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result.log
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0 -1 1 464 ultralytics.nn.modules.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.C2f [384, 256, 1]
22 [15, 18, 21] 1 897664 ultralytics.nn.modules.Detect [80, [64, 128, 256]]
YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
Ultralytics YOLOv8.0.66 Python-3.9.16 torch-2.0.0 CPU
yolo/engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=3, 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=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, 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_thickness=3, 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, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train3
2023-04-06 12:24:41.301591: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-06 12:24:41.921521: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.C2f [384, 256, 1]
22 [15, 18, 21] 1 897664 ultralytics.nn.modules.Detect [80, [64, 128, 256]]
Model summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
Transferred 355/355 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train3', view at http://localhost:6006/
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias
train: Scanning /home/crazydevlegend/Documents/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 80 corrupt: 100%|██████████| 128/128 [00:00<00:00, 1
train: New cache created: /home/crazydevlegend/Documents/datasets/coco128/labels/train2017.cache
val: Scanning /home/crazydevlegend/Documents/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 80 corrupt: 100%|██████████| 128/128 [00:00<?, ?
Plotting labels to runs/detect/train3/labels.jpg...
Image sizes 640 train, 640 val
Using 0 dataloader workers
Logging results to runs/detect/train3
Starting training for 3 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/3 0G 1.166 1.386 1.217 215 640: 100%|██████████| 8/8 [00:23<00:00, 2.99s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:07<00:00, 1.84s/it]
all 128 929 0.651 0.549 0.617 0.455
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/3 0G 1.181 1.426 1.255 185 640: 100%|██████████| 8/8 [00:18<00:00, 2.36s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:07<00:00, 2.00s/it]
all 128 929 0.669 0.589 0.646 0.479
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/3 0G 1.174 1.318 1.252 246 640: 100%|██████████| 8/8 [00:20<00:00, 2.61s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:11<00:00, 2.88s/it]
all 128 929 0.673 0.589 0.658 0.491
3 epochs completed in 0.026 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 YOLOv8.0.66 Python-3.9.16 torch-2.0.0 CPU
Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:09<00:00, 2.27s/it]
all 128 929 0.673 0.59 0.658 0.49
person 128 254 0.743 0.677 0.76 0.54
bicycle 128 6 0.74 0.333 0.388 0.268
car 128 46 0.846 0.217 0.333 0.187
motorcycle 128 5 0.672 0.8 0.92 0.759
airplane 128 6 0.638 0.833 0.942 0.739
bus 128 7 0.747 0.714 0.734 0.675
train 128 3 0.676 1 0.913 0.855
truck 128 12 0.905 0.5 0.527 0.358
boat 128 6 0.329 0.167 0.449 0.3
traffic light 128 14 0.629 0.214 0.226 0.142
