09065 2.66328 0.3687 0.08333 0.08813 0.01945 2.91605 3.20766 2.95926 5.96e-05 5.96e-05 5.96e-05

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