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glenn-jocher
star4s
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Question
Of course, the training is working with "--noautoanchor" by my custom data .
I need to use Hyperpameter Evolution for Hyperpameter tunning.
At first, I tested the example of COCO128.
python train.py --img-size 2352 --batch 1 --epochs 1 --data coco128.yaml --hyp './data/hyps/hyp.scratch.yaml' --cfg './models/yolov5x6.yaml' --weights yolov5x6.pt --cache --evolve &
The Hyperpameter Evolution of COCO128 is working well.
My command for my custom data:
python train.py --img 2352 --batch 1 --epochs 1 --data test.yaml --cfg './models/yolov5x6.yaml' --weights yolov5x6.pt --cache --evolve 2
After I run the command line, I meet to the error.
AutoAnchor: ERROR: AutoAnchor: ERROR: scipy.cluster.vq.kmeans requested 12 points but returned only 9.
I follow the instruction of Hyperpameter Evolution Guide.
My setting:
Name Version Build Channel
_libgcc_mutex 0.1 main
ca-certificates 2021.10.26 h06a4308_2
certifi 2021.10.8 py38h06a4308_2
cycler 0.11.0
fonttools 4.29.1
google-auth 2.6.0
google-auth-oauthlib 0.4.6
grpcio 1.43.0
idna 3.3
importlib-metadata 4.11.1
kiwisolver 1.3.2
ld_impl_linux-64 2.35.1 h7274673_9
matplotlib 3.5.1
ncurses 6.3 h7f8727e_2
oauthlib 3.2.0
opencv-python 4.5.5.62
openssl 1.1.1m h7f8727e_0
pandas 1.4.1
Pillow 9.0.1
pip 21.2.4 py38h06a4308_0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pyparsing 3.0.7
python 3.8.12 h12debd9_0
pytz 2021.3
PyYAML 6.0
readline 8.1.2 h7f8727e_1
requests-oauthlib 1.3.1
rsa 4.8
scipy 1.8.0
seaborn 0.11.2
setuptools 58.0.4 py38h06a4308_0
sqlite 3.37.2 hc218d9a_0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
thop-0.0.31 2005241907
tk 8.6.11 h1ccaba5_0
torchvision 0.11.3
tqdm 4.62.3
typing_extensions 4.1.1
urllib3 1.26.8
Werkzeug 2.0.3
wheel 0.37.1 pyhd3eb1b0_0
zlib 1.2.11 h7f8727e_4
absl-py 1.0.0
cachetools 5.0.0
charset-normalizer 2.0.12
libffi 3.3 he6710b0_2
libgcc-ng 9.1.0 hdf63c60_0
libstdcxx-ng 9.1.0 hdf63c60_0
Markdown 3.3.6
numpy 1.22.2
packaging 21.3
protobuf 3.19.4
python-dateutil 2.8.2
requests 2.27.1
six 1.16.0
tensorboard 2.8.0
torch 1.10.2
xz 5.2.5 h7b6447c_0
zipp 3.7.0
How can I use Hyperpameter Evolution for my custom data?
