使用训练好的模型进行预测
yolo predict task=detect model=训练好的模型路径 source=测试图片文件夹路径 show=True
效果展示
切换模型进行训练（yolov8s）
修改main.py训练参数文件
使用云gpu进行训练，很方便：点击链接转至在线云gpu
修改训练参数：
此文件位于：yolov8-main->ultralytics->datasets->keypoint.yaml
修改训练素材路径位置
安装依赖
修改default.yaml
开启训练
from n params module arguments
0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2]
1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
2 -1 1 29056 ultralytics.nn.modules.block.C2f [64, 64, 1, True]
3 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
4 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
5 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
6 -1 2 788480 ultralytics.nn.modules.block.C2f [256, 256, 2, True]
7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2]
8 -1 1 1838080 ultralytics.nn.modules.block.C2f [512, 512, 1, True]
9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 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 455008 ultralytics.nn.modules.block.VoVGSCSPC [768, 256]
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 114864 ultralytics.nn.modules.block.VoVGSCSPC [384, 128]
16 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 356704 ultralytics.nn.modules.block.VoVGSCSPC [384, 256]
19 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 1417920 ultralytics.nn.modules.block.VoVGSCSPC [768, 512]
22 [15, 18, 21] 1 2118757 ultralytics.nn.modules.head.Detect [7, [128, 256, 512]]
YOLOv8s summary: 301 layers, 10281013 parameters, 10280997 gradients
New https://pypi.org/project/ultralytics/8.0.168 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.0.118 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA GeForce RTX 3060, 12044MiB)
yolo/engine/trainer: task=detect, mode=train, model=/home/featurize/work/yolo/yolov8-main/yolov8s.pt, data=/home/featurize/work/yolo/yolov8-main/ultralytics/datasets/keypoint.yaml, epochs=100, patience=50, batch=4, imgsz=640, save=True, save_period=-1, cache=False, device=0, workers=6, project=None, name=None, exist_ok=False, pretrained=False, optimizer=auto, verbose=True, 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=runs/detect/train8
Downloading https://ultralytics.com/assets/Arial.Unicode.ttf to /home/featurize/.config/Ultralytics/Arial.Unicode.ttf...
100%|███████████████████████████████████████| 22.2M/22.2M [00:00<00:00, 279MB/s]
Overriding model.yaml nc=80 with nc=7
from n params module arguments
0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2]
1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
2 -1 1 29056 ultralytics.nn.modules.block.C2f [64, 64, 1, True]
3 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
4 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
5 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
6 -1 2 788480 ultralytics.nn.modules.block.C2f [256, 256, 2, True]
7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2]
8 -1 1 1838080 ultralytics.nn.modules.block.C2f [512, 512, 1, True]
9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 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 591360 ultralytics.nn.modules.block.C2f [768, 256, 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 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
16 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
19 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 1969152 ultralytics.nn.modules.block.C2f [768, 512, 1]
22 [15, 18, 21] 1 2118757 ultralytics.nn.modules.head.Detect [7, [128, 256, 512]]
Model summary: 225 layers, 11138309 parameters, 11138293 gradients
Transferred 349/355 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train8', view at http://localhost:6006/
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed ✅
train: Scanning /home/featurize/work/yolo/yolov8-main/datasets/injector_datasets
train: New cache created: /home/featurize/work/yolo/yolov8-main/datasets/injector_datasets/labels/trainImages.cache
val: Scanning /home/featurize/work/yolo/yolov8-main/datasets/injector_datasets/l
val: New cache created: /home/featurize/work/yolo/yolov8-main/datasets/injector_datasets/labels/valImages.cache
Plotting labels to runs/detect/train8/labels.jpg...
