YOLOV8

YOLOV8

Helpers

Training

class Args(argparse.Namespace):
  model = 'yolov8l.pt'
  cfg = 'default.yaml'
  iterative_steps = 15
  target_prune_rate = 0.15
  max_map_drop = 0.2
  sched = Schedule(partial(sched_onecycle,  α=10, β=4))

args=Args()
prune(args)

Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43668288 parameters, 0 gradients, 165.2 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.731      0.768      0.828      0.659
Speed: 0.1ms preprocess, 7.7ms inference, 0.0ms loss, 0.6ms postprocess per image
Results saved to runs/detect/val59
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train49
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/train49/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/train49
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      14.4G     0.8537     0.7447      1.082        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.776      0.741      0.832      0.667

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      12.8G     0.8612     0.7059      1.079        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.859       0.75      0.861      0.697

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      12.7G     0.8249     0.6306      1.054        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.882      0.753      0.862      0.709

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      12.8G     0.7998     0.5746      1.047         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.882      0.799       0.87      0.721

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      13.1G     0.8028     0.5566      1.034         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.88      0.804      0.876      0.728

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      12.8G     0.8042     0.5415      1.047        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.896      0.833      0.901      0.741

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      12.8G     0.7493     0.5095      1.003         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.906      0.827      0.902      0.746

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      12.8G     0.7589     0.5373      1.012        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.91      0.828      0.903      0.749

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      12.8G     0.7234     0.4783     0.9947        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.912      0.832      0.906      0.754

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      12.7G     0.7445     0.4764     0.9944        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.905      0.839      0.905      0.754

10 epochs completed in 0.027 hours.
Optimizer stripped from runs/detect/train49/weights/last.pt, 175.3MB
Optimizer stripped from runs/detect/train49/weights/best.pt, 175.3MB

Validating runs/detect/train49/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43668288 parameters, 0 gradients, 165.2 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.904      0.841      0.905      0.755
Speed: 0.1ms preprocess, 4.2ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to runs/detect/train49
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43668288 parameters, 0 gradients, 165.2 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.917      0.823      0.901      0.754
Speed: 0.2ms preprocess, 10.8ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/baseline_val184
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43325836 parameters, 74176 gradients, 163.3 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.883      0.849      0.903      0.743
Speed: 0.2ms preprocess, 12.4ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_0_pre_val131
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_0_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_0_finetune103
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_0_finetune103/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_0_finetune103
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      13.6G     0.7161     0.4777     0.9953        122        640: 100%|██████████| 8/8 [00:03
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.92      0.841      0.907       0.75

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      13.3G     0.6503     0.4152     0.9541        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.904      0.851       0.91      0.765

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      13.2G     0.6809      0.434     0.9746        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.902      0.854      0.907      0.768

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      13.2G     0.6464     0.4095     0.9681         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.919      0.853      0.913      0.773

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      13.2G     0.6799     0.4357     0.9637         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.916      0.856      0.918      0.779

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      13.2G     0.6787     0.4261     0.9708        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.924      0.851      0.923       0.78

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      13.2G     0.6758     0.4286      0.957         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.91      0.852       0.92      0.785

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      13.2G     0.6818     0.4492     0.9705        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.899      0.856      0.921      0.786

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      13.2G     0.6594      0.429     0.9635        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.894      0.871      0.925      0.794

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      13.2G     0.6705     0.4277     0.9541        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.899       0.87      0.927      0.793

10 epochs completed in 0.038 hours.
Optimizer stripped from runs/detect/step_0_finetune103/weights/last.pt, 173.9MB
Optimizer stripped from runs/detect/step_0_finetune103/weights/best.pt, 173.9MB

Validating runs/detect/step_0_finetune103/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43325836 parameters, 0 gradients, 163.3 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.895       0.87      0.925      0.793
Speed: 0.1ms preprocess, 5.1ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_0_finetune103
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43325836 parameters, 0 gradients, 163.3 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.897      0.863      0.922      0.787
Speed: 0.2ms preprocess, 12.4ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_0_post_val75
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43081939 parameters, 74176 gradients, 162.7 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.913      0.872      0.929      0.788
Speed: 0.1ms preprocess, 12.5ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_1_pre_val66
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_1_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_1_finetune62
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_1_finetune62/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_1_finetune62
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      13.3G     0.5906     0.3832     0.9224        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.903      0.868      0.925      0.797

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      13.3G     0.5313     0.3408     0.9001        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.908       0.87      0.925      0.796

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      13.2G     0.5791     0.3608     0.9246        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.902      0.871      0.927      0.799

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      13.3G     0.5612     0.3602     0.9298         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.914      0.867      0.929      0.795

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      13.2G     0.5719     0.3787     0.9132         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.906      0.875       0.93      0.792

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      13.2G     0.5878     0.3844     0.9333        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.935      0.859       0.93      0.796

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      13.3G     0.5939     0.3776     0.9134         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.928       0.86      0.928      0.799

