(yolov5) zhongming@ZhongMingdeMacBook
-
Pro yolov5
%
python train_ads.py
train: weights
=
yolov5s.pt, cfg
=
, data
=
data
/
ads.yaml, hyp
=
data
/
hyps
/
hyp.scratch.yaml, epochs
=
300
, batch_size
=
16
, imgsz
=
640
, rect
=
False
, resume
=
False
, nosave
=
False
, noval
=
False
, noautoanchor
=
False
, evolve
=
None
, bucket
=
, cache
=
None
, image_weights
=
False
, device
=
, multi_scale
=
False
, single_cls
=
True
, adam
=
False
, sync_bn
=
False
, workers
=
8
, project
=
runs
/
train, entity
=
None
, name
=
exp, exist_ok
=
False
, quad
=
False
, linear_lr
=
False
, label_smoothing
=
0.0
, upload_dataset
=
False
, bbox_interval
=
-
1
, save_period
=
-
1
, artifact_alias
=
latest, local_rank
=
-
1
, freeze
=
0
, patience
=
30
github: ⚠️ YOLOv5
is
out of date by
25
commits. Use `git pull`
or
`git clone https:
/
/
github.com
/
ultralytics
/
yolov5` to update.
YOLOv5v5.
0
-
405
-
gfad57c2 torch
1.9
.
0
CPU
hyperparameters: lr0
=
0.01
, lrf
=
0.2
, momentum
=
0.937
, weight_decay
=
0.0005
, warmup_epochs
=
3.0
, warmup_momentum
=
0.8
, warmup_bias_lr
=
0.1
, box
=
0.05
,
cls
=
0.5
, cls_pw
=
1.0
, obj
=
1.0
, obj_pw
=
1.0
, iou_t
=
0.2
, anchor_t
=
4.0
, fl_gamma
=
0.0
, 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
, mosaic
=
1.0
, mixup
=
0.0
, copy_paste
=
0.0
TensorBoard: Start with
'tensorboard --logdir runs/train'
, view at http:
/
/
localhost:
6006
/
wandb: (
1
) Create a W&B account
wandb: (
2
) Use an existing W&B account
wandb: (
3
) Don't visualize my results
wandb: Enter your choice:
3
wandb: You chose
'Don'
t visualize my results'
wandb: WARNING `resume` will be ignored since W&B syncing
is
set
to `offline`. Starting a new run with run
id
18h6dxo0
.
wandb: W&B syncing
is
set
to `offline`
in
this directory. Run `wandb online`
or
set
WANDB_MODE
=
online to enable cloud syncing.
Overriding model.yaml nc
=
80
with nc
=
1
from
n params module arguments
0
-
1
1
3520
models.common.Focus [
3
,
32
,
3
]
1
-
1
1
18560
models.common.Conv [
32
,
64
,
3
,
2
]
2
-
1
1
18816
models.common.C3 [
64
,
64
,
1
]
3
-
1
1
73984
models.common.Conv [
64
,
128
,
3
,
2
]
4
-
1
3
156928
models.common.C3 [
128
,
128
,
3
]
5
-
1
1
295424
models.common.Conv [
128
,
256
,
3
,
2
]
6
-
1
3
625152
models.common.C3 [
256
,
256
,
3
]
7
-
1
1
1180672
models.common.Conv [
256
,
512
,
3
,
2
]
8
-
1
1
656896
models.common.SPP [
512
,
512
, [
5
,
9
,
13
]]
9
-
1
1
1182720
models.common.C3 [
512
,
512
,
1
,
False
]
10
-
1
1
131584
models.common.Conv [
512
,
256
,
1
,
1
]
11
-
1
1
0
torch.nn.modules.upsampling.Upsample [
None
,
2
,
'nearest'
]
12
[
-
1
,
6
]
1
0
models.common.Concat [
1
]
13
-
1
1
361984
models.common.C3 [
512
,
256
,
1
,
False
]
14
-
1
1
33024
models.common.Conv [
256
,
128
,
1
,
1
]
15
-
1
1
0
torch.nn.modules.upsampling.Upsample [
None
,
2
,
'nearest'
]
16
[
-
1
,
4
]
1
0
models.common.Concat [
1
]
17
-
1
1
90880
models.common.C3 [
256
,
128
,
1
,
False
]
18
-
1
1
147712
models.common.Conv [
128
,
128
,
3
,
2
]
19
[
-
1
,
14
]
1
0
models.common.Concat [
1
]
20
-
1
1
296448
models.common.C3 [
256
,
256
,
1
,
False
]
21
-
1
1
590336
models.common.Conv [
256
,
256
,
3
,
2
]
22
[
-
1
,
10
]
1
0
models.common.Concat [
1
]
23
-
1
1
1182720
models.common.C3 [
512
,
512
,
1
,
False
]
24
[
17
,
20
,
23
]
1
16182
models.yolo.Detect [
1
, [[
10
,
13
,
16
,
30
,
33
,
23
], [
30
,
61
,
62
,
45
,
59
,
119
], [
116
,
90
,
156
,
198
,
373
,
326
]], [
128
,
256
,
512
]]
[W NNPACK.cpp:
79
] Could
not
initialize NNPACK! Reason: Unsupported hardware.
