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result_no_weight.txt
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result_no_weight.txt
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(base) minhh@z790:~/workspace/yolo/yolov5$ python train.py --img 640 --epochs 50 --data voc2012.yaml &
[1] 4072814
(base) minhh@z790:~/workspace/yolo/yolov5$ train: weights=yolov5s.pt, cfg=, data=voc2012.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, 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, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest, ndjson_console=False, ndjson_file=False
github: ⚠️ YOLOv5 is out of date by 2840 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.
YOLOv5 🚀 e787d2f7 Python-3.12.4 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 3090, 24245MiB)
hyperparameters: 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=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
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
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 2 115712 models.common.C3 [128, 128, 2]
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 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
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 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs
Transferred 349/349 items from yolov5s.pt
AMP: checks passed ✅
freezing model.0.conv.weight
freezing model.0.bn.weight
freezing model.0.bn.bias
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
train: Scanning /home/minhh/workspace/yolo/yolov5/datasets/VOCdevkit/VOC2012/labels/train.cache... 11987 images, 7 backgrounds, 0 corrupt: 100%|██████████| 11987/11987 [00:00<?, ?it/s]
val: Scanning /home/minhh/workspace/yolo/yolov5/datasets/VOCdevkit/VOC2012/labels/val.cache... 3425 images, 1 backgrounds, 0 corrupt: 100%|██████████| 3425/3425 [00:00<?, ?it/s]
AutoAnchor: 2.29 anchors/target, 0.999 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to runs/train/exp2/labels.jpg...
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/train/exp2
Starting training for 50 epochs...
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
0/49 3.21G 0.05286 0.04205 0.03934 16 640: 100%|██████████| 750/750 [02:44<00:00, 4.57it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 108/108 [00:19<00:00, 5.52it/s]
all 3425 7416 0.376 0.383 0.325 0.13
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
1/49 5.67G 0.04413 0.03884 0.03471 18 640: 100%|██████████| 750/750 [02:34<00:00, 4.85it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 108/108 [00:21<00:00, 4.99it/s]
all 3425 7416 0.294 0.348 0.23 0.0861
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
2/49 5.67G 0.04519 0.03896 0.03855 13 640: 100%|██████████| 750/750 [02:43<00:00, 4.59it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 108/108 [00:20<00:00, 5.21it/s]
all 3425 7416 0.188 0.198 0.0873 0.0235
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
3/49 5.67G 0.04582 0.03918 0.04154 18 640: 100%|██████████| 750/750 [02:36<00:00, 4.80it/s]
Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 108/108 [00:20<00:00, 5.37it/s]
all 3425 7416 0.276 0.233 0.164 0.0576
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
4/49 5.67G 0.04413 0.03907 0.04004 89 640: 80%|████████ | 602/750 [01:57<00:29, 5.03it/s]