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)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43668288 parameters, 0 gradients, 165.2 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.731 0.768 0.828 0.658
Speed: 0.1ms preprocess, 7.5ms inference, 0.0ms loss, 0.8ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/val39
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
Before Pruning: MACs= 82.72641 G, #Params= 43.69152 M, mAP= 0.65799
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=0, 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=/home/HubensN/ultralytics/runs/detect/train33
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/train33/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 /home/HubensN/ultralytics/runs/detect/train33
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 12G 0.9535 0.9388 1.178
Class Images Instances Box(P
all 128 929 0.824 0.712 0.832 0.665
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 12.2G 0.9172 0.8093 1.161
Class Images Instances Box(P
all 128 929 0.836 0.755 0.842 0.682
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 11.6G 0.9013 0.7191 1.099
Class Images Instances Box(P
all 128 929 0.874 0.756 0.852 0.691
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 12.1G 0.9249 0.7495 1.118
Class Images Instances Box(P
all 128 929 0.858 0.793 0.863 0.699
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 11.8G 0.8319 0.6796 1.082
Class Images Instances Box(P
all 128 929 0.859 0.81 0.882 0.717
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 11.9G 0.8412 0.6843 1.096
Class Images Instances Box(P
all 128 929 0.848 0.828 0.892 0.729
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 12.1G 0.814 0.6248 1.067
Class Images Instances Box(P
all 128 929 0.88 0.825 0.898 0.737
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 11.9G 0.8093 0.615 1.067
Class Images Instances Box(P
all 128 929 0.905 0.819 0.897 0.748
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 11.8G 0.7573 0.5698 1.036
Class Images Instances Box(P
all 128 929 0.905 0.83 0.901 0.755
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 12.1G 0.7934 0.6052 1.075
Class Images Instances Box(P
all 128 929 0.894 0.835 0.9 0.758
10 epochs completed in 0.029 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/train33/weights/last.pt, 175.3MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/train33/weights/best.pt, 175.3MB
Validating /home/HubensN/ultralytics/runs/detect/train33/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 43668288 parameters, 0 gradients, 165.2 GFLOPs
Class Images Instances Box(P
all 128 929 0.894 0.835 0.9 0.758
Speed: 0.1ms preprocess, 4.2ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/train33
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43668288 parameters, 0 gradients, 165.2 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.892 0.826 0.901 0.751
Speed: 0.2ms preprocess, 10.8ms inference, 0.0ms loss, 0.5ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/baseline_val28
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 43081939 parameters, 74176 gradients, 162.7 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.9 0.825 0.906 0.734
Speed: 0.2ms preprocess, 13.3ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_0_pre_val17
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_0_finetune16
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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=81.5020432 G, #Params=43.105009 M, mAP=0.7335178809742455, speed up=1.0150224847369225
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_0_finetune16/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 /home/HubensN/ultralytics/runs/detect/step_0_finetune16
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 12.7G 0.7839 0.65 1.053
Class Images Instances Box(P
all 128 929 0.886 0.849 0.906 0.752
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 12.7G 0.7319 0.499 1.036
Class Images Instances Box(P
all 128 929 0.901 0.856 0.914 0.756
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 12.5G 0.7449 0.4985 1.014
Class Images Instances Box(P
all 128 929 0.896 0.864 0.915 0.76
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 12.6G 0.7679 0.5147 1.027
Class Images Instances Box(P
all 128 929 0.896 0.864 0.912 0.758
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 12.7G 0.6996 0.4846 1.015
Class Images Instances Box(P
all 128 929 0.909 0.863 0.915 0.761
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 12.8G 0.7258 0.516 1.026
Class Images Instances Box(P
all 128 929 0.908 0.869 0.916 0.766
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 12.8G 0.7541 0.5142 1.024
Class Images Instances Box(P
all 128 929 0.919 0.863 0.915 0.771
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 12.8G 0.7233 0.5094 1.014
Class Images Instances Box(P
all 128 929 0.912 0.868 0.917 0.776
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 12.8G 0.69 0.4918 0.9917
Class Images Instances Box(P
all 128 929 0.909 0.869 0.919 0.778
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 12.8G 0.7366 0.5463 1.035
Class Images Instances Box(P
all 128 929 0.919 0.865 0.918 0.779
10 epochs completed in 0.029 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_0_finetune16/weights/last.pt, 173.0MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_0_finetune16/weights/best.pt, 173.0MB
Validating /home/HubensN/ultralytics/runs/detect/step_0_finetune16/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 43081939 parameters, 0 gradients, 162.7 GFLOPs
Class Images Instances Box(P
all 128 929 0.919 0.865 0.918 0.778
Speed: 0.1ms preprocess, 4.5ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_0_finetune16
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
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=80.7933916 G, #Params=42.735334 M, mAP=0.7671079746246661, speed up=1.0239254072854147
