coco128是coco数据集的子集只有128张图片
训练代码main.py
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
model.train(data="coco128.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
path = model.export(format="onnx") # export the model to ONNX format
(yolov8) nvidia@nvidia-desktop:~/yolov8$ python main.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??? 164608? ultralytics.nn.modules.block.SPPF??????????? [256, 256, 5]
?10????????????????? -1? 1???????? 0? torch.nn.modules.upsampling.Upsample???????? [None, 2, 'nearest']
?11???????????? [-1, 6]? 1???????? 0? ultralytics.nn.modules.conv.Concat?????????? [1]
?12????????????????? -1? 1??? 148224? ultralytics.nn.modules.block.C2f???????????? [384, 128, 1]
?13????????????????? -1? 1???????? 0? torch.nn.modules.upsampling.Upsample???????? [None, 2, 'nearest']
?14???????????? [-1, 4]? 1???????? 0? ultralytics.nn.modules.conv.Concat?????????? [1]
?15????????????????? -1? 1???? 37248? ultralytics.nn.modules.block.C2f???????????? [192, 64, 1]
?16????????????????? -1? 1???? 36992? ultralytics.nn.modules.conv.Conv???????????? [64, 64, 3, 2]
?17??????????? [-1, 12]? 1???????? 0? ultralytics.nn.modules.conv.Concat?????????? [1]
?18????????????????? -1? 1??? 123648? ultralytics.nn.modules.block.C2f???????????? [192, 128, 1]
?19????????????????? -1? 1??? 147712? ultralytics.nn.modules.conv.Conv???????????? [128, 128, 3, 2]
?20???????????? [-1, 9]? 1???????? 0? ultralytics.nn.modules.conv.Concat?????????? [1]
?21????????????????? -1? 1??? 493056? ultralytics.nn.modules.block.C2f???????????? [384, 256, 1]
?22??????? [15, 18, 21]? 1??? 897664? ultralytics.nn.modules.head.Detect?????????? [80, [64, 128, 256]]
YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
New https://pypi.org/project/ultralytics/8.1.1 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.0 🚀 Python-3.10.13 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce RTX 4060 Ti, 16081MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=3, time=None, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train3, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, 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/train3
?????????????????? 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??? 164608? ultralytics.nn.modules.block.SPPF??????????? [256, 256, 5]
?10????????????????? -1? 1???????? 0? torch.nn.modules.upsampling.Upsample???????? [None, 2, 'nearest']
?11???????????? [-1, 6]? 1???????? 0? ultralytics.nn.modules.conv.Concat?????????? [1]
?12????????????????? -1? 1??? 148224? ultralytics.nn.modules.block.C2f???????????? [384, 128, 1]
?13????????????????? -1? 1???????? 0? torch.nn.modules.upsampling.Upsample???????? [None, 2, 'nearest']
?14???????????? [-1, 4]? 1???????? 0? ultralytics.nn.modules.conv.Concat?????????? [1]
?15????????????????? -1? 1???? 37248? ultralytics.nn.modules.block.C2f???????????? [192, 64, 1]
?16????????????????? -1? 1???? 36992? ultralytics.nn.modules.conv.Conv???????????? [64, 64, 3, 2]
?17??????????? [-1, 12]? 1???????? 0? ultralytics.nn.modules.conv.Concat?????????? [1]
?18????????????????? -1? 1??? 123648? ultralytics.nn.modules.block.C2f???????????? [192, 128, 1]
?19????????????????? -1? 1??? 147712? ultralytics.nn.modules.conv.Conv???????????? [128, 128, 3, 2]
?20???????????? [-1, 9]? 1???????? 0? ultralytics.nn.modules.conv.Concat?????????? [1]
?21????????????????? -1? 1??? 493056? ultralytics.nn.modules.block.C2f???????????? [384, 256, 1]
?22??????? [15, 18, 21]? 1??? 897664? ultralytics.nn.modules.head.Detect?????????? [80, [64, 128, 256]]
Model summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
Transferred 355/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ?
train: Scanning /home/nvidia/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|██████████| 128/12
val: Scanning /home/nvidia/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|██████████| 128/128
Plotting labels to runs/detect/train3/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=0.000119, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
3 epochs...
