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(ultralytics) [hensdl@headnode01 DL_ultralytics]$ python annotate.py
/home/hensdl/mambaforge/envs/ultralytics/lib/python3.12/site-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML
warnings.warn("Can't initialize NVML")
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image 190/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_85.jpg: 384x640 14 birds, 1 cup, 22.0ms
image 191/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_86.jpg: 384x640 13 birds, 1 cup, 18.3ms
image 192/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_87.jpg: 384x640 12 birds, 1 cup, 23.0ms
image 193/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_88.jpg: 384x640 15 birds, 1 cup, 26.2ms
image 194/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_89.jpg: 384x640 13 birds, 1 cup, 28.8ms
image 195/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_9.jpg: 384x640 9 birds, 1 cup, 20.9ms
image 196/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_90.jpg: 384x640 14 birds, 1 cup, 21.3ms
image 197/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_91.jpg: 384x640 14 birds, 1 cup, 19.8ms
image 198/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_92.jpg: 384x640 15 birds, 1 cup, 44.7ms
image 199/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_93.jpg: 384x640 15 birds, 1 cup, 38.2ms
image 200/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_94.jpg: 384x640 15 birds, 1 cup, 18.7ms
image 201/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_95.jpg: 384x640 14 birds, 1 cup, 27.0ms
image 202/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_96.jpg: 384x640 15 birds, 24.4ms
image 203/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_97.jpg: 384x640 15 birds, 1 cup, 19.3ms
image 204/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_98.jpg: 384x640 16 birds, 21.0ms
image 205/205 /home/hensdl/others/DL_ultralytics/data/frames/frame_99.jpg: 384x640 15 birds, 1 cup, 20.6ms
Speed: 3.3ms preprocess, 27.3ms inference, 1.0ms postprocess per image at shape (1, 3, 384, 640)
(ultralytics) [hensdl@headnode01 DL_ultralytics]$
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yolo task=segment mode=train model=yolov8x-seg data=data.yaml epochs=20 imgsz=640
New https://pypi.org/project/ultralytics/8.2.39 available 😃 Update with 'pip install -U ultralytics'
/home/hensdl/mambaforge/envs/ultralytics/lib/python3.12/site-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML
warnings.warn("Can't initialize NVML")
Ultralytics YOLOv8.2.38 🚀 Python-3.12.4 torch-2.3.1 CUDA:0 (Tesla V100-PCIE-32GB, 32501MiB)
engine/trainer: task=segment, mode=train, model=yolov8x-seg, data=data.yaml, epochs=20, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train2, 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=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, 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=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, 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, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/segment/train2
Downloading https://ultralytics.com/assets/Arial.ttf to '/home/hensdl/.config/Ultralytics/Arial.ttf'...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 755k/755k [00:00<00:00, 20.3MB/s]
Overriding model.yaml nc=80 with nc=1
from n params module arguments
0 -1 1 2320 ultralytics.nn.modules.conv.Conv [3, 80, 3, 2]
1 -1 1 115520 ultralytics.nn.modules.conv.Conv [80, 160, 3, 2]
2 -1 3 436800 ultralytics.nn.modules.block.C2f [160, 160, 3, True]
3 -1 1 461440 ultralytics.nn.modules.conv.Conv [160, 320, 3, 2]
4 -1 6 3281920 ultralytics.nn.modules.block.C2f [320, 320, 6, True]
5 -1 1 1844480 ultralytics.nn.modules.conv.Conv [320, 640, 3, 2]
6 -1 6 13117440 ultralytics.nn.modules.block.C2f [640, 640, 6, True]
7 -1 1 3687680 ultralytics.nn.modules.conv.Conv [640, 640, 3, 2]