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_1_finetune9/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 /home/HubensN/ultralytics/runs/detect/step_1_finetune9
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 13.6G 0.6788 0.5264 0.9988
Class Images Instances Box(P
all 128 929 0.91 0.873 0.918 0.777
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 12.7G 0.6175 0.4036 0.9705
Class Images Instances Box(P
all 128 929 0.93 0.863 0.92 0.783
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 12.6G 0.646 0.4189 0.9632
Class Images Instances Box(P
all 128 929 0.918 0.863 0.922 0.782
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 12.7G 0.6623 0.4434 0.9798
Class Images Instances Box(P
all 128 929 0.925 0.86 0.923 0.785
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 12.8G 0.6136 0.4164 0.9643
Class Images Instances Box(P
all 128 929 0.924 0.861 0.926 0.787
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 12.8G 0.6421 0.4466 0.9727
Class Images Instances Box(P
all 128 929 0.928 0.862 0.928 0.794
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 12.8G 0.6767 0.457 0.9847
Class Images Instances Box(P
all 128 929 0.934 0.865 0.931 0.795
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 12.9G 0.6599 0.4573 0.9827
Class Images Instances Box(P
all 128 929 0.924 0.877 0.934 0.797
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 12.8G 0.6616 0.4547 0.9732
Class Images Instances Box(P
all 128 929 0.928 0.872 0.935 0.802
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 12.7G 0.6991 0.5169 1.008
Class Images Instances Box(P
all 128 929 0.934 0.869 0.936 0.803
10 epochs completed in 0.039 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_1_finetune9/weights/last.pt, 171.5MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_1_finetune9/weights/best.pt, 171.5MB
Validating /home/HubensN/ultralytics/runs/detect/step_1_finetune9/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 42712366 parameters, 0 gradients, 161.3 GFLOPs
Class Images Instances Box(P
all 128 929 0.933 0.871 0.935 0.803
Speed: 0.1ms preprocess, 4.5ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_1_finetune9
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
After fine tuning mAP=0.7924603103645905
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)
0.009769531739708686
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)
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 42094706 parameters, 74176 gradients, 158.8 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.908 0.868 0.924 0.779
Speed: 0.1ms preprocess, 13.4ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_2_pre_val9
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_2_finetune9
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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 3: MACs=79.5541908 G, #Params=42.117503 M, mAP=0.7792330059497833, speed up=1.0398749024796818
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_2_finetune9/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 /home/HubensN/ultralytics/runs/detect/step_2_finetune9
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 13G 0.6418 0.5222 0.9767
Class Images Instances Box(P
all 128 929 0.914 0.885 0.929 0.796
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 12.6G 0.5453 0.368 0.9336
Class Images Instances Box(P
all 128 929 0.92 0.884 0.938 0.802
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 12.5G 0.5814 0.3836 0.9312
Class Images Instances Box(P
all 128 929 0.925 0.884 0.937 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 12.6G 0.6058 0.3976 0.9452
Class Images Instances Box(P
all 128 929 0.949 0.87 0.937 0.807
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 12.6G 0.5516 0.3755 0.9373
Class Images Instances Box(P
all 128 929 0.948 0.867 0.939 0.81
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 12.7G 0.5859 0.4033 0.9496
Class Images Instances Box(P
all 128 929 0.929 0.887 0.942 0.811
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 12.6G 0.635 0.4225 0.9698
Class Images Instances Box(P
all 128 929 0.942 0.883 0.941 0.809
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 12.7G 0.6191 0.4212 0.9639
Class Images Instances Box(P
all 128 929 0.937 0.888 0.941 0.815
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 12.7G 0.631 0.4236 0.9608
Class Images Instances Box(P
all 128 929 0.936 0.889 0.942 0.82
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 12.6G 0.6808 0.5085 1.003
Class Images Instances Box(P
all 128 929 0.935 0.889 0.942 0.821
10 epochs completed in 0.040 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_2_finetune9/weights/last.pt, 169.0MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_2_finetune9/weights/best.pt, 169.0MB
Validating /home/HubensN/ultralytics/runs/detect/step_2_finetune9/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 42094706 parameters, 0 gradients, 158.8 GFLOPs
Class Images Instances Box(P
all 128 929 0.935 0.89 0.942 0.821
Speed: 0.1ms preprocess, 4.4ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_2_finetune9
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
After fine tuning mAP=0.8108994344622686
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)
0.017924759478681728
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)
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 40919781 parameters, 74176 gradients, 154.4 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.911 0.866 0.928 0.771
Speed: 0.2ms preprocess, 13.3ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_3_pre_val8
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_3_finetune8
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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 4: MACs=77.3600192 G, #Params=40.942254 M, mAP=0.7714194636333309, speed up=1.0693690003634333