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight
self._figure.tight_layout(*args, **kwargs)
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
if pd.api.types.is_categorical_dtype(vector):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/environment/miniconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 33014 (\N{CJK UNIFIED IDEOGRAPH-80F6}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 22622 (\N{CJK UNIFIED IDEOGRAPH-585E}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 25512 (\N{CJK UNIFIED IDEOGRAPH-63A8}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 26438 (\N{CJK UNIFIED IDEOGRAPH-6746}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 23614 (\N{CJK UNIFIED IDEOGRAPH-5C3E}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 37096 (\N{CJK UNIFIED IDEOGRAPH-90E8}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 38024 (\N{CJK UNIFIED IDEOGRAPH-9488}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 22068 (\N{CJK UNIFIED IDEOGRAPH-5634}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 27498 (\N{CJK UNIFIED IDEOGRAPH-6B6A}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 34746 (\N{CJK UNIFIED IDEOGRAPH-87BA}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 21475 (\N{CJK UNIFIED IDEOGRAPH-53E3}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 23567 (\N{CJK UNIFIED IDEOGRAPH-5C0F}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 33014 (\N{CJK UNIFIED IDEOGRAPH-80F6}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 22622 (\N{CJK UNIFIED IDEOGRAPH-585E}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 25512 (\N{CJK UNIFIED IDEOGRAPH-63A8}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 26438 (\N{CJK UNIFIED IDEOGRAPH-6746}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 23614 (\N{CJK UNIFIED IDEOGRAPH-5C3E}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 37096 (\N{CJK UNIFIED IDEOGRAPH-90E8}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 38024 (\N{CJK UNIFIED IDEOGRAPH-9488}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 22068 (\N{CJK UNIFIED IDEOGRAPH-5634}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 27498 (\N{CJK UNIFIED IDEOGRAPH-6B6A}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 34746 (\N{CJK UNIFIED IDEOGRAPH-87BA}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 21475 (\N{CJK UNIFIED IDEOGRAPH-53E3}) missing from current font.
plt.savefig(fname, dpi=200)
/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/plotting.py:276: UserWarning: Glyph 23567 (\N{CJK UNIFIED IDEOGRAPH-5C0F}) missing from current font.
plt.savefig(fname, dpi=200)
optimizer: AdamW(lr=0.000909, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 4 dataloader workers
Logging results to runs/detect/train8
Starting training for 100 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/100 1.49G 7.714 9.472 1.749 97 640: Downloading https://ultralytics.com/assets/Arial.ttf to /home/featurize/.config/Ultralytics/Arial.ttf...
1/100 1.49G 7.788 9.67 1.762 80 640: Downloading https://ultralytics.com/assets/Arial.ttf to /home/featurize/.config/Ultralytics/Arial.ttf...
1/100 1.49G 7.561 9.627 1.769 39 640: Downloading https://ultralytics.com/assets/Arial.ttf to /home/featurize/.config/Ultralytics/Arial.ttf...
1/100 1.49G 7.513 9.496 1.781 47 640:
100%|█████████████████████████████████████████| 755k/755k [00:00<00:00, 195MB/s]
0%| | 0.00/755k [00:00<?, ?B/s]
0%| | 0.00/755k [00:00<?, ?B/s]
10%|███▊ | 72.0k/755k [00:00<00:01, 673kB/s]
6%|██▌ | 48.0k/755k [00:00<00:01, 480kB/s]
22%|█████████ | 168k/755k [00:00<00:00, 834kB/s]
100%|████████████████████████████████████████| 755k/755k [00:00<00:00, 2.53MB/s]
100%|████████████████████████████████████████| 755k/755k [00:00<00:00, 2.63MB/s]
1/100 1.52G 7.088 8.711 1.734 75 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.26 0.336 0.312 0.0911
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/100 1.59G 2.256 2.264 1.08 56 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.587 0.616 0.72 0.406
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/100 1.59G 1.74 1.608 1.029 36 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.832 0.757 0.817 0.496
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/100 1.58G 1.578 1.259 1.006 70 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.728 0.762 0.857 0.542
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/100 1.56G 1.534 1.063 1.002 79 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.889 0.827 0.883 0.574
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/100 1.52G 1.439 0.9197 0.9803 64 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.978 0.85 0.893 0.617
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/100 1.57G 1.346 0.7932 0.9889 47 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.841 0.911 0.907 0.609
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/100 1.53G 1.304 0.7377 0.9628 27 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.847 0.929 0.953 0.669
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/100 1.53G 1.214 0.6728 0.95 56 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.936 0.968 0.988 0.711
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/100 1.51G 1.187 0.636 0.938 53 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.951 0.983 0.983 0.699
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/100 1.51G 1.218 0.6118 0.9495 41 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.974 0.974 0.989 0.688
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/100 1.51G 1.285 0.6297 0.9604 40 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.944 0.911 0.987 0.697
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/100 1.51G 1.239 0.6125 0.9448 46 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.973 0.987 0.987 0.701
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/100 1.51G 1.173 0.5838 0.9342 48 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.972 0.981 0.992 0.721
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/100 1.51G 1.095 0.554 0.917 82 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.993 1 0.995 0.744
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/100 1.51G 1.11 0.5582 0.9378 47 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.991 0.995 0.995 0.722
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/100 1.53G 1.129 0.5628 0.929 47 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.988 0.992 0.995 0.708
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/100 1.53G 1.111 0.542 0.9084 53 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.988 0.989 0.993 0.733
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/100 1.51G 1.074 0.5287 0.9198 89 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.99 0.981 0.991 0.757
