add weights

This commit is contained in:
Hunter Halloran 2025-05-12 20:24:20 +00:00
parent 66660c5507
commit 6011c5005a
9 changed files with 115 additions and 0 deletions

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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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