????? Epoch??? GPU_mem?? box_loss?? cls_loss?? dfl_loss? Instances?????? Size
??????? 1/3????? 2.66G????? 1.226????? 1.615????? 1.274??????? 178??????? 640: 100%|██████████| 8/8 [00:03<00:00,? 2.63it/s]
???????????????? Class???? Images? Instances????? Box(P????????? R????? mAP50? mAP50-95): 100%|██████████| 4/4 [00:00<00:00,? 4.44it/
?????????????????? all??????? 128??????? 929????? 0.645????? 0.533????? 0.614????? 0.455
????? Epoch??? GPU_mem?? box_loss?? cls_loss?? dfl_loss? Instances?????? Size
??????? 2/3????? 2.67G????? 1.225????? 1.514????? 1.268??????? 231??????? 640: 100%|██████████| 8/8 [00:01<00:00,? 5.24it/s]
???????????????? Class???? Images? Instances????? Box(P????????? R????? mAP50? mAP50-95): 100%|██████████| 4/4 [00:00<00:00,? 5.06it/
?????????????????? all??????? 128??????? 929????? 0.675????? 0.538????? 0.626????? 0.467
????? Epoch??? GPU_mem?? box_loss?? cls_loss?? dfl_loss? Instances?????? Size
??????? 3/3????? 2.71G????? 1.209????? 1.448????? 1.222??????? 178??????? 640: 100%|██████████| 8/8 [00:01<00:00,? 5.52it/s]
???????????????? Class???? Images? Instances????? Box(P????????? R????? mAP50? mAP50-95): 100%|██████████| 4/4 [00:00<00:00,? 4.99it/
?????????????????? all??????? 128??????? 929????? 0.676????? 0.548????? 0.632?????? 0.47
3 epochs completed in 0.003 hours.
Optimizer stripped from runs/detect/train3/weights/last.pt, 6.5MB
Optimizer stripped from runs/detect/train3/weights/best.pt, 6.5MB
Validating runs/detect/train3/weights/best.pt...
Ultralytics YOLOv8.1.0 🚀 Python-3.10.13 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce RTX 4060 Ti, 16081MiB)
Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs
???????????????? Class???? Images? Instances????? Box(P????????? R????? mAP50? mAP50-95): 100%|██████████| 4/4 [00:03<00:00,? 1.13it/
?????????????????? all??????? 128??????? 929????? 0.675????? 0.549????? 0.632????? 0.469
??????????????? person??????? 128??????? 254????? 0.802????? 0.671????? 0.769????? 0.542
?????????????? bicycle??????? 128????????? 6????? 0.581????? 0.333????? 0.332????? 0.286
?????????????????? car??????? 128???????? 46????? 0.844????? 0.217????? 0.285????? 0.178
??????????? motorcycle??????? 128????????? 5????? 0.687????? 0.886????? 0.938????? 0.721
????????????? airplane??????? 128????????? 6????? 0.826????? 0.799????? 0.903????? 0.673
?????????????????? bus??????? 128????????? 7????? 0.745????? 0.714????? 0.736????? 0.648
???????????????? train??????? 128????????? 3????? 0.556????? 0.667?????? 0.83????? 0.731
???????????????? truck??????? 128???????? 12????????? 1????? 0.353????? 0.498????? 0.304
????????????????? boat??????? 128????????? 6????? 0.293????? 0.167????? 0.351????? 0.225
???????? traffic light??????? 128???????? 14????? 0.696????? 0.168????? 0.202????? 0.139
???????????? stop sign??????? 128????????? 2????? 0.966????????? 1????? 0.995????? 0.711
???????????????? bench??????? 128????????? 9????? 0.838????? 0.575????? 0.637??????? 0.4
????????????????? bird??????? 128???????? 16????? 0.923????? 0.748?????? 0.88?????? 0.53
?????????????????? cat??????? 128????????? 4????? 0.866????????? 1????? 0.995????? 0.835
?????????????????? dog??????? 128????????? 9????? 0.704????? 0.778????? 0.821????? 0.633
???????????????? horse??????? 128????????? 2????? 0.536????????? 1????? 0.995????? 0.511