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/100 1.51G 1.051 0.5111 0.9007 49 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.988 0.985 0.992 0.726
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/100 1.53G 1.048 0.5056 0.905 58 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.985 0.99 0.994 0.741
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/100 1.53G 1.027 0.5085 0.9059 79 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.993 0.991 0.995 0.774
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/100 1.57G 1.026 0.4933 0.9085 67 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.989 0.991 0.995 0.746
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/100 1.55G 0.9934 0.4795 0.9004 56 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.99 0.991 0.995 0.775
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/100 1.53G 0.9916 0.4686 0.8907 50 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.991 0.988 0.994 0.763
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/100 1.51G 0.9791 0.4671 0.8914 40 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.989 0.991 0.995 0.764
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/100 1.53G 0.9848 0.4532 0.885 107 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.992 0.989 0.994 0.761
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/100 1.53G 0.9716 0.4541 0.905 34 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.99 0.989 0.994 0.778
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/100 1.53G 0.9671 0.455 0.8927 55 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.992 0.99 0.994 0.765
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/100 1.53G 0.9647 0.449 0.8885 43 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.989 0.988 0.994 0.785
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/100 1.53G 0.935 0.4334 0.8953 55 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.991 0.99 0.994 0.755
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/100 1.53G 0.9801 0.4383 0.8881 89 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.991 0.999 0.995 0.786
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/100 1.53G 0.9725 0.4386 0.8858 40 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.995 0.999 0.995 0.747
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/100 1.51G 0.9803 0.444 0.8938 65 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.995 1 0.995 0.743
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/100 1.51G 0.9246 0.4233 0.8812 48 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.992 0.995 0.995 0.771
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/100 1.51G 0.9377 0.4236 0.884 105 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.993 0.991 0.995 0.768
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/100 1.51G 0.9631 0.428 0.8964 56 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.995 0.991 0.995 0.749
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/100 1.51G 0.9436 0.4259 0.8921 37 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.994 0.991 0.995 0.804
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/100 1.57G 0.9083 0.4119 0.8846 85 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.995 0.992 0.995 0.764
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/100 1.53G 0.9459 0.4209 0.8814 43 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.995 0.99 0.994 0.768
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/100 1.53G 0.9183 0.4124 0.8725 57 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.996 0.992 0.995 0.774
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/100 1.51G 0.8959 0.4084 0.8798 71 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.996 0.991 0.995 0.787
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/100 1.51G 0.8924 0.4123 0.8796 55 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.993 0.991 0.995 0.778
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/100 1.51G 0.9295 0.4177 0.8847 84 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.994 0.991 0.995 0.796
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/100 1.51G 0.9271 0.4138 0.8807 44 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.993 0.992 0.994 0.747
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/100 1.51G 0.8881 0.4022 0.8704 52 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.99 0.991 0.995 0.781
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/100 1.51G 0.8914 0.4048 0.8768 36 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.985 0.999 0.995 0.786
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/100 1.51G 0.9257 0.4075 0.8832 39 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.991 1 0.995 0.785
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/100 1.51G 0.9245 0.4068 0.8723 42 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.994 1 0.995 0.805
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/100 1.51G 0.8915 0.3981 0.8768 76 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.992 0.999 0.995 0.771
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
51/100 1.51G 0.8769 0.3943 0.8804 51 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.992 0.991 0.995 0.785
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
52/100 1.51G 0.8647 0.3863 0.8672 47 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.994 0.991 0.995 0.785
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
53/100 1.51G 0.878 0.3854 0.8713 22 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.996 0.991 0.995 0.782
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
54/100 1.51G 0.8804 0.3957 0.8731 47 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.994 0.998 0.995 0.784
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
55/100 1.51G 0.8723 0.3911 0.8733 57 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.993 0.998 0.995 0.777
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
56/100 1.51G 0.8739 0.3835 0.8791 49 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.987 0.998 0.995 0.756
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
57/100 1.53G 0.8824 0.3906 0.8712 56 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.984 0.998 0.995 0.776
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
58/100 1.53G 0.8651 0.3856 0.87 40 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.99 0.987 0.994 0.799
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
59/100 1.51G 0.8714 0.3881 0.8755 55 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.995 0.981 0.995 0.774
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
60/100 1.51G 0.8584 0.3883 0.8713 54 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.996 0.981 0.995 0.781
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