8 -1 3 6969600 ultralytics.nn.modules.block.C2f [640, 640, 3, True]
9 -1 1 1025920 ultralytics.nn.modules.block.SPPF [640, 640, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 3 7379200 ultralytics.nn.modules.block.C2f [1280, 640, 3]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 3 1948800 ultralytics.nn.modules.block.C2f [960, 320, 3]
16 -1 1 922240 ultralytics.nn.modules.conv.Conv [320, 320, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 3 7174400 ultralytics.nn.modules.block.C2f [960, 640, 3]
19 -1 1 3687680 ultralytics.nn.modules.conv.Conv [640, 640, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 3 7379200 ultralytics.nn.modules.block.C2f [1280, 640, 3]
22 [15, 18, 21] 1 12317171 ultralytics.nn.modules.head.Segment [1, 32, 320, [320, 640, 640]]
YOLOv8x-seg summary: 401 layers, 71751811 parameters, 71751795 gradients
Transferred 651/657 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.23M/6.23M [00:00<00:00, 63.2MB/s]
AMP: checks passed ✅
train: Scanning /home/hensdl/others/DL_ultralytics/train/labels... 185 images, 0 backgrounds, 0 corrupt: 100%|██████████| 185/185 [00:00<00:00
train: WARNING ⚠️ /home/hensdl/others/DL_ultralytics/train/images/frame_123.jpg: 1 duplicate labels removed
train: WARNING ⚠️ /home/hensdl/others/DL_ultralytics/train/images/frame_133.jpg: 1 duplicate labels removed
train: WARNING ⚠️ /home/hensdl/others/DL_ultralytics/train/images/frame_24.jpg: 1 duplicate labels removed
train: New cache created: /home/hensdl/others/DL_ultralytics/train/labels.cache
val: Scanning /home/hensdl/others/DL_ultralytics/val/labels... 20 images, 0 backgrounds, 0 corrupt: 100%|██████████| 20/20 [00:00<00:00, 137.6
val: WARNING ⚠️ /home/hensdl/others/DL_ultralytics/val/images/frame_188.jpg: 1 duplicate labels removed
val: New cache created: /home/hensdl/others/DL_ultralytics/val/labels.cache
Plotting labels to runs/segment/train2/labels.jpg...
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically...
optimizer: AdamW(lr=0.002, momentum=0.9) with parameter groups 106 weight(decay=0.0), 117 weight(decay=0.0005), 116 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/segment/train2
Starting training for 20 epochs...
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
1/20 14.5G 1.035 1.742 2.63 1.08 182 640: 100%|██████████| 12/12 [00:10<00:00, 1.15it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.395 0.807 0.722 0.593 0.413 0.835 0.782 0.614
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
2/20 15.2G 0.5767 0.86 0.7767 0.8687 184 640: 100%|██████████| 12/12 [00:07<00:00, 1.62it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.537 0.851 0.538 0.436 0.572 0.908 0.591 0.44
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
3/20 15.4G 0.5211 0.7058 0.6693 0.8538 164 640: 100%|██████████| 12/12 [00:07<00:00, 1.65it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.737 0.462 0.498 0.381 0.73 0.458 0.488 0.355
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
4/20 15.4G 0.5178 0.7812 0.6236 0.8504 193 640: 100%|██████████| 12/12 [00:07<00:00, 1.63it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.826 0.382 0.543 0.432 0.826 0.382 0.549 0.406
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
5/20 15.4G 0.5213 0.683 0.5679 0.8503 204 640: 100%|██████████| 12/12 [00:07<00:00, 1.65it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.826 0.382 0.543 0.432 0.826 0.382 0.549 0.406
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
6/20 15.1G 0.5125 0.6198 0.5621 0.846 227 640: 100%|██████████| 12/12 [00:07<00:00, 1.66it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.8 0.0803 0.138 0.12 0.8 0.0803 0.139 0.0779
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
7/20 15.3G 0.5273 0.5671 0.601 0.8551 169 640: 100%|██████████| 12/12 [00:07<00:00, 1.67it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.422 0.759 0.549 0.454 0.43 0.779 0.542 0.461
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