????????????? elephant??????? 128???????? 17????? 0.851????? 0.765????? 0.879????? 0.669
????????????????? bear??????? 128????????? 1????? 0.631????????? 1????? 0.995????? 0.995
???????????????? zebra??????? 128????????? 4????? 0.857????????? 1????? 0.995????? 0.965
?????????????? giraffe??????? 128????????? 9????? 0.899????? 0.993????? 0.973????? 0.714
????????????? backpack??????? 128????????? 6????? 0.605????? 0.333????? 0.392????? 0.234
????????????? umbrella??????? 128???????? 18?????? 0.71??????? 0.5????? 0.663????? 0.453
?????????????? handbag??????? 128???????? 19????? 0.518???? 0.0582?????? 0.18???? 0.0947
?????????????????? tie??????? 128????????? 7?????? 0.69????? 0.642????? 0.641????? 0.457
????????????? suitcase??????? 128????????? 4????? 0.641????????? 1????? 0.828????? 0.596
?????????????? frisbee??????? 128????????? 5????? 0.567??????? 0.8????? 0.759????? 0.663
????????????????? skis??????? 128????????? 1????? 0.473????????? 1????? 0.995????? 0.497
???????????? snowboard??????? 128????????? 7????? 0.661????? 0.714????? 0.755????? 0.486
?????????? sports ball??????? 128????????? 6????? 0.703????? 0.406????? 0.503?????? 0.29
????????????????? kite??????? 128???????? 10????? 0.811??????? 0.5????? 0.595????? 0.208
????????? baseball bat??????? 128????????? 4????? 0.574????? 0.362????? 0.414????? 0.174
??????? baseball glove??????? 128????????? 7????? 0.672????? 0.429????? 0.429????? 0.295
??????????? skateboard??????? 128????????? 5????? 0.776??????? 0.6??????? 0.6?????? 0.44
???????? tennis racket??????? 128????????? 7????? 0.742????? 0.415????? 0.529????? 0.367
??????????????? bottle??????? 128???????? 18????? 0.508????? 0.389????? 0.394?????? 0.24
??????????? wine glass??????? 128???????? 16????? 0.584????? 0.562????? 0.581????? 0.361
?????????????????? cup??????? 128???????? 36????? 0.632????? 0.287??????? 0.4????? 0.279
????????????????? fork??????? 128????????? 6????? 0.597????? 0.167????? 0.264????? 0.193
???????????????? knife??????? 128???????? 16????? 0.643??????? 0.5????? 0.612????? 0.351
???????????????? spoon??????? 128???????? 22????? 0.554????? 0.182????? 0.332?????? 0.18
????????????????? bowl??????? 128???????? 28????? 0.676????? 0.571????? 0.615????? 0.495
??????????????? banana??????? 128????????? 1????????? 0????????? 0????? 0.166????? 0.048
????????????? sandwich??????? 128????????? 2????? 0.394??????? 0.5????? 0.497????? 0.497
??????????????? orange??????? 128????????? 4????????? 1????? 0.313????? 0.995????? 0.666
????????????? broccoli??????? 128???????? 11????? 0.471????? 0.182????? 0.247????? 0.221
??????????????? carrot??????? 128???????? 24????? 0.739????? 0.458????? 0.658????? 0.411
?????????????? hot dog??????? 128????????? 2?????? 0.65?????? 0.95????? 0.828????? 0.796
???????????????? pizza??????? 128????????? 5????? 0.689????????? 1????? 0.995?????? 0.86
???????????????? donut??????? 128???????? 14????? 0.639????????? 1????? 0.946????? 0.859
????????????????? cake??????? 128????????? 4????? 0.658????????? 1????? 0.995?????? 0.88
???????????????? chair??????? 128???????? 35????? 0.514????? 0.514????? 0.468????? 0.262
???????????????? couch??????? 128????????? 6????? 0.746????? 0.493????? 0.673????? 0.497
????????? potted plant??????? 128???????? 14????? 0.739????? 0.643????? 0.722????? 0.484