61/100 1.51G 0.8537 0.3796 0.8658 38 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.984 0.999 0.995 0.801
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
62/100 1.51G 0.8624 0.388 0.8758 40 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.992 0.99 0.995 0.793
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
63/100 1.51G 0.841 0.3857 0.864 52 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.991 0.993 0.995 0.767
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
64/100 1.51G 0.8598 0.3821 0.87 85 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.989 0.991 0.995 0.778
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
65/100 1.51G 0.8324 0.3825 0.8609 58 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.993 0.991 0.995 0.784
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
66/100 1.51G 0.8577 0.3801 0.8708 46 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.991 0.997 0.995 0.786
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
67/100 1.51G 0.8637 0.3803 0.8723 65 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.988 0.998 0.995 0.791
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
68/100 1.51G 0.841 0.3772 0.8654 82 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.979 1 0.995 0.781
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
69/100 1.51G 0.8025 0.3636 0.8565 51 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.984 0.985 0.994 0.797
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
70/100 1.51G 0.835 0.3664 0.8588 49 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.992 0.981 0.994 0.784
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
71/100 1.59G 0.845 0.3734 0.8596 38 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.98 0.995 0.994 0.785
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
72/100 1.59G 0.8206 0.3693 0.8711 38 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.991 0.99 0.994 0.785
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
73/100 1.55G 0.8175 0.3641 0.8638 63 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.995 0.991 0.995 0.796
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
74/100 1.55G 0.8229 0.3611 0.8556 39 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.994 0.991 0.995 0.814
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
75/100 1.54G 0.8236 0.3669 0.8611 77 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.992 0.991 0.994 0.797
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
76/100 1.54G 0.8275 0.3671 0.8672 39 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.995 0.991 0.994 0.798
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
77/100 1.54G 0.819 0.3612 0.8627 37 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.996 0.991 0.995 0.804
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
78/100 1.58G 0.8077 0.3593 0.8684 55 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.996 0.992 0.995 0.8
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
79/100 1.56G 0.8067 0.359 0.8571 49 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.996 0.993 0.995 0.799
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
80/100 1.58G 0.8012 0.3588 0.8646 52 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.997 0.991 0.995 0.798
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
81/100 1.56G 0.8159 0.364 0.8627 61 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.989 0.999 0.995 0.813
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
82/100 1.59G 0.8072 0.3583 0.8635 60 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.992 0.999 0.995 0.814
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
83/100 1.53G 0.8153 0.3605 0.8662 49 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.995 0.999 0.995 0.791
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
84/100 1.53G 0.7978 0.3544 0.8585 58 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.994 1 0.995 0.786
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
85/100 1.51G 0.7747 0.351 0.8523 48 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.995 1 0.995 0.804
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
86/100 1.51G 0.7944 0.3504 0.8563 67 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.997 1 0.995 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
87/100 1.57G 0.7787 0.3409 0.8558 29 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.994 0.999 0.995 0.799
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
88/100 1.53G 0.7863 0.3461 0.8556 55 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.99 0.999 0.995 0.79
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
89/100 1.51G 0.7875 0.3413 0.8485 18 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.991 0.999 0.995 0.799
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
90/100 1.51G 0.794 0.3489 0.8616 38 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.994 0.999 0.995 0.809
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
91/100 1.51G 0.8086 0.3484 0.8653 87 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.997 1 0.995 0.811
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
92/100 1.51G 0.7732 0.3432 0.862 31 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.997 1 0.995 0.801
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
93/100 1.51G 0.7827 0.3431 0.8462 86 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.997 1 0.995 0.807
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
94/100 1.51G 0.7678 0.3417 0.8454 43 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.997 1 0.995 0.808
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
95/100 1.53G 0.7703 0.3397 0.8499 42 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.996 0.999 0.995 0.81
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
96/100 1.53G 0.7611 0.338 0.8461 47 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.994 0.999 0.995 0.811
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
97/100 1.53G 0.7629 0.3372 0.8534 41 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.991 0.999 0.995 0.807
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
98/100 1.53G 0.7512 0.3332 0.8415 51 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.997 0.992 0.995 0.809
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
99/100 1.53G 0.7686 0.3399 0.8489 37 640: 1
Class Images Instances Box(P R mAP50 m
all 46 521 0.997 0.992 0.995 0.808
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
100/100 1.53G 0.7692 0.3428 0.8536 64 640: 1
Class Images Instances Box(P R mAP50 m
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/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/metrics.py:517: UserWarning: Glyph 22622 (\N{CJK UNIFIED IDEOGRAPH-585E}) missing from current font.
