YOLO模型：16系显卡用户关闭amp和half后指标恢复（引用[4]）。 关闭了，没用啊 (ultralytics) C:\Users\86187\ultralytics-8.1.0>C:/Users/86187/miniconda3/envs/ultralytics/python.exe c:/Users/86187/ultralytics-8.1.0/tarin.py
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 8210 ultralytics.nn.Attention.CBAM.CBAM [256, 3]
10 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
23 [16, 19, 22] 1 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]]
YOLOv8n-CBAM summary: 236 layers, 3019448 parameters, 3019432 gradients, 8.2 GFLOPs
New https://pypi.org/project/ultralytics/8.3.174 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.0 🚀 Python-3.9.23 torch-1.12.1+cu116 CUDA:0 (NVIDIA GeForce GTX 1650, 4096MiB)
WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.
engine\trainer: task=detect, mode=train, model=C:\Users\86187\ultralytics-8.1.0\ultralytics\cfg\models\v8\yolov8n-CBAM.yaml, data=C:\Users\86187\ultralytics-8.1.0\tomato_train.yaml, epochs=5, time=None, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=0, project=None, name=train17, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=None, amp=False, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs\detect\train17
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 8210 ultralytics.nn.Attention.CBAM.CBAM [256, 3]
10 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
23 [16, 19, 22] 1 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]]
YOLOv8n-CBAM summary: 236 layers, 3019448 parameters, 3019432 gradients, 8.2 GFLOPs
TensorBoard: Start with 'tensorboard --logdir runs\detect\train17', view at http://localhost:6006/
Freezing layer 'model.23.dfl.conv.weight'
train: Scanning C:\Users\86187\ultralytics-8.1.0\datasets\tomato\labels\train.cache... 392 images, 0 backgrounds, 0 corrupt: 100%|██████████|
val: Scanning C:\Users\86187\ultralytics-8.1.0\datasets\tomato\labels\val.cache... 128 images, 0 backgrounds, 0 corrupt: 100%|██████████| 128/
Plotting labels to runs\detect\train17\labels.jpg...
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically...
optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 57 weight(decay=0.0), 67 weight(decay=0.0005), 63 bias(decay=0.0)
5 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/5 3.65G 4.558 4.098 4.315 411 640: 4%|▍ | 1/25 [00:08<03:35, 8.98s/it]
Traceback (most recent call last):
File "c:\Users\86187\ultralytics-8.1.0\tarin.py", line 17, in <module>
results = model.train(data=r'C:\Users\86187\ultralytics-8.1.0\tomato_train.yaml',
File "c:\Users\86187\ultralytics-8.1.0\ultralytics\engine\model.py", line 390, in train
self.trainer.train()
File "c:\Users\86187\ultralytics-8.1.0\ultralytics\engine\trainer.py", line 208, in train
self._do_train(world_size)
File "c:\Users\86187\ultralytics-8.1.0\ultralytics\engine\trainer.py", line 379, in _do_train
self.loss, self.loss_items = self.model(batch)
File "C:\Users\86187\miniconda3\envs\ultralytics\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "c:\Users\86187\ultralytics-8.1.0\ultralytics\nn\tasks.py", line 81, in forward
return self.loss(x, *args, **kwargs)
File "c:\Users\86187\ultralytics-8.1.0\ultralytics\nn\tasks.py", line 260, in loss
return self.criterion(preds, batch)
File "c:\Users\86187\ultralytics-8.1.0\ultralytics\utils\loss.py", line 220, in __call__
_, target_bboxes, target_scores, fg_mask, _ = self.assigner(
File "C:\Users\86187\miniconda3\envs\ultralytics\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Users\86187\miniconda3\envs\ultralytics\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "c:\Users\86187\ultralytics-8.1.0\ultralytics\utils\tal.py", line 72, in forward
mask_pos, align_metric, overlaps = self.get_pos_mask(
File "c:\Users\86187\ultralytics-8.1.0\ultralytics\utils\tal.py", line 92, in get_pos_mask
mask_in_gts = self.select_candidates_in_gts(anc_points, gt_bboxes)
File "c:\Users\86187\ultralytics-8.1.0\ultralytics\utils\tal.py", line 227, in select_candidates_in_gts
bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1)
RuntimeError: CUDA out of memory. Tried to allocate 158.00 MiB (GPU 0; 4.00 GiB total capacity; 3.25 GiB already allocated; 0 bytes free; 3.45
GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
(ultralytics) C:\Users\86187\ultralytics-8.1.0>C:/Users/86187/miniconda3/envs/ultralytics/python.exe c:/Users/86187/ultralytics-8.1.0/tarin.py
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 8210 ultralytics.nn.Attention.CBAM.CBAM [256, 3]
10 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
23 [16, 19, 22] 1 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]]
YOLOv8n-CBAM summary: 236 layers, 3019448 parameters, 3019432 gradients, 8.2 GFLOPs
New https://pypi.org/project/ultralytics/8.3.174 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.0 🚀 Python-3.9.23 torch-1.12.1+cu116 CUDA:0 (NVIDIA GeForce GTX 1650, 4096MiB)
WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.
engine\trainer: task=detect, mode=train, model=C:\Users\86187\ultralytics-8.1.0\ultralytics\cfg\models\v8\yolov8n-CBAM.yaml, data=C:\Users\86187\ultralytics-8.1.0\tomato_train.yaml, epochs=5, time=None, patience=50, batch=8, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=0, project=None, name=train18, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=None, amp=False, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs\detect\train18
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 8210 ultralytics.nn.Attention.CBAM.CBAM [256, 3]
10 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
23 [16, 19, 22] 1 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]]
YOLOv8n-CBAM summary: 236 layers, 3019448 parameters, 3019432 gradients, 8.2 GFLOPs
TensorBoard: Start with 'tensorboard --logdir runs\detect\train18', view at http://localhost:6006/
Freezing layer 'model.23.dfl.conv.weight'
train: Scanning C:\Users\86187\ultralytics-8.1.0\datasets\tomato\labels\train.cache... 392 images, 0 backgrounds, 0 corrupt: 100%|██████████|
val: Scanning C:\Users\86187\ultralytics-8.1.0\datasets\tomato\labels\val.cache... 128 images, 0 backgrounds, 0 corrupt: 100%|██████████| 128/
Plotting labels to runs\detect\train18\labels.jpg...
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically...
optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 57 weight(decay=0.0), 67 weight(decay=0.0005), 63 bias(decay=0.0)
5 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/5 2.02G 4.28 3.798 4.17 231 640: 100%|██████████| 49/49 [01:28<00:00, 1.81s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:29<00:00, 3.71s/it]
all 128 1976 0 0 0 0
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/5 2G 3.658 2.893 3.676 213 640: 100%|██████████| 49/49 [01:25<00:00, 1.74s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:28<00:00, 3.58s/it]
all 128 1976 0 0 0 0
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/5 2.01G 2.895 2.494 3.126 109 640: 100%|██████████| 49/49 [01:26<00:00, 1.76s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:30<00:00, 3.87s/it]
all 128 1976 0 0 0 0
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/5 2.25G 2.666 2.218 2.636 240 640: 100%|██████████| 49/49 [01:26<00:00, 1.76s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:28<00:00, 3.61s/it]
all 128 1976 0 0 0 0
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