?????????????????? bed??????? 128????????? 3????? 0.768????? 0.667????? 0.806????? 0.636
????????? dining table??????? 128???????? 13????? 0.484????? 0.615????? 0.504????? 0.415
??????????????? toilet??????? 128????????? 2????????? 1????? 0.869????? 0.995????? 0.946
??????????????????? tv??????? 128????????? 2????? 0.384??????? 0.5????? 0.695????? 0.656
??????????????? laptop??????? 128????????? 3????????? 1????????? 0????? 0.696????? 0.544
???????????????? mouse??????? 128????????? 2????????? 1????????? 0???? 0.0527??? 0.00527
??????????????? remote??????? 128????????? 8????? 0.849??????? 0.5????? 0.583????? 0.507
??????????? cell phone??????? 128????????? 8????????? 0????????? 0???? 0.0688???? 0.0465
???????????? microwave??????? 128????????? 3?????? 0.63????? 0.667????? 0.863????? 0.719
????????????????? oven??????? 128????????? 5????? 0.472??????? 0.4????? 0.338????? 0.269
????????????????? sink??????? 128????????? 6????? 0.367????? 0.167????? 0.232????? 0.159
????????? refrigerator??????? 128????????? 5????? 0.688??????? 0.4????? 0.647????? 0.525
????????????????? book??????? 128???????? 29????? 0.621????? 0.114????? 0.328????? 0.187
???????????????? clock??????? 128????????? 9????? 0.779????? 0.781????? 0.893????? 0.713
????????????????? vase??????? 128????????? 2????? 0.418????????? 1????? 0.828????? 0.745
????????????? scissors??????? 128????????? 1????????? 1????????? 0????? 0.249???? 0.0746
??????????? teddy bear??????? 128???????? 21????? 0.884????? 0.381?????? 0.64????? 0.429
??????????? toothbrush??????? 128????????? 5??????? 0.9??????? 0.6????? 0.786????? 0.503
Speed: 3.0ms preprocess, 1.8ms inference, 0.0ms loss, 1.2ms postprocess per image
Results saved to runs/detect/train3
Ultralytics YOLOv8.1.0 🚀 Python-3.10.13 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce RTX 4060 Ti, 16081MiB)
Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs
val: Scanning /home/nvidia/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|██████████| 128/128
???????????????? Class???? Images? Instances????? Box(P????????? R????? mAP50? mAP50-95): 100%|██████████| 8/8 [00:03<00:00,? 2.06it/
?????????????????? all??????? 128??????? 929????? 0.658????? 0.548????? 0.627????? 0.466
??????????????? person??????? 128??????? 254????? 0.808?????? 0.68????? 0.773????? 0.542
?????????????? bicycle??????? 128????????? 6????? 0.569????? 0.333????? 0.326????? 0.283
?????????????????? car??????? 128???????? 46????? 0.805????? 0.217????? 0.285????? 0.178
??????????? motorcycle??????? 128????????? 5????? 0.689????? 0.896????? 0.898????? 0.697
????????????? airplane??????? 128????????? 6????? 0.827????? 0.803????? 0.903????? 0.681
?????????????????? bus??????? 128????????? 7????? 0.681????? 0.714????? 0.736????? 0.648
???????????????? train??????? 128????????? 3????? 0.551????? 0.667?????? 0.83????? 0.731
???????????????? truck??????? 128???????? 12????????? 1????? 0.374????? 0.494????? 0.295
????????????????? boat??????? 128????????? 6????? 0.261????? 0.167????? 0.324?????? 0.14
???????? traffic light??????? 128???????? 14????? 0.696????? 0.168????? 0.202????? 0.139
???????????? stop sign??????? 128????????? 2?????? 0.93????????? 1????? 0.995????? 0.711