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/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/metrics.py:517: UserWarning: Glyph 25512 (\N{CJK UNIFIED IDEOGRAPH-63A8}) missing from current font.
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/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/metrics.py:517: UserWarning: Glyph 26438 (\N{CJK UNIFIED IDEOGRAPH-6746}) missing from current font.
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/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/metrics.py:517: UserWarning: Glyph 37096 (\N{CJK UNIFIED IDEOGRAPH-90E8}) missing from current font.
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/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/metrics.py:517: UserWarning: Glyph 38024 (\N{CJK UNIFIED IDEOGRAPH-9488}) missing from current font.
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/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/metrics.py:517: UserWarning: Glyph 22068 (\N{CJK UNIFIED IDEOGRAPH-5634}) missing from current font.
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all 46 521 0.997 0.992 0.995 0.812
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fig.savefig(plot_fname, dpi=250)
100 epochs completed in 0.119 hours.
Optimizer stripped from runs/detect/train8/weights/last.pt, 22.5MB
Optimizer stripped from runs/detect/train8/weights/best.pt, 22.5MB
Validating runs/detect/train8/weights/best.pt...
Ultralytics YOLOv8.0.118 🚀 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (NVIDIA GeForce RTX 3060, 12044MiB)
Model summary (fused): 168 layers, 11128293 parameters, 0 gradients
Class Images Instances Box(P R mAP50 m
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/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/metrics.py:517: UserWarning: Glyph 23614 (\N{CJK UNIFIED IDEOGRAPH-5C3E}) missing from current font.
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/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/metrics.py:517: UserWarning: Glyph 37096 (\N{CJK UNIFIED IDEOGRAPH-90E8}) missing from current font.
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/home/featurize/work/yolo/yolov8-main/ultralytics/yolo/utils/metrics.py:517: UserWarning: Glyph 38024 (\N{CJK UNIFIED IDEOGRAPH-9488}) missing from current font.
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all 46 521 0.994 0.991 0.995 0.814
胶塞 46 121 0.997 1 0.995 0.825
推杆尾部 46 124 0.997 1 0.995 0.86
针尾部 46 129 0.997 1 0.995 0.878
针嘴 46 92 0.986 1 0.995 0.731
歪嘴 46 14 1 0.936 0.995 0.78
螺口 46 15 0.993 1 0.995 0.841
小胶塞 46 26 0.99 1 0.995 0.785
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fig.savefig(plot_fname, dpi=250)
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Speed: 0.5ms preprocess, 2.5ms inference, 0.0ms loss, 1.2ms postprocess per image
Results saved to runs/detect/train8
观察mAP50，在第三个Epoch时，已经达到了0.8，从第8个Epoch开始，已经稳定在了0.9，收敛很快
模型最终保存到了Results saved to runs/detect/train8中
模型转换
修改main.py文件，mode更改为 onnx，并且model路径更改为训练好的模型地址，执行python main.py即可
执行完毕后将会在刚训练好的模型路径下生成转换后的onnx模型文件
使用yolov8s预训练模型训练的模型再试试我们的预测
yolo predict task=detect model=runs/yolov8s/best.pt source=datasets/injector_datasets/images/testImages show=True
预测效果还是很不错的
![[Unity]UI和美术出图效果不一致](https://img-blog.csdnimg.cn/e27e9b850495476d931e7cc785cf9d1d.webp)