8/20 15.2G 0.4966 0.5834 0.5286 0.8493 135 640: 100%|██████████| 12/12 [00:07<00:00, 1.68it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.577 0.751 0.775 0.648 0.557 0.847 0.778 0.625
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
9/20 15.1G 0.4786 0.5685 0.5229 0.8354 175 640: 100%|██████████| 12/12 [00:07<00:00, 1.69it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.775 0.663 0.846 0.735 0.775 0.663 0.845 0.677
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
10/20 15.2G 0.4558 0.5806 0.5063 0.8278 203 640: 100%|██████████| 12/12 [00:07<00:00, 1.70it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.874 0.799 0.902 0.78 0.795 0.904 0.899 0.743
Closing dataloader mosaic
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
11/20 14.7G 0.4068 0.4994 0.5296 0.8298 104 640: 100%|██████████| 12/12 [00:08<00:00, 1.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.83 0.759 0.859 0.738 0.838 0.759 0.841 0.678
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
12/20 14.7G 0.382 0.4771 0.4798 0.8223 105 640: 100%|██████████| 12/12 [00:07<00:00, 1.62it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.889 0.635 0.735 0.642 0.88 0.635 0.752 0.646
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
13/20 14.7G 0.3807 0.4497 0.4837 0.8149 108 640: 100%|██████████| 12/12 [00:07<00:00, 1.60it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.843 0.924 0.942 0.808 0.871 0.956 0.953 0.786
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
14/20 14.7G 0.3505 0.4561 0.4571 0.8091 105 640: 100%|██████████| 12/12 [00:07<00:00, 1.63it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.852 0.951 0.957 0.825 0.874 0.972 0.964 0.813
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
15/20 14.7G 0.3339 0.4242 0.4511 0.8084 105 640: 100%|██████████| 12/12 [00:07<00:00, 1.61it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.845 0.944 0.944 0.835 0.873 0.976 0.961 0.815
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
16/20 14.7G 0.3212 0.4018 0.4265 0.7998 117 640: 100%|██████████| 12/12 [00:07<00:00, 1.61it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.733 0.94 0.922 0.832 0.764 0.98 0.934 0.792
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
17/20 14.7G 0.3068 0.4096 0.4088 0.8024 107 640: 100%|██████████| 12/12 [00:07<00:00, 1.54it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.833 0.942 0.946 0.852 0.857 0.966 0.946 0.815
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
18/20 14.6G 0.286 0.3804 0.3952 0.8049 110 640: 100%|██████████| 12/12 [00:07<00:00, 1.54it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.836 0.939 0.944 0.861 0.864 0.98 0.949 0.8
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
19/20 14.7G 0.2763 0.363 0.3742 0.7964 108 640: 100%|██████████| 12/12 [00:07<00:00, 1.52it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.851 0.936 0.954 0.869 0.87 0.972 0.954 0.807
Epoch GPU_mem box_loss seg_loss cls_loss dfl_loss Instances Size
20/20 14.7G 0.2626 0.3752 0.3671 0.789 104 640: 100%|██████████| 12/12 [00:07<00:00, 1.53it/s]
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.849 0.936 0.954 0.874 0.871 0.976 0.958 0.812
20 epochs completed in 0.061 hours.
Optimizer stripped from runs/segment/train2/weights/last.pt, 143.9MB
Optimizer stripped from runs/segment/train2/weights/best.pt, 143.9MB
Validating runs/segment/train2/weights/best.pt...
Ultralytics YOLOv8.2.38 🚀 Python-3.12.4 torch-2.3.1 CUDA:0 (Tesla V100-PCIE-32GB, 32501MiB)
YOLOv8x-seg summary (fused): 295 layers, 71721619 parameters, 0 gradients
Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100%|██████████|
all 20 249 0.85 0.936 0.954 0.874 0.871 0.976 0.958 0.811
Speed: 0.2ms preprocess, 6.2ms inference, 0.0ms loss, 1.0ms postprocess per image
Results saved to runs/segment/train2
💡 Learn more at https://docs.ultralytics.com/modes/train
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