Additional
a Label in my Custom data 1 0.52 0.921 0.072 0.098
After running Hyperparameters Evolution:
train: weights=yolov5x6.pt, cfg=./models/yolov5x6.yaml, data=OP1_WW.yaml, hyp=./data/hyps/hyp.scratch.yaml, epochs=1, batch_size=1, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=2, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: skipping check (offline), for updates see https://github.com/ultralytics/yolov5 YOLOv5 🚀 2022-2-8 torch 1.10.2+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
hyperparameters: lr0=0.001, lrf=0.1, 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, anchors=3, fl_gamma=0.0, hsv_h=0.0, hsv_s=0.0, hsv_v=0.0, degrees=0.0, translate=0.0, scale=0.0, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.0, mosaic=0.0, mixup=0.0, copy_paste=0.0 Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED) Overriding model.yaml anchors with anchors=3
0 -1 1 8800 models.common.Conv [3, 80, 6, 2, 2]
1 -1 1 115520 models.common.Conv [80, 160, 3, 2]
2 -1 4 309120 models.common.C3 [160, 160, 4]
3 -1 1 461440 models.common.Conv [160, 320, 3, 2]
4 -1 8 2259200 models.common.C3 [320, 320, 8]
5 -1 1 1844480 models.common.Conv [320, 640, 3, 2]
6 -1 12 13125120 models.common.C3 [640, 640, 12]
7 -1 1 5531520 models.common.Conv [640, 960, 3, 2]
8 -1 4 11070720 models.common.C3 [960, 960, 4]
9 -1 1 11061760 models.common.Conv [960, 1280, 3, 2]
10 -1 4 19676160 models.common.C3 [1280, 1280, 4]
11 -1 1 4099840 models.common.SPPF [1280, 1280, 5]
12 -1 1 1230720 models.common.Conv [1280, 960, 1, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 8] 1 0 models.common.Concat [1]
15 -1 4 11992320 models.common.C3 [1920, 960, 4, False]
16 -1 1 615680 models.common.Conv [960, 640, 1, 1]
17 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
18 [-1, 6] 1 0 models.common.Concat [1]
19 -1 4 5332480 models.common.C3 [1280, 640, 4, False]
20 -1 1 205440 models.common.Conv [640, 320, 1, 1]
21 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
22 [-1, 4] 1 0 models.common.Concat [1]
23 -1 4 1335040 models.common.C3 [640, 320, 4, False]
24 -1 1 922240 models.common.Conv [320, 320, 3, 2]
25 [-1, 20] 1 0 models.common.Concat [1]
26 -1 4 4922880 models.common.C3 [640, 640, 4, False]
27 -1 1 3687680 models.common.Conv [640, 640, 3, 2]
28 [-1, 16] 1 0 models.common.Concat [1]
29 -1 4 11377920 models.common.C3 [1280, 960, 4, False]
30 -1 1 8296320 models.common.Conv [960, 960, 3, 2]
31 [-1, 12] 1 0 models.common.Concat [1]
32 -1 4 20495360 models.common.C3 [1920, 1280, 4, False]
33 [23, 26, 29, 32] 1 67284 models.yolo.Detect [2, [[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]], [320, 640, 960, 1280]] Model Summary: 733 layers, 140045044 parameters, 140045044 gradients, 208.3 GFLOPs
Transferred 954/963 items from yolov5x6.pt Scaled weight_decay = 0.0005 optimizer: SGD with parameter groups 159 weight (no decay), 163 weight, 163 bias train: Scanning '/yolov5/datasets/OP1_test/labels/train.cache' images and labels... 14 found, 0 missing, 0 empty, 0 corrupt: 100%|██████████████████████| 14/14 [00:00<?, ?it/s] train: Caching images (0.0GB ram): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [00:00<00:00, 131.07it/s] val: Scanning '/yolov5/datasets/OP1_test/labels/train.cache' images and labels... 14 found, 0 missing, 0 empty, 0 corrupt: 100%|████████████████████████| 14/14 [00:00<?, ?it/s]
AutoAnchor: 1.71 anchors/target, 0.429 Best Possible Recall (BPR). Anchors are a poor fit to dataset ⚠️, attempting to improve... AutoAnchor: Running kmeans for 12 anchors on 14 points... AutoAnchor: ERROR: AutoAnchor: ERROR: scipy.cluster.vq.kmeans requested 12 points but returned only 9 Traceback (most recent call last): File "train.py", line 638, in
main(opt)
File "train.py", line 616, in main
results = train(hyp.copy(), opt, device, callbacks)
File "train.py", line 248, in train
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
File "/yolov5/utils/autoanchor.py", line 55, in check_anchors
new_bpr = metric(anchors)[0]
File "/yolov5/utils/autoanchor.py", line 36, in metric
r = wh[:, None] / k[None]
RuntimeError: The size of tensor a (14) must match the size of tensor b (4) at non-singleton dimension 1
Exception in thread Thread-13:
Traceback (most recent call last):
File "yolo_2/lib/python3.8/threading.py", line 932, in _bootstrap_inner