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      13.2G     0.6093     0.3903     0.9311        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.932      0.862      0.933      0.802

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      13.3G     0.6003      0.386     0.9318        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.92      0.877      0.935      0.804

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      13.2G     0.6393      0.408     0.9322        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.911      0.888      0.937      0.806

10 epochs completed in 0.025 hours.
Optimizer stripped from runs/detect/step_1_finetune62/weights/last.pt, 173.0MB
Optimizer stripped from runs/detect/step_1_finetune62/weights/best.pt, 173.0MB

Validating runs/detect/step_1_finetune62/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43081939 parameters, 0 gradients, 162.7 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.911      0.888      0.937      0.806
Speed: 0.1ms preprocess, 5.1ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to runs/detect/step_1_finetune62
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43081939 parameters, 0 gradients, 162.7 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.907      0.888      0.937      0.804
Speed: 0.1ms preprocess, 12.5ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_1_post_val48
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 42712366 parameters, 74176 gradients, 161.3 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.923      0.871      0.933      0.794
Speed: 0.2ms preprocess, 12.5ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_2_pre_val50
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_2_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_2_finetune48
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_2_finetune48/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_2_finetune48
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      13.4G      0.548     0.3528     0.9023        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.923      0.871      0.935      0.801

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      13.3G     0.4746     0.3015     0.8763        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.93      0.877       0.94      0.806

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      13.2G     0.5379     0.3445     0.9065        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.94      0.871      0.942      0.812

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      13.3G     0.5157     0.3339     0.9019         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.93      0.877      0.938      0.811

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      13.3G     0.5169     0.3404     0.8804         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.921      0.883      0.939       0.81

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      13.3G     0.5339     0.3559     0.9031        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.929      0.878       0.94      0.811

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      13.3G     0.5597     0.3561     0.8945         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.939      0.876      0.941      0.813

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      13.3G     0.5733     0.3857     0.9149        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.936      0.879      0.941      0.818

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      13.3G     0.5769     0.3717     0.9222        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.941      0.882      0.941      0.821

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      13.2G     0.6167     0.3871     0.9151        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.939      0.882      0.941      0.821

10 epochs completed in 0.024 hours.
Optimizer stripped from runs/detect/step_2_finetune48/weights/last.pt, 171.5MB
Optimizer stripped from runs/detect/step_2_finetune48/weights/best.pt, 171.5MB

Validating runs/detect/step_2_finetune48/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 42712366 parameters, 0 gradients, 161.3 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.94      0.882      0.942      0.822
Speed: 0.1ms preprocess, 4.9ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_2_finetune48
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 42712366 parameters, 0 gradients, 161.3 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.92      0.888      0.943      0.813
Speed: 0.1ms preprocess, 12.5ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_2_post_val42
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 42094706 parameters, 74176 gradients, 158.8 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.94      0.864      0.936      0.804
Speed: 0.1ms preprocess, 12.7ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_3_pre_val36
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_3_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_3_finetune36
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_3_finetune36/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_3_finetune36
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      13.9G     0.5395     0.3534      0.897        122        640: 100%|██████████| 8/8 [00:42
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.932      0.875      0.937      0.808

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      13.2G     0.4523     0.2943     0.8601        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942      0.875      0.942      0.816

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      13.2G     0.5011     0.3242      0.885        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.94      0.879       0.94      0.816

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      13.2G     0.4896     0.3239      0.881         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.945      0.873      0.941      0.818

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      13.2G     0.4877     0.3266     0.8653         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.924      0.883      0.939      0.819

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      13.2G     0.5175      0.341     0.8913        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942      0.879      0.943      0.824

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      13.2G     0.5484     0.3518     0.8896         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942      0.882      0.944      0.825

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      13.2G     0.5657     0.3636      0.901        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.947      0.877      0.944      0.827

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      13.2G     0.5557     0.3553      0.908        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.932      0.888      0.945      0.827

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      13.2G     0.6072      0.381     0.9066        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.938      0.885      0.945      0.827

10 epochs completed in 0.037 hours.
Optimizer stripped from runs/detect/step_3_finetune36/weights/last.pt, 169.0MB
Optimizer stripped from runs/detect/step_3_finetune36/weights/best.pt, 169.0MB

Validating runs/detect/step_3_finetune36/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 42094706 parameters, 0 gradients, 158.8 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.947      0.877      0.944      0.827
Speed: 0.1ms preprocess, 4.8ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_3_finetune36
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 42094706 parameters, 0 gradients, 158.8 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.945      0.875      0.943      0.824
Speed: 0.2ms preprocess, 12.7ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_3_post_val34
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 40919781 parameters, 74176 gradients, 154.4 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.913      0.876      0.935      0.792
Speed: 0.2ms preprocess, 12.6ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_4_pre_val32
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_4_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_4_finetune32
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_4_finetune32/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_4_finetune32
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      13.8G      0.573     0.3665     0.9011        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.926      0.881       0.94      0.803

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      13.4G     0.4596     0.2974     0.8554        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.932      0.879      0.943      0.815