Model Summary:
283
layers,
7063542
parameters,
7063542
gradients,
16.4
GFLOPs
Transferred
356
/
362
items
from
yolov5s.pt
Scaled weight_decay
=
0.0005
optimizer: SGD with parameter groups
59
weight,
62
weight (no decay),
62
bias
train: Scanning
'data/train'
images
and
labels...
16
found,
0
missing,
0
empty,
0
corrupted:
100
%
|██|
16
/
16
[
00
:
02
<
00
:
00
,
6.10it
/
s]
train: New cache created: data
/
train.cache
val: Scanning
'data/val'
images
and
labels...
2
found,
0
missing,
0
empty,
0
corrupted:
100
%
|█████████|
2
/
2
[
00
:
04
<
00
:
00
,
2.46s
/
it]
val: New cache created: data
/
val.cache
Plotting labels...
autoanchor: Analyzing anchors... anchors
/
target
=
4.44
, Best Possible Recall (BPR)
=
1.0000
Image sizes
640
train,
640
val
Using
8
dataloader workers
Logging results to runs
/
train
/
exp3
Starting training
for
300
epochs...
Epoch gpu_mem box obj
cls
labels img_size
0
/
299
0G
0.1386
0.01956
0
28
640
:
100
%
|████████████████████████|
1
/
1
[
00
:
33
<
00
:
00
,
33.46s
/
it]
Class Images Labels P R
mAP
@.
5
mAP
@.
5
:.
95
:
100
%
|████████|
1
/
1
[
00
:
00
<
00
:
00
,
1.56it
/
s]
all
2
0
0
0
0
0
Class Images Labels P R
mAP
@.
5
mAP
@.
5
:.
95
:
100
%
|████████|
1
/
1
[
00
:
00
<
00
:
00
,
1.56it
/
s]
Epoch gpu_mem box obj
cls
labels img_size
1
/
299
0G
0.1378
0.0202
0
31
640
:
100
%
|████████████████████████|
1
/
1
[
00
:
27
<
00
:
00
,
27.41s
/
it]
Class Images Labels P R
mAP
@.
5
mAP
@.
5
:.
95
:
100
%
|████████|
1
/
1
[
00
:
00
<
00
:
00
,
1.66it
/
s]
all
2
0
0
0
0
0
Epoch gpu_mem box obj
cls
labels img_size
150
/
299
0G
0.05562
0.01635
0
27
640
:
100
%
|████████████████████████|
1
/
1
[
00
:
26
<
00
:
00
,
26.94s
/
it]
Class Images Labels P R
mAP
@.
5
mAP
@.
5
:.
95
:
100
%
|████████|
1
/
1
[
00
:
00
<
00
:
00
,
1.85it
/
s]
all
2
2
0.99
0.5
0.535
0.252
Epoch gpu_mem box obj
cls
labels img_size
151
/
299
0G
0.05614
0.01598
0
23
640
:
100
%
|████████████████████████|
1
/
1
[
00
:
26
<
00
:
00
,
26.93s
/
it]
Class Images Labels P R
mAP
@.
5
mAP
@.
5
:.
95
:
100
%
|████████|
1
/
1
[
00
:
00
<
00
:
00
,
1.86it
/
s]
all
2
2
0.997
0.5
0.538
0.207
EarlyStopping patience
30
exceeded, stopping training.