stop sign 128 2 0.683 1 0.995 0.721
bench 128 9 0.756 0.556 0.68 0.531
bird 128 16 0.876 0.875 0.955 0.625
cat 128 4 0.684 1 0.995 0.778
dog 128 9 0.553 0.778 0.812 0.579
horse 128 2 0.692 1 0.995 0.484
elephant 128 17 0.774 0.941 0.936 0.749
bear 128 1 0.516 1 0.995 0.995
zebra 128 4 0.858 1 0.995 0.965
giraffe 128 9 0.775 1 0.973 0.727
backpack 128 6 0.637 0.333 0.462 0.262
umbrella 128 18 0.666 0.555 0.692 0.453
handbag 128 19 1 0.12 0.271 0.148
tie 128 7 0.772 0.714 0.729 0.508
suitcase 128 4 0.773 0.866 0.895 0.569
frisbee 128 5 0.755 0.8 0.758 0.655
skis 128 1 0.746 1 0.995 0.497
snowboard 128 7 0.446 0.714 0.666 0.463
sports ball 128 6 0.717 0.434 0.544 0.27
kite 128 10 0.661 0.6 0.553 0.206
baseball bat 128 4 0.465 0.447 0.26 0.127
baseball glove 128 7 0.686 0.429 0.43 0.303
skateboard 128 5 0.759 0.6 0.599 0.4
tennis racket 128 7 0.732 0.397 0.534 0.331
bottle 128 18 0.472 0.444 0.436 0.267
wine glass 128 16 0.519 0.562 0.586 0.353
cup 128 36 0.662 0.327 0.43 0.315
fork 128 6 0.636 0.167 0.19 0.183
knife 128 16 0.633 0.5 0.621 0.369
spoon 128 22 0.501 0.182 0.339 0.204
bowl 128 28 0.606 0.679 0.66 0.552
banana 128 1 0 0 0.199 0.073
sandwich 128 2 1 0.823 0.995 0.995
orange 128 4 1 0.391 0.825 0.531
broccoli 128 11 0.499 0.273 0.277 0.229
carrot 128 24 0.698 0.583 0.738 0.482
hot dog 128 2 0.453 1 0.995 0.946
pizza 128 5 0.742 1 0.995 0.834
donut 128 14 0.634 1 0.941 0.858
cake 128 4 0.85 1 0.995 0.89
chair 128 35 0.555 0.543 0.49 0.298
couch 128 6 0.264 0.333 0.607 0.464
potted plant 128 14 0.656 0.714 0.72 0.484
bed 128 3 0.89 1 0.995 0.765
dining table 128 13 0.503 0.615 0.501 0.405
toilet 128 2 1 0.962 0.995 0.946
tv 128 2 0.511 0.5 0.745 0.696
laptop 128 3 1 0 0.5 0.398
mouse 128 2 1 0 0.0525 0.00525
remote 128 8 0.805 0.5 0.584 0.502
cell phone 128 8 0 0 0.088 0.046
microwave 128 3 0.578 0.928 0.731 0.625
oven 128 5 0.511 0.4 0.399 0.299
sink 128 6 0.318 0.167 0.395 0.208
refrigerator 128 5 0.546 0.4 0.648 0.52
book 128 29 0.66 0.207 0.402 0.236
clock 128 9 0.945 0.889 0.918 0.77
vase 128 2 0.458 1 0.828 0.795
scissors 128 1 1 0 0.497 0.149
teddy bear 128 21 0.752 0.577 0.638 0.413
toothbrush 128 5 1 0.578 0.826 0.527
Speed: 1.1ms preprocess, 41.3ms inference, 0.0ms loss, 1.3ms postprocess per image
Results saved to runs/detect/train3
Ultralytics YOLOv8.0.66 Python-3.9.16 torch-2.0.0 CPU
Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs
val: Scanning /home/crazydevlegend/Documents/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 80 corrupt: 100%|██████████| 128/128 [00:00<?, ?
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:08<00:00, 1.05s/it]
all 128 929 0.664 0.592 0.654 0.488
person 128 254 0.739 0.677 0.762 0.542
bicycle 128 6 0.748 0.333 0.383 0.265
car 128 46 0.852 0.217 0.333 0.187
motorcycle 128 5 0.622 0.8 0.92 0.752
airplane 128 6 0.639 0.833 0.942 0.739
bus 128 7 0.758 0.714 0.734 0.675
train 128 3 0.677 1 0.913 0.855
truck 128 12 0.908 0.5 0.528 0.36
boat 128 6 0.278 0.197 0.445 0.26
traffic light 128 14 0.634 0.214 0.229 0.143
stop sign 128 2 0.685 1 0.995 0.721
bench 128 9 0.758 0.556 0.68 0.531
bird 128 16 0.877 0.875 0.954 0.624