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      13.2G     0.5037     0.3324     0.8775        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.94      0.875       0.94      0.815

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      13.3G     0.4863      0.313     0.8774         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.954      0.872      0.943      0.813

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      13.2G     0.4905     0.3254     0.8586         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942       0.87      0.939      0.812

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      13.2G     0.5016     0.3312     0.8863        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.914      0.888      0.939      0.812

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      13.2G     0.5446     0.3534     0.8808         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.937      0.874      0.942      0.818

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      13.2G      0.554     0.3697     0.8957        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.927      0.885      0.942      0.821

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      13.2G     0.5756      0.359     0.9062        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942      0.889      0.945      0.825

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      13.2G     0.6089     0.3807     0.9001        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942      0.884      0.945      0.826

10 epochs completed in 0.035 hours.
Optimizer stripped from runs/detect/step_4_finetune32/weights/last.pt, 164.3MB
Optimizer stripped from runs/detect/step_4_finetune32/weights/best.pt, 164.3MB

Validating runs/detect/step_4_finetune32/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 40919781 parameters, 0 gradients, 154.4 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942      0.884      0.945      0.827
Speed: 0.1ms preprocess, 4.9ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to runs/detect/step_4_finetune32
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 40919781 parameters, 0 gradients, 154.4 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.929      0.891      0.944       0.82
Speed: 0.1ms preprocess, 12.5ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_4_post_val31
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 39455305 parameters, 74176 gradients, 149.4 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.916      0.864      0.929      0.789
Speed: 0.2ms preprocess, 13.0ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_5_pre_val31
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_5_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_5_finetune31
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_5_finetune31/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_5_finetune31
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      13.5G     0.5773     0.3687     0.8973        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.904      0.881      0.935      0.801

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10        13G     0.4697     0.3058     0.8551        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.906      0.893      0.941      0.807

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      12.9G     0.5138     0.3263     0.8829        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.92      0.889      0.942      0.811

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10        13G     0.4938     0.3293     0.8823         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.928      0.883      0.944      0.813

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      12.9G     0.5047     0.3398     0.8637         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.934       0.88      0.943      0.819

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10        13G     0.5144     0.3483      0.886        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942      0.869       0.94      0.815

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10        13G      0.539     0.3521      0.879         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.943      0.865      0.941      0.814

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10        13G     0.5493     0.3683      0.895        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.934      0.875      0.939      0.817

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10        13G      0.581     0.3607     0.9096        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.939      0.876       0.94       0.82

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      12.9G     0.6293     0.3874     0.9156        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.938      0.886      0.946      0.822

10 epochs completed in 0.035 hours.
Optimizer stripped from runs/detect/step_5_finetune31/weights/last.pt, 158.5MB
Optimizer stripped from runs/detect/step_5_finetune31/weights/best.pt, 158.5MB

Validating runs/detect/step_5_finetune31/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 39455305 parameters, 0 gradients, 149.4 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.938      0.886      0.946      0.822
Speed: 0.1ms preprocess, 4.8ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_5_finetune31
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 39455305 parameters, 0 gradients, 149.4 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.928      0.891      0.943       0.82
Speed: 0.2ms preprocess, 12.9ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_5_post_val31
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 37708749 parameters, 74176 gradients, 143.2 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.904      0.848      0.923       0.76
Speed: 0.2ms preprocess, 10.9ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_6_pre_val28
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_6_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_6_finetune28
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_6_finetune28/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_6_finetune28
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      13.3G     0.6267     0.3973     0.9214        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.909      0.859      0.932      0.782

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      12.8G     0.5114     0.3263     0.8662        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.932      0.874      0.939      0.803

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      12.7G     0.5466     0.3434     0.8876        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.931      0.875      0.939      0.808

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      12.7G     0.5238     0.3378     0.8878         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.939      0.874      0.939       0.81

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      12.7G     0.5198     0.3522     0.8708         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.947       0.87       0.94      0.808

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      12.8G     0.5276      0.352     0.8876        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.933      0.879      0.939       0.81

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      12.8G     0.5516     0.3594     0.8792         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.931      0.882      0.943       0.81

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      12.8G     0.5795     0.3736     0.9109        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.933      0.893      0.949      0.817

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      12.8G      0.597     0.3801     0.9136        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.932       0.89      0.948      0.815

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      12.7G     0.6406     0.3889     0.9059        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.928      0.899      0.948      0.816

10 epochs completed in 0.034 hours.
Optimizer stripped from runs/detect/step_6_finetune28/weights/last.pt, 151.5MB
Optimizer stripped from runs/detect/step_6_finetune28/weights/best.pt, 151.5MB