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_3_finetune8/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 /home/HubensN/ultralytics/runs/detect/step_3_finetune8
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 12.6G 0.6354 0.4858 0.9676
Class Images Instances Box(P
all 128 929 0.931 0.87 0.936 0.788
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 12.6G 0.5377 0.3467 0.9207
Class Images Instances Box(P
all 128 929 0.933 0.878 0.939 0.795
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 12.5G 0.588 0.3751 0.9277
Class Images Instances Box(P
all 128 929 0.939 0.875 0.939 0.801
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 12.6G 0.5795 0.3872 0.9303
Class Images Instances Box(P
all 128 929 0.944 0.87 0.937 0.805
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 12.6G 0.5511 0.3675 0.9235
Class Images Instances Box(P
all 128 929 0.943 0.872 0.937 0.812
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 12.7G 0.5638 0.3974 0.9317
Class Images Instances Box(P
all 128 929 0.925 0.879 0.939 0.812
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 12.6G 0.6183 0.4048 0.9559
Class Images Instances Box(P
all 128 929 0.915 0.885 0.939 0.812
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 12.7G 0.6064 0.4156 0.9551
Class Images Instances Box(P
all 128 929 0.924 0.888 0.94 0.81
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 12.7G 0.6126 0.4111 0.9473
Class Images Instances Box(P
all 128 929 0.928 0.898 0.943 0.819
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 12.6G 0.6751 0.4922 1.001
Class Images Instances Box(P
all 128 929 0.922 0.898 0.942 0.82
10 epochs completed in 0.027 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_3_finetune8/weights/last.pt, 164.3MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_3_finetune8/weights/best.pt, 164.3MB
Validating /home/HubensN/ultralytics/runs/detect/step_3_finetune8/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 40919781 parameters, 0 gradients, 154.4 GFLOPs
Class Images Instances Box(P
all 128 929 0.922 0.898 0.942 0.82
Speed: 0.1ms preprocess, 4.5ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_3_finetune8
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 39455305 parameters, 74176 gradients, 149.4 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.922 0.853 0.93 0.771
Speed: 0.2ms preprocess, 13.4ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_4_pre_val7
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_4_finetune7
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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 5: MACs=74.8418608 G, #Params=39.477376 M, mAP=0.7709511018059185, speed up=1.1053494062777232
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_4_finetune7/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 /home/HubensN/ultralytics/runs/detect/step_4_finetune7
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 12.8G 0.6393 0.4996 0.9632
Class Images Instances Box(P
all 128 929 0.91 0.879 0.934 0.787
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 12.7G 0.5385 0.3464 0.9142
Class Images Instances Box(P
all 128 929 0.915 0.879 0.936 0.801
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 12.5G 0.5725 0.3758 0.9139
Class Images Instances Box(P
all 128 929 0.916 0.899 0.943 0.805
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 12.6G 0.5814 0.3863 0.9212
Class Images Instances Box(P
all 128 929 0.923 0.9 0.948 0.812
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 12.7G 0.5392 0.3637 0.9171
Class Images Instances Box(P
all 128 929 0.924 0.901 0.948 0.811
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 12.9G 0.5582 0.4023 0.9233
Class Images Instances Box(P
all 128 929 0.93 0.898 0.95 0.813
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 12.8G 0.6234 0.4091 0.9585
Class Images Instances Box(P
all 128 929 0.927 0.905 0.951 0.816
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 12.8G 0.6062 0.4115 0.9444
Class Images Instances Box(P
all 128 929 0.922 0.908 0.953 0.816
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 12.7G 0.6105 0.4111 0.9411
Class Images Instances Box(P
all 128 929 0.922 0.906 0.949 0.818
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 12.6G 0.6834 0.498 1.001
Class Images Instances Box(P
all 128 929 0.926 0.906 0.95 0.819
10 epochs completed in 0.027 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_4_finetune7/weights/last.pt, 158.5MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_4_finetune7/weights/best.pt, 158.5MB
Validating /home/HubensN/ultralytics/runs/detect/step_4_finetune7/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 39455305 parameters, 0 gradients, 149.4 GFLOPs
Class Images Instances Box(P
all 128 929 0.927 0.905 0.95 0.819
Speed: 0.1ms preprocess, 4.5ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_4_finetune7
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
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 6: MACs=71.732976 G, #Params=37.730325 M, mAP=0.7640629035267851, speed up=1.1532549046898597
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_5_finetune6/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 /home/HubensN/ultralytics/runs/detect/step_5_finetune6
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 13.1G 0.6622 0.5187 0.9815
Class Images Instances Box(P
all 128 929 0.911 0.878 0.936 0.782
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 12.7G 0.5412 0.3602 0.91
Class Images Instances Box(P
all 128 929 0.908 0.899 0.94 0.793
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 12.6G 0.5946 0.3883 0.9197
Class Images Instances Box(P
all 128 929 0.918 0.893 0.941 0.799
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 12.7G 0.5856 0.396 0.9263
Class Images Instances Box(P
all 128 929 0.937 0.88 0.942 0.805
