标签：yolov5 False models 768 -- 384 common 日志 光标
yolov5训练警告反光标检测模型日志
1、标注数据
2、整理数据
3、训练：修改：myvoc.yaml
myvoc.yamltrain: VOC_2022052001/train.txt val: VOC_2022052001/val.txt # number of classes nc: 1 # class names names: ["warning"]
4、开始训练
python train_20220520.py --img-size 640 --batch-size 1 --epochs 300 --data ./data/myvoc.yaml --cfg ./models/yolov5m.yaml --workers 0
训练日志
(wind_2021) F:\PytorchProject\yolov5_train_warning>
(wind_2021) F:\PytorchProject\yolov5_train_warning>
(wind_2021) F:\PytorchProject\yolov5_train_warning>python train_20220520.py --img-size 640 --batch-size 1 --epochs 300 --data ./data/myvoc.yaml --cfg ./models/yolov5m.yaml --workers 0
Using torch 1.8.1+cu111 CUDA:0 (NVIDIA GeForce RTX 3080 Laptop GPU, 16383.5MB)
Namespace(adam=False, batch_size=1, bucket='', cache_images=False, cfg='./models/yolov5m.yaml', data='./data/myvoc.yaml', device='', epochs=300, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=False, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs\\train\\exp2', single_cls=False, sync_bn=False, total_batch_size=1, weights='yolov5s.pt', workers=0, world_size=1)
Start Tensorboard with "tensorboard --logdir runs/train", view at http://localhost:6006/
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}
Overriding model.yaml nc=80 with nc=1
from n params module arguments
0 -1 1 5280 models.common.Focus [3, 48, 3]
1 -1 1 41664 models.common.Conv [48, 96, 3, 2]
2 -1 1 65280 models.common.C3 [96, 96, 2]
3 -1 1 166272 models.common.Conv [96, 192, 3, 2]
4 -1 1 629760 models.common.C3 [192, 192, 6]
5 -1 1 664320 models.common.Conv [192, 384, 3, 2]
6 -1 1 2512896 models.common.C3 [384, 384, 6]
7 -1 1 2655744 models.common.Conv [384, 768, 3, 2]
8 -1 1 1476864 models.common.SPP [768, 768, [5, 9, 13]]
9 -1 1 4134912 models.common.C3 [768, 768, 2, False]
10 -1 1 295680 models.common.Conv [768, 384, 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 1182720 models.common.C3 [768, 384, 2, False]
14 -1 1 74112 models.common.Conv [384, 192, 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 296448 models.common.C3 [384, 192, 2, False]
18 -1 1 332160 models.common.Conv [192, 192, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 1035264 models.common.C3 [384, 384, 2, False]
21 -1 1 1327872 models.common.Conv [384, 384, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 4134912 models.common.C3 [768, 768, 2, False]
24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]
Model Summary: 391 layers, 21056406 parameters, 21056406 gradients, 50.4 GFLOPS
Transferred 59/506 items from yolov5s.pt
Scaled weight_decay = 0.0005
Optimizer groups: 86 .bias, 86 conv.weight, 83 other
Scanning 'VOC_2022052001\labels' for images and labels... 341 found, 0 missing, 0 empty, 0 corrupted: 100%|█████████████████████████████████████████████| 341/341 [00:00<00:00, 1891.93it/s]
New cache created: VOC_2022052001\labels.cache
Scanning 'VOC_2022052001\labels' for images and labels... 61 found, 0 missing, 0 empty, 0 corrupted: 100%|████████████████████████████████████████████████| 61/61 [00:00<00:00, 2084.54it/s]
New cache created: VOC_2022052001\labels.cache | 0/61 [00:00<?, ?it/s]
Plotting labels... 052001\labels.cache' for images and labels... 61 found, 0 missing, 0 empty, 0 corrupted: 100%|████████████████████████████████████████████████████| 61/61 [00:00<?, ?it/s]
Scanning 'VOC_2022052001\labels.cache' for images and labels... 341 found, 0 missing, 0 empty, 0 corrupted: 100%|█████████████████████████████████████████████████| 341/341 [00:00<?, ?it/s]
Scanning 'VOC_2022052001\labels.cache' for images and labels... 61 found, 0 missing, 0 empty, 0 corrupted: 100%|████████████████████████████████████████████████████| 61/61 [00:00<?, ?it/s]
Analyzing anchors... anchors/target = 5.90, Best Possible Recall (BPR) = 1.0000
Image sizes 640 train, 640 test
Using 0 dataloader workers
Logging results to runs\train\exp2
Starting training for 300 epochs...
Starting training for 300 epochs...
Epoch gpu_mem box obj cls total targets img_size
0/299 1.22G 0.1006 0.03639 0 0.137 2 640: 100%|███████████████████████████████████████████████████████████████████| 341/341 [01:16<00:00, 4.44it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100%|█████████████████████████████████████████████████████████| 61/61 [00:05<00:00, 11.15it/s]
all 61 124 5.46e-05 0.00806 1.79e-06 1.79e-07
Epoch gpu_mem box obj cls total targets img_size
1/299 1.2G 0.0903 0.04164 0 0.1319 2 640: 100%|███████████████████████████████████████████████████████████████████| 341/341 [01:10<00:00, 4.81it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100%|█████████████████████████████████████████████████████████| 61/61 [00:04<00:00, 14.48it/s]
all 61 124 0 0 3.48e-05 3.48e-06
Epoch gpu_mem box obj cls total targets img_size
2/299 1.2G 0.07705 0.03868 0 0.1157 1 640: 100%|███████████████████████████████████████████████████████████████████| 341/341 [01:11<00:00, 4.75it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100%|█████████████████████████████████████████████████████████| 61/61 [00:04<00:00, 14.37it/s]
all 61 124 0.0459 0.0726 0.0135 0.00226
Epoch gpu_mem box obj cls total targets img_size
3/299 1.2G 0.0841 0.03379 0 0.1179 4 640: 100%|███████████████████████████████████████████████████████████████████| 341/341 [01:10<00:00, 4.82it/s]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100%|█████████████████████████████████████████████████████████| 61/61 [00:04<00:00, 14.10it/s]
all 61 124 0 0 0.00207 0.00028
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标签：yolov5,False,models,768,--,384,common,日志,光标 来源： https://www.cnblogs.com/herd/p/16295008.html
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