Validating runs/detect/step_6_finetune28/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 37708749 parameters, 0 gradients, 143.2 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.933      0.889      0.948      0.817
Speed: 0.1ms preprocess, 4.8ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_6_finetune28
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 37708749 parameters, 0 gradients, 143.2 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.937      0.878      0.946      0.808
Speed: 0.1ms preprocess, 10.8ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_6_post_val27
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 35995675 parameters, 74176 gradients, 136.7 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.838      0.847      0.905      0.744
Speed: 0.2ms preprocess, 12.1ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_7_pre_val25
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_7_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_7_finetune25
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_7_finetune25/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_7_finetune25
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10        13G     0.6576     0.4219     0.9433        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.884      0.852      0.921      0.764

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      13.2G     0.5285     0.3538     0.8714        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.921      0.864      0.937      0.782

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      12.4G     0.5672     0.3781     0.8972        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.921       0.87       0.94      0.791

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      12.4G     0.5324     0.3593     0.8898         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.921      0.869      0.937      0.796

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      12.1G     0.5564      0.395     0.8841         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.926      0.888      0.941      0.799

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      12.3G     0.5555     0.3674     0.9059        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.92      0.891      0.942      0.797

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      12.3G     0.5972     0.3946     0.9014         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.909      0.897      0.942      0.797

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      12.3G     0.6033     0.4048     0.9106        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.919      0.892      0.943      0.805

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      12.3G     0.6098     0.3878     0.9253        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.933      0.884      0.944      0.808

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      12.3G     0.6518     0.4124     0.9181        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.934      0.887      0.945      0.811

10 epochs completed in 0.036 hours.
Optimizer stripped from runs/detect/step_7_finetune25/weights/last.pt, 144.6MB
Optimizer stripped from runs/detect/step_7_finetune25/weights/best.pt, 144.6MB

Validating runs/detect/step_7_finetune25/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 35995675 parameters, 0 gradients, 136.7 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.934      0.887      0.945       0.81
Speed: 0.1ms preprocess, 4.8ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_7_finetune25
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 35995675 parameters, 0 gradients, 136.7 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.934      0.877      0.942      0.805
Speed: 0.1ms preprocess, 12.0ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_7_post_val25
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 34583399 parameters, 74176 gradients, 131.4 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.861      0.846      0.915      0.747
Speed: 0.2ms preprocess, 12.0ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_8_pre_val24
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_8_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_8_finetune24
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_8_finetune24/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_8_finetune24
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      12.7G     0.6527     0.4186     0.9399        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.878       0.86      0.925      0.769

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      12.9G     0.5123     0.3376     0.8642        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.902      0.884      0.932       0.78

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      12.2G     0.5575     0.3672     0.8903        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.918      0.887      0.935      0.784

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      12.2G     0.5313     0.3422     0.8975         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.913      0.897      0.936      0.795

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      12.2G      0.543     0.3699      0.874         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.922      0.891      0.939      0.795

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      12.2G     0.5544     0.3693        0.9        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.936      0.885      0.938      0.798

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      12.2G     0.5915     0.3854     0.8924         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942      0.883      0.939      0.801

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10        12G     0.6192     0.4081     0.9123        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.944      0.882       0.94      0.803

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      12.2G     0.6259     0.4123     0.9284        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.947       0.88      0.941      0.806

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      12.1G     0.6654     0.4213     0.9262        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.944      0.886       0.94      0.808

10 epochs completed in 0.037 hours.
Optimizer stripped from runs/detect/step_8_finetune24/weights/last.pt, 139.0MB
Optimizer stripped from runs/detect/step_8_finetune24/weights/best.pt, 139.0MB

Validating runs/detect/step_8_finetune24/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 34583399 parameters, 0 gradients, 131.4 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.948      0.882       0.94      0.808
Speed: 0.1ms preprocess, 4.1ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_8_finetune24
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 34583399 parameters, 0 gradients, 131.4 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.939      0.885       0.94      0.804
Speed: 0.1ms preprocess, 12.5ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_8_post_val24
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 33747610 parameters, 74176 gradients, 128.5 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.919      0.872      0.923      0.774
Speed: 0.2ms preprocess, 13.1ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_9_pre_val23
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_9_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_9_finetune23
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_9_finetune23/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_9_finetune23
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      12.6G     0.6022     0.3899     0.9207        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.921      0.881       0.93      0.784

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      12.1G     0.4755     0.3118      0.851        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.941      0.883      0.933      0.794

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10        12G     0.5226     0.3441     0.8847        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.936      0.884      0.936      0.795

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      11.8G     0.5197     0.3324     0.8815         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.935      0.883      0.933      0.792

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10        12G     0.5239      0.353     0.8671         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.936      0.882      0.934      0.792

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10        12G     0.5413     0.3589     0.8919        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.93      0.877      0.934      0.802

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      12.1G     0.5753     0.3723     0.8863         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.944      0.873      0.933      0.802

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      12.1G     0.6104     0.3991     0.9113        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.95      0.868      0.931        0.8

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      12.1G     0.6059      0.395     0.9182        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.943      0.873      0.932      0.805

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10        12G     0.6558     0.4098     0.9218        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.945      0.871      0.933      0.805

10 epochs completed in 0.033 hours.
Optimizer stripped from runs/detect/step_9_finetune23/weights/last.pt, 135.6MB
Optimizer stripped from runs/detect/step_9_finetune23/weights/best.pt, 135.6MB