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 12.7G 0.5495 0.3637 0.9176
Class Images Instances Box(P
all 128 929 0.93 0.873 0.944 0.808
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 12.8G 0.5562 0.396 0.9231
Class Images Instances Box(P
all 128 929 0.938 0.872 0.945 0.805
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 12.7G 0.6301 0.4115 0.9537
Class Images Instances Box(P
all 128 929 0.924 0.883 0.942 0.808
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 12.8G 0.6138 0.4093 0.944
Class Images Instances Box(P
all 128 929 0.919 0.894 0.946 0.81
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 12.8G 0.6297 0.4207 0.9478
Class Images Instances Box(P
all 128 929 0.926 0.898 0.949 0.813
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 12.7G 0.703 0.5027 1.009
Class Images Instances Box(P
all 128 929 0.924 0.898 0.95 0.814
10 epochs completed in 0.037 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_5_finetune6/weights/last.pt, 151.5MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_5_finetune6/weights/best.pt, 151.5MB
Validating /home/HubensN/ultralytics/runs/detect/step_5_finetune6/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 37708749 parameters, 0 gradients, 143.2 GFLOPs
Class Images Instances Box(P
all 128 929 0.924 0.898 0.95 0.814
Speed: 0.1ms preprocess, 4.5ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_5_finetune6
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
After fine tuning mAP=0.8104708131787314
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)
0.07518590641324997
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)
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 35995675 parameters, 74176 gradients, 136.7 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.878 0.799 0.907 0.74
Speed: 0.2ms preprocess, 12.7ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_6_pre_val6
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_6_finetune6
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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 7: MACs=68.4860368 G, #Params=36.016747 M, mAP=0.7398590274182758, speed up=1.207930992438447
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_6_finetune6/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 /home/HubensN/ultralytics/runs/detect/step_6_finetune6
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 11.7G 0.6993 0.5584 0.9858
Class Images Instances Box(P
all 128 929 0.902 0.841 0.921 0.764
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 11.8G 0.5531 0.3752 0.9125
Class Images Instances Box(P
all 128 929 0.91 0.849 0.927 0.782
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 11.7G 0.6039 0.4089 0.9216
Class Images Instances Box(P
all 128 929 0.926 0.86 0.93 0.788
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 11.5G 0.6132 0.414 0.9289
Class Images Instances Box(P
all 128 929 0.906 0.882 0.933 0.796
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 11.5G 0.5718 0.383 0.9257
Class Images Instances Box(P
all 128 929 0.909 0.879 0.938 0.789
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 11.9G 0.5734 0.4089 0.9254
Class Images Instances Box(P
all 128 929 0.928 0.877 0.942 0.797
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 11.5G 0.6396 0.4206 0.9589
Class Images Instances Box(P
all 128 929 0.929 0.883 0.945 0.8
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 11.6G 0.6347 0.4307 0.9533
Class Images Instances Box(P
all 128 929 0.932 0.879 0.946 0.807
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 11.6G 0.6589 0.4376 0.9609
Class Images Instances Box(P
all 128 929 0.931 0.885 0.947 0.808
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 11.8G 0.7121 0.5232 1.016
Class Images Instances Box(P
all 128 929 0.93 0.886 0.945 0.81
10 epochs completed in 0.025 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_6_finetune6/weights/last.pt, 144.6MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_6_finetune6/weights/best.pt, 144.6MB
Validating /home/HubensN/ultralytics/runs/detect/step_6_finetune6/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 35995675 parameters, 0 gradients, 136.7 GFLOPs
Class Images Instances Box(P
all 128 929 0.929 0.887 0.945 0.808
Speed: 0.1ms preprocess, 4.7ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_6_finetune6
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
After fine tuning mAP=0.8040926175515907
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)
0.09935913300797124
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)
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 34583399 parameters, 74176 gradients, 131.4 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.915 0.829 0.917 0.733
Speed: 0.1ms preprocess, 12.0ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_7_pre_val5
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_7_finetune5
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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 8: MACs=65.8289424 G, #Params=34.604045 M, mAP=0.7333194226117398, speed up=1.2566874597092115
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_7_finetune5/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 /home/HubensN/ultralytics/runs/detect/step_7_finetune5
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 11.2G 0.6839 0.5488 0.9811
Class Images Instances Box(P
all 128 929 0.931 0.847 0.932 0.764
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 11.5G 0.5602 0.3693 0.9134
Class Images Instances Box(P
all 128 929 0.92 0.869 0.936 0.781
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 11.4G 0.5984 0.3895 0.9188
Class Images Instances Box(P
all 128 929 0.905 0.879 0.936 0.782
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 11.6G 0.6034 0.418 0.9248
Class Images Instances Box(P
all 128 929 0.899 0.886 0.936 0.782