???????????????? bench??????? 128????????? 9????? 0.842????? 0.596????? 0.636??????? 0.4
????????????????? bird??????? 128???????? 16????? 0.853?????? 0.75????? 0.866??????? 0.5
?????????????????? cat??????? 128????????? 4????? 0.863????????? 1????? 0.995????? 0.835
?????????????????? dog??????? 128????????? 9????? 0.682????? 0.778????? 0.821????? 0.626
???????????????? horse??????? 128????????? 2?????? 0.53????????? 1????? 0.995????? 0.515
????????????? elephant??????? 128???????? 17????? 0.849????? 0.765????? 0.879????? 0.669
????????????????? bear??????? 128????????? 1????? 0.626????????? 1????? 0.995????? 0.995
???????????????? zebra??????? 128????????? 4????? 0.854????????? 1????? 0.995????? 0.965
?????????????? giraffe??????? 128????????? 9????? 0.744????? 0.971????? 0.943????? 0.732
????????????? backpack??????? 128????????? 6????? 0.613????? 0.333????? 0.391????? 0.238
????????????? umbrella??????? 128???????? 18????? 0.675??????? 0.5????? 0.657????? 0.453
?????????????? handbag??????? 128???????? 19????? 0.554???? 0.0698????? 0.173???? 0.0932
?????????????????? tie??????? 128????????? 7????? 0.698????? 0.665????? 0.641????? 0.457
????????????? suitcase??????? 128????????? 4????? 0.633????????? 1????? 0.828????? 0.596
?????????????? frisbee??????? 128????????? 5????? 0.563??????? 0.8????? 0.759????? 0.663
????????????????? skis??????? 128????????? 1????? 0.461????????? 1????? 0.995????? 0.497
???????????? snowboard??????? 128????????? 7????? 0.657????? 0.714????? 0.757????? 0.484
?????????? sports ball??????? 128????????? 6????? 0.705????? 0.411????? 0.502????? 0.274
????????????????? kite??????? 128???????? 10????? 0.801??????? 0.5????? 0.598????? 0.206
????????? baseball bat??????? 128????????? 4????? 0.577?????? 0.25????? 0.348????? 0.174
??????? baseball glove??????? 128????????? 7????? 0.638????? 0.429????? 0.429????? 0.295
??????????? skateboard??????? 128????????? 5????? 0.871??????? 0.6??????? 0.6?????? 0.44
???????? tennis racket??????? 128????????? 7????? 0.744????? 0.419????? 0.529????? 0.365
??????????????? bottle??????? 128???????? 18????? 0.469????? 0.389????? 0.358????? 0.217
??????????? wine glass??????? 128???????? 16????? 0.574????? 0.562????? 0.554????? 0.347
?????????????????? cup??????? 128???????? 36????? 0.566????? 0.278????? 0.401????? 0.286
????????????????? fork??????? 128????????? 6?????? 0.59????? 0.167????? 0.228????? 0.195
???????????????? knife??????? 128???????? 16????? 0.563??????? 0.5????? 0.587????? 0.358
???????????????? spoon??????? 128???????? 22????? 0.633????? 0.182????? 0.331????? 0.186
????????????????? bowl??????? 128???????? 28????? 0.737????? 0.643????? 0.658????? 0.498
??????????????? banana??????? 128????????? 1????????? 0????????? 0???? 0.0995????? 0.042
????????????? sandwich??????? 128????????? 2????? 0.161????? 0.241????? 0.497????? 0.497
??????????????? orange??????? 128????????? 4????????? 1????? 0.321????? 0.995????? 0.666
????????????? broccoli??????? 128???????? 11????? 0.501????? 0.182????? 0.257????? 0.207
??????????????? carrot??????? 128???????? 24????? 0.728????? 0.558????? 0.653????? 0.418
?????????????? hot dog??????? 128????????? 2????? 0.649????? 0.946????? 0.828????? 0.796
???????????????? pizza??????? 128????????? 5????? 0.717????????? 1????? 0.995?????? 0.86