Validating runs/detect/step_9_finetune23/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 33747610 parameters, 0 gradients, 128.5 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.945      0.871      0.933      0.806
Speed: 0.1ms preprocess, 4.4ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_9_finetune23
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 33747610 parameters, 0 gradients, 128.5 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.943      0.875      0.932      0.804
Speed: 0.2ms preprocess, 14.0ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_9_post_val23
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 33209910 parameters, 74176 gradients, 126.7 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.933      0.855      0.928      0.782
Speed: 0.2ms preprocess, 13.6ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_10_pre_val17
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_10_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_10_finetune17
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_10_finetune17/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_10_finetune17
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      12.3G     0.5909     0.3739      0.911        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.938      0.863      0.931      0.795

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      11.9G     0.4459     0.2951     0.8389        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942       0.87      0.932      0.802

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      11.8G     0.5232     0.3395     0.8787        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.946      0.875      0.935      0.802

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      11.8G     0.4976     0.3318      0.877         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.945      0.878      0.934      0.795

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      11.8G     0.5079     0.3425     0.8612         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.946      0.875      0.934      0.798

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      11.8G     0.5287     0.3422     0.8902        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.951      0.875      0.937      0.804

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      11.9G     0.5654     0.3632     0.8837         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.951      0.879      0.938      0.803

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      11.9G     0.5918     0.3874     0.9027        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.948      0.878      0.937      0.802

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      11.9G     0.6008     0.3761      0.914        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.953      0.876      0.939      0.807

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      11.8G     0.6525     0.4039     0.9107        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.951      0.876      0.939      0.806

10 epochs completed in 0.032 hours.
Optimizer stripped from runs/detect/step_10_finetune17/weights/last.pt, 133.4MB
Optimizer stripped from runs/detect/step_10_finetune17/weights/best.pt, 133.4MB

Validating runs/detect/step_10_finetune17/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 33209910 parameters, 0 gradients, 126.7 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.952      0.876      0.937      0.807
Speed: 0.1ms preprocess, 4.3ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_10_finetune17
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 33209910 parameters, 0 gradients, 126.7 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.949      0.873      0.938      0.803
Speed: 0.2ms preprocess, 14.4ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_10_post_val17
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32703049 parameters, 74176 gradients, 124.6 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.926      0.871      0.929      0.785
Speed: 0.1ms preprocess, 15.1ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_11_pre_val15
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_11_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_11_finetune15
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_11_finetune15/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_11_finetune15
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      12.4G      0.592     0.3808     0.9108        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.912      0.894      0.937      0.794

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      12.5G     0.4274     0.2822     0.8394        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.936       0.88       0.94      0.808

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      11.6G     0.4962     0.3279     0.8649        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.934      0.885      0.942      0.807

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      11.6G     0.4768     0.3227     0.8737         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.946      0.877      0.938      0.807

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      11.5G     0.4901     0.3294     0.8562         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.932      0.884      0.939      0.804

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      11.5G     0.5087     0.3373     0.8861        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.94      0.883      0.939       0.81

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      11.5G     0.5481     0.3556     0.8798         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.919       0.89      0.936      0.804

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      11.6G     0.5694     0.3718     0.8979        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.937      0.888      0.938      0.804

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      11.6G     0.5756     0.3754     0.9038        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.944      0.886      0.939      0.806

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      11.5G     0.6536     0.3981     0.9224        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.95      0.883      0.939      0.809

10 epochs completed in 0.030 hours.
Optimizer stripped from runs/detect/step_11_finetune15/weights/last.pt, 131.4MB
Optimizer stripped from runs/detect/step_11_finetune15/weights/best.pt, 131.4MB

Validating runs/detect/step_11_finetune15/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32703049 parameters, 0 gradients, 124.6 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.94      0.883      0.939      0.809
Speed: 0.1ms preprocess, 4.4ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_11_finetune15
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32703049 parameters, 0 gradients, 124.6 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.932      0.885      0.938      0.803
Speed: 0.2ms preprocess, 15.9ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_11_post_val14
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32669140 parameters, 74176 gradients, 124.6 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.945      0.883      0.942      0.806
Speed: 0.1ms preprocess, 15.9ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_12_pre_val14
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_12_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_12_finetune13
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_12_finetune13/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_12_finetune13
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      12.7G      0.499     0.3319     0.8801        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.933      0.887      0.937      0.809

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      11.6G     0.3689     0.2572     0.8209        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.932      0.886      0.938      0.812

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      11.6G     0.4539      0.302     0.8599        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.949      0.876      0.938       0.81

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      11.6G     0.4334     0.2969     0.8595         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.94      0.893      0.941      0.808

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      11.6G     0.4529     0.3123     0.8466         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.944      0.894      0.941      0.807

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      11.6G     0.4631     0.3128     0.8697        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.95      0.887      0.943      0.809