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 11.4G 0.5658 0.3782 0.928
Class Images Instances Box(P
all 128 929 0.925 0.872 0.938 0.788
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 11.7G 0.5917 0.4156 0.9288
Class Images Instances Box(P
all 128 929 0.92 0.882 0.939 0.794
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 11.4G 0.6339 0.4252 0.9516
Class Images Instances Box(P
all 128 929 0.933 0.876 0.944 0.798
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 11.4G 0.6471 0.4304 0.9604
Class Images Instances Box(P
all 128 929 0.933 0.878 0.945 0.8
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 11.4G 0.668 0.4424 0.959
Class Images Instances Box(P
all 128 929 0.927 0.884 0.944 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 11.6G 0.7304 0.5445 1.031
Class Images Instances Box(P
all 128 929 0.919 0.887 0.943 0.802
10 epochs completed in 0.024 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_7_finetune5/weights/last.pt, 139.0MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_7_finetune5/weights/best.pt, 139.0MB
Validating /home/HubensN/ultralytics/runs/detect/step_7_finetune5/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 34583399 parameters, 0 gradients, 131.4 GFLOPs
Class Images Instances Box(P
all 128 929 0.924 0.884 0.944 0.802
Speed: 0.1ms preprocess, 4.1ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_7_finetune5
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
After fine tuning mAP=0.7931599917268005
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)
0.11900297040141611
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)
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 33747610 parameters, 74176 gradients, 128.5 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.919 0.861 0.93 0.769
Speed: 0.2ms preprocess, 11.9ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_8_pre_val5
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_8_finetune5
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
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 9: MACs=64.3900056 G, #Params=33.768007 M, mAP=0.7690853683854553, speed up=1.2847709148203583
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_8_finetune5/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 /home/HubensN/ultralytics/runs/detect/step_8_finetune5
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 12G 0.6504 0.5212 0.9696
Class Images Instances Box(P
all 128 929 0.934 0.87 0.935 0.782
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 11.6G 0.5047 0.3425 0.8995
Class Images Instances Box(P
all 128 929 0.908 0.889 0.936 0.791
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 11.5G 0.5622 0.3725 0.9046
Class Images Instances Box(P
all 128 929 0.913 0.892 0.938 0.788
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 11.7G 0.5558 0.3856 0.9139
Class Images Instances Box(P
all 128 929 0.925 0.894 0.941 0.791
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 11.7G 0.5481 0.3728 0.92
Class Images Instances Box(P
all 128 929 0.92 0.886 0.939 0.79
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 11.2G 0.5576 0.4045 0.9157
Class Images Instances Box(P
all 128 929 0.928 0.888 0.943 0.792
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 11.1G 0.635 0.429 0.9474
Class Images Instances Box(P
all 128 929 0.935 0.891 0.947 0.794
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 11.2G 0.6145 0.4019 0.9449
Class Images Instances Box(P
all 128 929 0.939 0.887 0.949 0.8
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 11.2G 0.6569 0.4243 0.9517
Class Images Instances Box(P
all 128 929 0.945 0.881 0.949 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 11.6G 0.7253 0.5226 1.024
Class Images Instances Box(P
all 128 929 0.945 0.883 0.948 0.802
10 epochs completed in 0.034 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_8_finetune5/weights/last.pt, 135.6MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_8_finetune5/weights/best.pt, 135.6MB
Validating /home/HubensN/ultralytics/runs/detect/step_8_finetune5/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 33747610 parameters, 0 gradients, 128.5 GFLOPs
Class Images Instances Box(P
all 128 929 0.946 0.881 0.949 0.803
Speed: 0.1ms preprocess, 4.1ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_8_finetune5
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 33747610 parameters, 0 gradients, 128.5 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.937 0.875 0.942 0.796
Speed: 0.2ms preprocess, 11.9ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_8_post_val5
After fine tuning mAP=0.7963247276929302
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)
0.1324470533478182
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)
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
YOLOv8l summary (fused): 285 layers, 33209910 parameters, 74176 gradients, 126.7 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.935 0.847 0.931 0.768
Speed: 0.2ms preprocess, 11.3ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_9_pre_val3
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_9_finetune3
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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 10: MACs=63.4942128 G, #Params=33.230145 M, mAP=0.7676251325156896, speed up=1.302896795658832
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_9_finetune3/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 /home/HubensN/ultralytics/runs/detect/step_9_finetune3
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 12.2G 0.627 0.5006 0.9614
Class Images Instances Box(P
all 128 929 0.92 0.879 0.94 0.792
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 12.4G 0.4757 0.3198 0.8867
Class Images Instances Box(P
all 128 929 0.935 0.878 0.944 0.798
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 11.7G 0.5402 0.3563 0.8958
Class Images Instances Box(P