cat 128 4 0.633 1 0.895 0.694
dog 128 9 0.553 0.778 0.808 0.576
horse 128 2 0.694 1 0.995 0.492
elephant 128 17 0.774 0.941 0.936 0.749
bear 128 1 0.518 1 0.995 0.995
zebra 128 4 0.858 1 0.995 0.953
giraffe 128 9 0.724 1 0.968 0.721
backpack 128 6 0.486 0.333 0.458 0.28
umbrella 128 18 0.665 0.552 0.689 0.451
handbag 128 19 1 0.119 0.274 0.147
tie 128 7 0.773 0.714 0.725 0.505
suitcase 128 4 0.772 0.86 0.895 0.569
frisbee 128 5 0.758 0.8 0.758 0.655
skis 128 1 0.749 1 0.995 0.497
snowboard 128 7 0.448 0.714 0.665 0.463
sports ball 128 6 0.716 0.432 0.534 0.266
kite 128 10 0.666 0.6 0.554 0.206
baseball bat 128 4 0.292 0.25 0.207 0.112
baseball glove 128 7 0.664 0.429 0.43 0.317
skateboard 128 5 0.806 0.6 0.599 0.396
tennis racket 128 7 0.731 0.396 0.534 0.33
bottle 128 18 0.462 0.444 0.443 0.274
wine glass 128 16 0.523 0.562 0.561 0.351
cup 128 36 0.663 0.382 0.447 0.324
fork 128 6 0.623 0.167 0.194 0.187
knife 128 16 0.473 0.5 0.596 0.369
spoon 128 22 0.575 0.182 0.357 0.221
bowl 128 28 0.652 0.75 0.693 0.565
banana 128 1 0 0 0.124 0.0846
sandwich 128 2 1 0.871 0.995 0.995
orange 128 4 1 0.386 0.825 0.531
broccoli 128 11 0.363 0.21 0.274 0.225
carrot 128 24 0.721 0.646 0.73 0.471
hot dog 128 2 0.582 1 0.828 0.796
pizza 128 5 0.754 1 0.995 0.834
donut 128 14 0.634 1 0.941 0.853
cake 128 4 0.735 1 0.995 0.89
chair 128 35 0.563 0.514 0.473 0.285
couch 128 6 0.41 0.583 0.714 0.557
potted plant 128 14 0.668 0.714 0.72 0.484
bed 128 3 0.744 1 0.995 0.839
dining table 128 13 0.522 0.615 0.504 0.409
toilet 128 2 1 0.961 0.995 0.946
tv 128 2 0.512 0.5 0.745 0.696
laptop 128 3 1 0 0.446 0.374
mouse 128 2 1 0 0.106 0.0212
remote 128 8 0.806 0.5 0.597 0.509
cell phone 128 8 0 0 0.0881 0.046
microwave 128 3 0.558 0.862 0.731 0.602
oven 128 5 0.512 0.4 0.399 0.299
sink 128 6 0.367 0.167 0.426 0.196
refrigerator 128 5 0.552 0.4 0.648 0.513
book 128 29 0.637 0.242 0.405 0.236
clock 128 9 0.982 0.889 0.917 0.769
vase 128 2 0.466 1 0.828 0.795
scissors 128 1 1 0 0.497 0.149
teddy bear 128 21 0.75 0.571 0.635 0.411
toothbrush 128 5 0.89 0.6 0.846 0.494
Speed: 1.3ms preprocess, 51.4ms inference, 0.0ms loss, 1.3ms postprocess per image
Results saved to runs/detect/val
Downloading https://ultralytics.com/images/bus.jpg to bus.jpg...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 476k/476k [00:00<00:00, 62.1MB/s]
image 1/1 /home/crazydevlegend/Documents/yolo8/bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign, 307.8ms
Speed: 3.4ms preprocess, 307.8ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)
Ultralytics YOLOv8.0.66 Python-3.9.16 torch-2.0.0 CPU
ONNX: starting export with onnx 1.13.1 opset 17...
================ Diagnostic Run torch.onnx.export version 2.0.0 ================
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================
ONNX: export success ✅ 5.2s, saved as runs/detect/train3/weights/best.onnx (12.2 MB)
Export complete (5.7s)
Results saved to /home/crazydevlegend/Documents/yolo8/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/crazydevlegend/miniconda3/envs/yolo/lib/python3.9/site-packages/ultralytics/datasets/coco128.yaml
Visualize: https://netron.app