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      11.6G     0.5213     0.3408     0.8692         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.941      0.891      0.939      0.811

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      11.7G      0.539     0.3604     0.8853        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.939      0.892       0.94       0.81

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      11.7G     0.5515      0.358     0.8976        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.929      0.897       0.94      0.813

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      11.6G     0.6402     0.3891     0.9106        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.941      0.897      0.943      0.816

10 epochs completed in 0.029 hours.
Optimizer stripped from runs/detect/step_12_finetune13/weights/last.pt, 131.3MB
Optimizer stripped from runs/detect/step_12_finetune13/weights/best.pt, 131.3MB

Validating runs/detect/step_12_finetune13/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32669140 parameters, 0 gradients, 124.6 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.942      0.897      0.944      0.816
Speed: 0.1ms preprocess, 4.2ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_12_finetune13
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32669140 parameters, 0 gradients, 124.6 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.937      0.892      0.941      0.811
Speed: 0.2ms preprocess, 16.7ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_12_post_val13
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 74176 gradients, 123.4 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.939      0.886       0.94      0.805
Speed: 0.2ms preprocess, 16.6ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_13_pre_val13
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_13_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_13_finetune13
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_13_finetune13/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_13_finetune13
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      12.1G     0.5096      0.332     0.8815        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.935      0.891      0.943      0.812

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      11.6G     0.3826     0.2558     0.8262        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.956       0.88      0.949      0.813

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      11.4G     0.4512     0.3126     0.8555        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.948      0.883      0.945      0.814

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      11.5G     0.4423      0.299     0.8562         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.95      0.882      0.944      0.811

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      11.5G     0.4557     0.3122     0.8492         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.935      0.896      0.945      0.804

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      11.5G     0.4734     0.3233     0.8728        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.93      0.892      0.941      0.805

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      11.5G     0.5367      0.352     0.8796         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.934      0.886      0.942      0.804

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      11.5G     0.5403     0.3508     0.8848        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.941      0.886      0.943       0.81

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      11.5G     0.5416     0.3534      0.889        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.944      0.893      0.947      0.814

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      11.5G     0.6465     0.4027     0.9187        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.945      0.891      0.947      0.818

10 epochs completed in 0.030 hours.
Optimizer stripped from runs/detect/step_13_finetune13/weights/last.pt, 130.3MB
Optimizer stripped from runs/detect/step_13_finetune13/weights/best.pt, 130.3MB

Validating runs/detect/step_13_finetune13/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 0 gradients, 123.4 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.944      0.891      0.947      0.819
Speed: 0.1ms preprocess, 4.2ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_13_finetune13
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 0 gradients, 123.4 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.951      0.887      0.946      0.815
Speed: 0.2ms preprocess, 17.3ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_13_post_val13
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 74176 gradients, 123.4 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.951      0.886      0.945      0.815
Speed: 0.2ms preprocess, 17.4ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_14_pre_val13
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
yolo/engine/trainer: task=detect, mode=train, model=None, data=coco128.yaml, epochs=10, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=step_14_finetune, exist_ok=False, pretrained=True, optimizer=auto, verbose=False, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/step_14_finetune13
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgr
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
Plotting labels to runs/detect/step_14_finetune13/labels.jpg... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 105 weight(decay=0.0), 112 weight(decay=0.0005), 111 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to runs/detect/step_14_finetune13
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       1/10      11.4G     0.4922     0.3236     0.8733        122        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.955      0.882      0.946      0.822

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       2/10      11.4G     0.3523     0.2478     0.8197        112        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.953       0.89      0.943      0.817

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       3/10      11.4G     0.4283     0.2858     0.8487        116        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929       0.95       0.89      0.941       0.82

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       4/10      11.4G      0.408     0.2829     0.8446         68        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.953      0.886      0.945      0.819

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       5/10      11.4G     0.4299     0.2959     0.8395         96        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.943      0.902      0.944      0.823

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       6/10      11.4G     0.4327     0.3007     0.8581        120        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.946      0.889      0.943       0.82

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       7/10      11.4G     0.4762     0.3178     0.8584         69        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.944      0.892      0.945      0.814

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       8/10      11.4G     0.5052     0.3337     0.8724        141        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.946      0.893      0.944      0.819

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
       9/10      11.4G     0.5167     0.3323     0.8776        104        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.943      0.898      0.944      0.819

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
      10/10      11.4G     0.5994     0.3758     0.8965        170        640: 100%|██████████| 8/8 [00:02
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.952      0.893      0.946      0.822

10 epochs completed in 0.017 hours.
Optimizer stripped from runs/detect/step_14_finetune13/weights/last.pt, 130.3MB
Optimizer stripped from runs/detect/step_14_finetune13/weights/best.pt, 130.3MB

Validating runs/detect/step_14_finetune13/weights/best.pt...
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 0 gradients, 123.4 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.943      0.902      0.944      0.823
Speed: 0.1ms preprocess, 4.2ms inference, 0.0ms loss, 0.2ms postprocess per image
Results saved to runs/detect/step_14_finetune13
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 0 gradients, 123.4 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrou
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████|
                   all        128        929      0.947      0.899      0.943      0.819
Speed: 0.2ms preprocess, 17.2ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/step_14_post_val13
Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CPU
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 0 gradients, 123.4 GFLOPs