all 128 929 0.931 0.877 0.941 0.798
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 11.8G 0.5459 0.3752 0.9083
Class Images Instances Box(P
all 128 929 0.934 0.882 0.945 0.796
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 11.8G 0.5164 0.3487 0.9079
Class Images Instances Box(P
all 128 929 0.923 0.893 0.943 0.795
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 11.9G 0.5421 0.385 0.9102
Class Images Instances Box(P
all 128 929 0.918 0.896 0.942 0.793
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 11.8G 0.6251 0.4038 0.9468
Class Images Instances Box(P
all 128 929 0.926 0.89 0.945 0.795
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 11.9G 0.5953 0.3998 0.934
Class Images Instances Box(P
all 128 929 0.918 0.905 0.946 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 11.9G 0.6388 0.4184 0.9476
Class Images Instances Box(P
all 128 929 0.92 0.898 0.947 0.802
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 11.7G 0.7187 0.5164 1.018
Class Images Instances Box(P
all 128 929 0.93 0.895 0.947 0.806
10 epochs completed in 0.031 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_9_finetune3/weights/last.pt, 133.4MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_9_finetune3/weights/best.pt, 133.4MB
Validating /home/HubensN/ultralytics/runs/detect/step_9_finetune3/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 33209910 parameters, 0 gradients, 126.7 GFLOPs
Class Images Instances Box(P
all 128 929 0.932 0.893 0.947 0.805
Speed: 0.1ms preprocess, 4.1ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_9_finetune3
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
After fine tuning mAP=0.8009899904343383
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)
0.14060228108679124
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)
YOLOv8l summary (fused): 285 layers, 32703049 parameters, 74176 gradients, 124.6 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.927 0.847 0.937 0.771
Speed: 0.1ms preprocess, 12.6ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_10_pre_val2
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_10_finetune2
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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 11: MACs=62.4345712 G, #Params=32.723122 M, mAP=0.7711514639154989, speed up=1.3250096030130178
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_10_finetune2/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 /home/HubensN/ultralytics/runs/detect/step_10_finetune2
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 12G 0.6334 0.5193 0.9605
Class Images Instances Box(P
all 128 929 0.931 0.852 0.94 0.788
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 11.5G 0.466 0.3067 0.8851
Class Images Instances Box(P
all 128 929 0.942 0.847 0.937 0.796
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 11.4G 0.5156 0.342 0.8852
Class Images Instances Box(P
all 128 929 0.946 0.868 0.938 0.793
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 11.5G 0.5392 0.3641 0.9066
Class Images Instances Box(P
all 128 929 0.934 0.884 0.937 0.791
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 11.5G 0.5117 0.3557 0.9081
Class Images Instances Box(P
all 128 929 0.909 0.894 0.941 0.796
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 11.6G 0.515 0.373 0.9027
Class Images Instances Box(P
all 128 929 0.911 0.889 0.939 0.796
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 11.5G 0.6053 0.4073 0.9379
Class Images Instances Box(P
all 128 929 0.905 0.884 0.939 0.797
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 11.6G 0.5829 0.39 0.9326
Class Images Instances Box(P
all 128 929 0.927 0.881 0.944 0.801
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 11.5G 0.6332 0.4096 0.942
Class Images Instances Box(P
all 128 929 0.929 0.886 0.945 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 11.5G 0.7178 0.5183 1.015
Class Images Instances Box(P
all 128 929 0.941 0.877 0.945 0.808
10 epochs completed in 0.033 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_10_finetune2/weights/last.pt, 131.4MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_10_finetune2/weights/best.pt, 131.4MB
Validating /home/HubensN/ultralytics/runs/detect/step_10_finetune2/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 32703049 parameters, 0 gradients, 124.6 GFLOPs
Class Images Instances Box(P
all 128 929 0.941 0.876 0.945 0.809
Speed: 0.1ms preprocess, 4.1ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_10_finetune2
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
After fine tuning mAP=0.8081809745840371
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)
0.14519222631100823
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)
YOLOv8l summary (fused): 285 layers, 32669140 parameters, 74176 gradients, 124.6 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.917 0.899 0.95 0.81
Speed: 0.2ms preprocess, 12.6ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_11_pre_val2
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_11_finetune2
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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 12: MACs=62.4070664 G, #Params=32.689204 M, mAP=0.8098376035512942, speed up=1.325593577332454
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_11_finetune2/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 /home/HubensN/ultralytics/runs/detect/step_11_finetune2
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 11.5G 0.5624 0.4578 0.9404
Class Images Instances Box(P
all 128 929 0.905 0.907 0.946 0.813
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 11.6G 0.399 0.2726 0.865
Class Images Instances Box(P
all 128 929 0.931 0.879 0.941 0.807
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 11.4G 0.4809 0.3212 0.879
Class Images Instances Box(P
all 128 929 0.929 0.892 0.941 0.811