???????????????? donut??????? 128???????? 14????? 0.637????????? 1?????? 0.94????? 0.854
????????????????? cake??????? 128????????? 4?????? 0.61????????? 1????? 0.945????? 0.845
???????????????? chair??????? 128???????? 35????? 0.484????? 0.543????? 0.472????? 0.258
???????????????? couch??????? 128????????? 6????? 0.613??????? 0.5????? 0.745????? 0.578
????????? potted plant??????? 128???????? 14????? 0.716????? 0.643????? 0.723????? 0.481
?????????????????? bed??????? 128????????? 3????? 0.757????? 0.667????? 0.913????? 0.661
????????? dining table??????? 128???????? 13????? 0.457????? 0.615????? 0.496????? 0.391
??????????????? toilet??????? 128????????? 2????????? 1????? 0.875????? 0.995????? 0.946
??????????????????? tv??????? 128????????? 2????? 0.378??????? 0.5????? 0.695????? 0.656
??????????????? laptop??????? 128????????? 3????????? 1????????? 0????? 0.605????? 0.484
???????????????? mouse??????? 128????????? 2????????? 1????????? 0???? 0.0698??? 0.00698
??????????????? remote??????? 128????????? 8????? 0.845??????? 0.5????? 0.605????? 0.514
??????????? cell phone??????? 128????????? 8????????? 0????????? 0???? 0.0696???? 0.0469
???????????? microwave??????? 128????????? 3????? 0.617????? 0.667????? 0.863????? 0.733
????????????????? oven??????? 128????????? 5????? 0.431??????? 0.4????? 0.339?????? 0.27
????????????????? sink??????? 128????????? 6????? 0.378????? 0.167?????? 0.18????? 0.131
????????? refrigerator??????? 128????????? 5????? 0.684??????? 0.4?????? 0.65????? 0.517
????????????????? book??????? 128???????? 29????? 0.637????? 0.122????? 0.343????? 0.195
???????????????? clock??????? 128????????? 9?????? 0.78????? 0.788????? 0.894????? 0.734
????????????????? vase??????? 128????????? 2????? 0.407????????? 1????? 0.828????? 0.745
????????????? scissors??????? 128????????? 1????????? 1????????? 0????? 0.249???? 0.0746
??????????? teddy bear??????? 128???????? 21????? 0.883????? 0.381????? 0.634?????? 0.42
??????????? toothbrush??????? 128????????? 5????? 0.636??????? 0.6????? 0.736????? 0.468
Speed: 3.4ms preprocess, 13.3ms inference, 0.0ms loss, 3.5ms postprocess per image
Results saved to runs/detect/train32
Found https://ultralytics.com/images/bus.jpg locally at bus.jpg
image 1/1 /home/nvidia/yolov8/bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign, 17.2ms
Speed: 4.8ms preprocess, 17.2ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 480)
Ultralytics YOLOv8.1.0 🚀 Python-3.10.13 torch-2.0.1+cu117 CPU (Intel Xeon E5-2686 v4 2.30GHz)
PyTorch: starting from 'runs/detect/train3/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)
ONNX: starting export with onnx 1.15.0 opset 17...
============= Diagnostic Run torch.onnx.export version 2.0.1+cu117 =============
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================
ONNX: export success ? 0.9s, saved as 'runs/detect/train3/weights/best.onnx' (12.2 MB)
Export complete (2.4s)
Results saved to /home/nvidia/yolov8/runs/detect/train3/weights
Predict:???????? yolo predict task=detect model=runs/detect/train3/weights/best.onnx imgsz=640
Validate:??????? yolo val task=detect model=runs/detect/train3/weights/best.onnx imgsz=640 data=/home/nvidia/anaconda3/envs/yolov8/lib/python3.10/site-packages/ultralytics/cfg/datasets/coco128.yaml
Visualize:?????? https://netron.app 可视化网站