PyTorch: starting from runs/detect/step_14_finetune13/weights/best.pt with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (124.2 MB)

ONNX: starting export with onnx 1.16.0 opset 17...
ONNX: export success ✅ 2.6s, saved as runs/detect/step_14_finetune13/weights/best.onnx (123.9 MB)

Export complete (3.5s)
Results saved to /home/HubensN/fasterai/nbs/runs/detect/step_14_finetune13/weights
Predict:         yolo predict task=detect model=runs/detect/step_14_finetune13/weights/best.onnx imgsz=640 
Validate:        yolo val task=detect model=runs/detect/step_14_finetune13/weights/best.onnx imgsz=640 data=/home/HubensN/miniconda3/envs/fasterai/lib/python3.9/site-packages/ultralytics/datasets/coco128.yaml 
Visualize:       https://netron.app

Before Pruning: MACs= 82.72641 G, #Params= 43.69152 M, mAP= 0.65869
Before Pruning: MACs= 82.72641 G, #Params= 43.69152 M, mAP= 0.75438
Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
0.27046189978777607
After Pruning
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 1: MACs=81.8125668 G, #Params=43.348966 M, mAP=0.7428735001565969, speed up=1.0111699172357467
After fine-tuning
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.7869655326484724
After post fine-tuning validation
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
0.5179586515491672
After Pruning
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 2: MACs=81.5020432 G, #Params=43.105009 M, mAP=0.7879549975477981, speed up=1.0150224847369225
After fine-tuning
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.804165683147925
After post fine-tuning validation
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
0.9769531739708688
After Pruning
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 3: MACs=80.7933916 G, #Params=42.735334 M, mAP=0.7940590327289188, speed up=1.0239254072854147
After fine-tuning
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8133375576554807
After post fine-tuning validation
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
1.7924759478681729
After Pruning
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 62, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 63, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 62, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 4: MACs=79.5541908 G, #Params=42.117503 M, mAP=0.8043294271876973, speed up=1.0398749024796818
After fine-tuning
Model Conv2d(3, 62, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 62, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8242263974664863
After post fine-tuning validation
Model Conv2d(3, 62, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 62, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
3.1368842425083825
After Pruning
Model Conv2d(3, 62, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 61, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 62, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 61, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 5: MACs=77.3600192 G, #Params=40.942254 M, mAP=0.7920074671210469, speed up=1.0693690003634333
After fine-tuning
Model Conv2d(3, 61, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 61, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.820206153122929
After post fine-tuning validation
Model Conv2d(3, 61, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 61, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
5.101267981852869
After Pruning
Model Conv2d(3, 61, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 60, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 61, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 60, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 6: MACs=74.8418608 G, #Params=39.477376 M, mAP=0.7891253582912163, speed up=1.1053494062777232
After fine-tuning
Model Conv2d(3, 60, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 60, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8197008467567249
After post fine-tuning validation
Model Conv2d(3, 60, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 60, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
7.518590641324997
After Pruning
Model Conv2d(3, 60, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 59, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 60, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 59, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 7: MACs=71.732976 G, #Params=37.730325 M, mAP=0.7604747253578685, speed up=1.1532549046898597
After fine-tuning
Model Conv2d(3, 59, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 59, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8082043641470185
After post fine-tuning validation
Model Conv2d(3, 59, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 59, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
9.935913300797125
After Pruning
Model Conv2d(3, 59, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 57, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 59, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 57, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 8: MACs=68.4860368 G, #Params=36.016747 M, mAP=0.7439133908787243, speed up=1.207930992438447
After fine-tuning
Model Conv2d(3, 57, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 57, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8047311680941978
After post fine-tuning validation
Model Conv2d(3, 57, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 57, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
11.900297040141613
After Pruning
Model Conv2d(3, 57, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 56, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 57, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 56, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 9: MACs=65.8289424 G, #Params=34.604045 M, mAP=0.746892685800743, speed up=1.2566874597092115
After fine-tuning
Model Conv2d(3, 56, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 56, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8042272329558376
After post fine-tuning validation
Model Conv2d(3, 56, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 56, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
13.24470533478182
After Pruning
Model Conv2d(3, 56, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 56, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 10: MACs=64.3900056 G, #Params=33.768007 M, mAP=0.77353892505729, speed up=1.2847709148203583
After fine-tuning
Model Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8042200149576527
After post fine-tuning validation
Model Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
14.060228108679125
After Pruning
Model Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 11: MACs=63.4942128 G, #Params=33.230145 M, mAP=0.7824563352367453, speed up=1.302896795658832
After fine-tuning
Model Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8030008974391184
After post fine-tuning validation
Model Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
14.519222631100824
After Pruning
Model Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 55, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 12: MACs=62.4345712 G, #Params=32.723122 M, mAP=0.7849986248769537, speed up=1.3250096030130178
After fine-tuning
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8028105881367777
After post fine-tuning validation
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
14.766719382862217
After Pruning
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 13: MACs=62.4070664 G, #Params=32.689204 M, mAP=0.8058915915724488, speed up=1.325593577332454
After fine-tuning
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8111457220640336
After post fine-tuning validation
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
14.89709551315643
After Pruning
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 14: MACs=61.8488912 G, #Params=32.436843 M, mAP=0.8050285863373501, speed up=1.3375568226839933
After fine-tuning
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8146198835662797
After post fine-tuning validation
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
14.96493134246744
After Pruning
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After post-pruning Validation
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After pruning iter 15: MACs=61.8488912 G, #Params=32.436843 M, mAP=0.8153698637584581, speed up=1.3375568226839933
After fine-tuning
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
After fine tuning mAP=0.8192818206570706
After post fine-tuning validation
Model Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
Pruner Conv2d(3, 54, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)