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 11.5G 0.4806 0.334 0.8879
Class Images Instances Box(P
all 128 929 0.945 0.881 0.94 0.804
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 11.6G 0.4862 0.3288 0.8994
Class Images Instances Box(P
all 128 929 0.938 0.883 0.942 0.806
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 11.6G 0.4887 0.3584 0.893
Class Images Instances Box(P
all 128 929 0.929 0.89 0.943 0.81
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 11.6G 0.5702 0.3791 0.9254
Class Images Instances Box(P
all 128 929 0.937 0.888 0.943 0.815
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 11.6G 0.5531 0.3689 0.9184
Class Images Instances Box(P
all 128 929 0.933 0.896 0.946 0.812
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 11.6G 0.588 0.3884 0.923
Class Images Instances Box(P
all 128 929 0.926 0.9 0.95 0.815
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 11.5G 0.7003 0.4962 1.007
Class Images Instances Box(P
all 128 929 0.927 0.899 0.949 0.814
10 epochs completed in 0.019 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_11_finetune2/weights/last.pt, 131.3MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_11_finetune2/weights/best.pt, 131.3MB
Validating /home/HubensN/ultralytics/runs/detect/step_11_finetune2/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 32669140 parameters, 0 gradients, 124.6 GFLOPs
Class Images Instances Box(P
all 128 929 0.925 0.9 0.95 0.815
Speed: 0.1ms preprocess, 4.0ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_11_finetune2
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
After fine tuning mAP=0.80867019892426
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)
0.14766719382862217
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)
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 74176 gradients, 123.4 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.922 0.884 0.943 0.793
Speed: 0.2ms preprocess, 12.0ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_12_pre_val2
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_12_finetune2
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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=61.8488912 G, #Params=32.436843 M, mAP=0.7929855954557918, speed up=1.3375568226839933
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_12_finetune2/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 /home/HubensN/ultralytics/runs/detect/step_12_finetune2
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 11.7G 0.5728 0.4693 0.9395
Class Images Instances Box(P
all 128 929 0.935 0.885 0.944 0.813
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 11.6G 0.403 0.267 0.8612
Class Images Instances Box(P
all 128 929 0.934 0.876 0.946 0.813
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 11.4G 0.4806 0.3161 0.8771
Class Images Instances Box(P
all 128 929 0.922 0.885 0.945 0.815
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 11.5G 0.4954 0.3358 0.8895
Class Images Instances Box(P
all 128 929 0.923 0.898 0.946 0.81
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 11.5G 0.4868 0.3256 0.8983
Class Images Instances Box(P
all 128 929 0.927 0.889 0.945 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 11.5G 0.5005 0.3485 0.893
Class Images Instances Box(P
all 128 929 0.923 0.901 0.946 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 11.5G 0.5637 0.3694 0.9171
Class Images Instances Box(P
all 128 929 0.923 0.898 0.946 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 11.6G 0.5525 0.361 0.9134
Class Images Instances Box(P
all 128 929 0.94 0.892 0.945 0.809
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 11.6G 0.5873 0.3731 0.923
Class Images Instances Box(P
all 128 929 0.94 0.897 0.945 0.815
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 11.5G 0.7004 0.4849 1.012
Class Images Instances Box(P
all 128 929 0.94 0.898 0.946 0.813
10 epochs completed in 0.030 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_12_finetune2/weights/last.pt, 130.3MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_12_finetune2/weights/best.pt, 130.3MB
Validating /home/HubensN/ultralytics/runs/detect/step_12_finetune2/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 0 gradients, 123.4 GFLOPs
Class Images Instances Box(P
all 128 929 0.921 0.886 0.945 0.814
Speed: 0.1ms preprocess, 4.0ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_12_finetune2
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
After fine tuning mAP=0.81035824268819
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)
0.14897095513156428
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)
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 74176 gradients, 123.4 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.921 0.899 0.946 0.809
Speed: 0.1ms preprocess, 12.1ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_13_pre_val2
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_13_finetune2
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
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=0.8094708031863412, speed up=1.3375568226839933
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_13_finetune2/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 /home/HubensN/ultralytics/runs/detect/step_13_finetune2
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 11.2G 0.5243 0.4358 0.9284
Class Images Instances Box(P
all 128 929 0.922 0.904 0.948 0.817
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 11.3G 0.3797 0.2496 0.8509
Class Images Instances Box(P
all 128 929 0.926 0.903 0.949 0.819
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 11.2G 0.4293 0.2921 0.8642
Class Images Instances Box(P
all 128 929 0.939 0.89 0.948 0.821
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 11.2G 0.4573 0.3141 0.8773
Class Images Instances Box(P
all 128 929 0.936 0.899 0.949 0.816