152
epochs completed
in
8.084
hours.
Optimizer stripped
from
runs
/
train
/
exp3
/
weights
/
last.pt,
14.4MB
Optimizer stripped
from
runs
/
train
/
exp3
/
weights
/
best.pt,
14.4MB
wandb: Waiting
for
W&B process to finish, PID
63332
wandb: Program ended successfully.
wandb: Find user logs
for
this run at:
/
Users
/
zhongming
/
PycharmProjects
/
yolov5
/
wandb
/
offline
-
run
-
20210913_191626
-
18h6dxo0
/
logs
/
debug.log
wandb: Find internal logs
for
this run at:
/
Users
/
zhongming
/
PycharmProjects
/
yolov5
/
wandb
/
offline
-
run
-
20210913_191626
-
18h6dxo0
/
logs
/
debug
-
internal.log
wandb: Run summary:
wandb: train
/
box_loss
0.05614
wandb: train
/
obj_loss
0.01598
wandb: train
/
cls_loss
0.0
wandb: metrics
/
precision
0.99749
wandb: metrics
/
recall
0.5
wandb: metrics
/
mAP_0.
5
0.53848
wandb: metrics
/
mAP_0.
5
:
0.95
0.20678
wandb: val
/
box_loss
0.06087
wandb: val
/
obj_loss
0.02391
wandb: val
/
cls_loss
0.0
wandb: x
/
lr0
0.0009
wandb: x
/
lr1
0.0009
wandb: x
/
lr2
0.0858
wandb: _runtime
29117
wandb: _timestamp
1631560903
wandb: _step
152
wandb: Run history:
wandb: train
/
box_loss ███▇▇▇▇▆▆▆▅▅▅▄▄▅▄▄▃▃▅▄▄▃▂▃▂▂▃▂▂▂▂▂▂▂▂▁▂▂
wandb: train
/
obj_loss ▅▅▅▄▃▃▃▂▄▄▅▄▃▆▄▄▅▁▅▅▃▅▆█▇██▃▁▃▃▃▆▄▅▂▆▅▄▂
wandb: train
/
cls_loss ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb: metrics
/
precision ▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁▂▁▁▁▁▁▁▁▁▂▃▃▄█████████
wandb: metrics
/
recall ▁▁▁▁▁▁▁▁▁▁▁▅▅▅▁▅▅█▅▁▅▅█▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅▅
wandb: metrics
/
mAP_0.
5
▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁▂▁▁▁▁▁▁▂▂▂▃▃▄█████████
wandb: metrics
/
mAP_0.
5
:
0.95
▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂▃▃▅█▅▆▆▅▅▆▅▅
wandb: val
/
box_loss █████▇▇▇▇▆▆▅▅▅▅▅▅▄▄▅▅▄▄▃▂▃▂▂▃▂▂▂▁▁▁▂▂▂▂▁
wandb: val
/
obj_loss ▄▄▄▃▃▂▂▁▁▁▂▂▂▂▂▂▂▃▃▃▂▃▄▅▇▆▇▇▇▆▇▆█▇▇▆▆▆▄▆
wandb: val
/
cls_loss ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb: x
/
lr0 ▁▁▁▂▂▂▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇███████████
wandb: x
/
lr1 ▁▁▁▂▂▂▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇▇███████████
wandb: x
/
lr2 ████▇▇▇▇▇▆▆▆▆▆▆▅▅▅▅▅▅▄▄▄▄▄▃▃▃▃▃▃▂▂▂▂▂▁▁▁
wandb: _runtime ▁▁▁▁▁▁▁▁▁▂▂▂▂▃▃▃▃▃▃▃▃▄▅▅▆▆▇▇▇▇▇▇▇▇▇█████
wandb: _timestamp ▁▁▁▁▁▁▁▁▁▂▂▂▂▃▃▃▃▃▃▃▃▄▅▅▆▆▇▇▇▇▇▇▇▇▇█████
wandb: _step ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb:
wandb: You can sync this run to the cloud by running:
wandb: wandb sync
/
Users
/
zhongming
/
PycharmProjects
/
yolov5
/
wandb
/
offline
-
run
-
20210913_191626
-
18h6dxo0