Post-Training Checks

model = YOLO('/home/HubensN/fasterai/nbs/runs/detect/step_14_finetune4/weights/best.pt')

base_macs, base_nparams = tp.utils.count_ops_and_params(model.model, example_inputs); base_macs, base_nparams

(57692198400.0, 30077028)

results = model.val(
                data='coco128.yaml',
                batch=1,
                imgsz=640,
                verbose=False,
            )

Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CUDA:0 (NVIDIA GeForce RTX 3090, 24268MiB)
YOLOv8l summary (fused): 285 layers, 30057792 parameters, 0 gradients, 115.1 GFLOPs
val: Scanning /home/HubensN/fasterai/nbs/datasets/coco128/labels/tra
                 Class     Images  Instances      Box(P          R  
                   all        128        929      0.917      0.907      0.945      0.809
Speed: 0.2ms preprocess, 24.4ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to runs/detect/val35

results

ultralytics.yolo.utils.metrics.DetMetrics object with attributes:

ap_class_index: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 11, 13, 14, 15, 16, 17, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 71, 72, 73, 74, 75, 76, 77, 79])
box: ultralytics.yolo.utils.metrics.Metric object
confusion_matrix: <ultralytics.yolo.utils.metrics.ConfusionMatrix object>
fitness: 0.8221835652536718
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([    0.75668,     0.47387,      0.3594,     0.91994,     0.94661,     0.90211,     0.94289,     0.68531,     0.68927,     0.34436,     0.80851,      0.8955,     0.80851,     0.86091,     0.84563,     0.96863,     0.89911,      0.9501,     0.80851,     0.80851,     0.88959,       0.995,       0.995,     0.93382,
            0.8273,     0.84511,      0.6686,     0.77723,     0.80558,      0.8234,      0.8955,     0.68357,     0.40784,     0.61583,     0.67229,     0.39977,     0.87814,     0.80851,     0.60498,     0.56705,       0.726,     0.76821,     0.76074,     0.56956,     0.72918,     0.79545,       0.995,     0.80851,
             0.995,      0.8713,     0.73808,      0.7727,       0.995,     0.94354,     0.96781,      0.9387,     0.81318,     0.93981,     0.90203,       0.995,     0.80108,     0.90138,       0.995,      0.9641,     0.55702,     0.69798,     0.80851,     0.74254,     0.90157,     0.90353,     0.80851,     0.80032,
           0.92735,     0.62032,     0.86949,     0.92389,       0.995,      0.9143,     0.80851,     0.94696])
names: {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
plot: True
results_dict: {'metrics/precision(B)': 0.9173606393289034, 'metrics/recall(B)': 0.9071337022261394, 'metrics/mAP50(B)': 0.9452653330065343, 'metrics/mAP50-95(B)': 0.8085078132811314, 'fitness': 0.8221835652536718}
save_dir: Path('runs/detect/val35')
speed: {'preprocess': 0.17499923706054688, 'inference': 24.442605674266815, 'loss': 0.0046156346797943115, 'postprocess': 0.376259908080101}

model.export(format = 'onnx', half = True)

Ultralytics YOLOv8.0.132 🚀 Python-3.9.0 torch-2.2.1 CPU
WARNING ⚠️ half=True only compatible with GPU export, i.e. use device=0
YOLOv8l summary (fused): 268 layers, 43668288 parameters, 0 gradients, 165.2 GFLOPs

PyTorch: starting from yolov8l.pt with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (83.7 MB)

ONNX: starting export with onnx 1.16.0 opset 17...
ONNX: export success ✅ 2.8s, saved as yolov8l.onnx (166.8 MB)

Export complete (4.0s)
Results saved to /home/HubensN/fasterai/nbs
Predict:         yolo predict task=detect model=yolov8l.onnx imgsz=640 
Validate:        yolo val task=detect model=yolov8l.onnx imgsz=640 data=coco.yaml 
Visualize:       https://netron.app

'yolov8l.onnx'