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 11.3G 0.4667 0.3122 0.8898
Class Images Instances Box(P
all 128 929 0.929 0.897 0.947 0.811
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 11.4G 0.4877 0.3356 0.8894
Class Images Instances Box(P
all 128 929 0.932 0.889 0.945 0.802
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 11.3G 0.5567 0.3642 0.9154
Class Images Instances Box(P
all 128 929 0.937 0.882 0.947 0.807
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 11.4G 0.522 0.3407 0.9033
Class Images Instances Box(P
all 128 929 0.945 0.885 0.947 0.816
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 11.3G 0.59 0.3656 0.9174
Class Images Instances Box(P
all 128 929 0.946 0.884 0.947 0.818
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 11.3G 0.7007 0.488 1.009
Class Images Instances Box(P
all 128 929 0.941 0.887 0.949 0.824
10 epochs completed in 0.020 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_13_finetune2/weights/last.pt, 130.3MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_13_finetune2/weights/best.pt, 130.3MB
Validating /home/HubensN/ultralytics/runs/detect/step_13_finetune2/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 0 gradients, 123.4 GFLOPs
Class Images Instances Box(P
all 128 929 0.941 0.887 0.949 0.823
Speed: 0.1ms preprocess, 4.1ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_13_finetune2
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
After fine tuning mAP=0.8178296276387611
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)
0.14964931342467439
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)
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 74176 gradients, 123.4 GFLOPs
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Class Images Instances Box(P
all 128 929 0.926 0.897 0.949 0.817
Speed: 0.2ms preprocess, 12.0ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_14_pre_val2
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 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=0, 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=/home/HubensN/ultralytics/runs/detect/step_14_finetune2
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
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=0.8171226992562459, speed up=1.3375568226839933
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/utils/checks.py:373: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(True):
AMP: checks passed ✅
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:224: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
self.scaler = amp.GradScaler(enabled=self.amp)
train: Scanning /home/HubensN/datasets/coco128/labels/train
val: Scanning /home/HubensN/datasets/coco128/labels/train20
Plotting labels to /home/HubensN/ultralytics/runs/detect/step_14_finetune2/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 /home/HubensN/ultralytics/runs/detect/step_14_finetune2
Starting training for 10 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/8 [00:00<?, ?it/s]/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/yolo/engine/trainer.py:330: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
with torch.cuda.amp.autocast(self.amp):
1/10 11.3G 0.5084 0.4275 0.9241
Class Images Instances Box(P
all 128 929 0.943 0.886 0.945 0.824
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/10 11.3G 0.3449 0.2386 0.8452
Class Images Instances Box(P
all 128 929 0.939 0.889 0.948 0.826
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/10 11.2G 0.4181 0.2863 0.8581
Class Images Instances Box(P
all 128 929 0.945 0.89 0.948 0.821
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/10 11.5G 0.4197 0.3033 0.8692
Class Images Instances Box(P
all 128 929 0.954 0.903 0.952 0.82
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/10 11.3G 0.4552 0.3053 0.8846
Class Images Instances Box(P
all 128 929 0.949 0.905 0.95 0.819
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/10 11.4G 0.4572 0.3242 0.878
Class Images Instances Box(P
all 128 929 0.941 0.904 0.955 0.816
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/10 11.3G 0.5533 0.3546 0.9156
Class Images Instances Box(P
all 128 929 0.928 0.905 0.948 0.818
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/10 11.7G 0.5238 0.3388 0.9018
Class Images Instances Box(P
all 128 929 0.934 0.909 0.949 0.82
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/10 11.6G 0.5513 0.3486 0.9081
Class Images Instances Box(P
all 128 929 0.943 0.912 0.952 0.821
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/10 11.3G 0.6869 0.468 0.9985
Class Images Instances Box(P
all 128 929 0.944 0.913 0.952 0.82
10 epochs completed in 0.018 hours.
/tmp/ipykernel_4010773/2763320149.py:114: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
x = torch.load(f, map_location=torch.device('cpu'))
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_14_finetune2/weights/last.pt, 130.3MB
Optimizer stripped from /home/HubensN/ultralytics/runs/detect/step_14_finetune2/weights/best.pt, 130.3MB
Validating /home/HubensN/ultralytics/runs/detect/step_14_finetune2/weights/best.pt...
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 3090, 24253MiB)
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
YOLOv8l summary (fused): 285 layers, 32416863 parameters, 0 gradients, 123.4 GFLOPs
Class Images Instances Box(P
all 128 929 0.94 0.889 0.948 0.826
Speed: 0.1ms preprocess, 4.1ms inference, 0.0ms loss, 0.3ms postprocess per image
Results saved to /home/HubensN/ultralytics/runs/detect/step_14_finetune2
/home/HubensN/miniconda3/envs/fasterai20/lib/python3.10/site-packages/ultralytics/nn/tasks.py:518: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
return torch.load(file, map_location='cpu'), file # load
Ultralytics YOLOv8.0.124 🚀 Python-3.10.15 torch-2.5.1+cu124 CPU
WARNING ⚠️ half=True only compatible with GPU export, i.e. use device=0
KeyboardInterrupt