yolo 整体操作流程
提示
注意：后面相关的脚本执行都在这个中 
如果一定要用开发环境的终端，要选择cmd 不是powershell
1. 环境相关
1.1 有Anaconda
=>打开它
先创建一个虚拟环境yolov8
bash
conda create -n yolov8 python=3.10.16由于我创建过了，我换一个名字, 下面是安装日志
bash
(base) C:\Users\slien>conda create -n yolov8copy python=3.10.16
Retrieving notices: ...working... done
Channels:
- defaults
Platform: win-64
Collecting package metadata (repodata.json): done
Solving environment: done
## Package Plan ##
environment location: C:\Users\slien\.conda\envs\yolov8copy
added / updated specs:
- python=3.10.16
The following NEW packages will be INSTALLED:
bzip2 pkgs/main/win-64::bzip2-1.0.8-h2bbff1b_6
ca-certificates pkgs/main/win-64::ca-certificates-2025.2.25-haa95532_0
libffi pkgs/main/win-64::libffi-3.4.4-hd77b12b_1
openssl pkgs/main/win-64::openssl-3.0.16-h3f729d1_0
pip pkgs/main/win-64::pip-25.0-py310haa95532_0
python pkgs/main/win-64::python-3.10.16-h4607a30_1
setuptools pkgs/main/win-64::setuptools-75.8.0-py310haa95532_0
sqlite pkgs/main/win-64::sqlite-3.45.3-h2bbff1b_0
tk pkgs/main/win-64::tk-8.6.14-h0416ee5_0
tzdata pkgs/main/noarch::tzdata-2025a-h04d1e81_0
vc pkgs/main/win-64::vc-14.42-haa95532_5
vs2015_runtime pkgs/main/win-64::vs2015_runtime-14.42.34433-hbfb602d_5
wheel pkgs/main/win-64::wheel-0.45.1-py310haa95532_0
xz pkgs/main/win-64::xz-5.6.4-h4754444_1
zlib pkgs/main/win-64::zlib-1.2.13-h8cc25b3_1
Proceed ([y]/n)? y ========》 # 这里输入 y 回车 《============
Downloading and Extracting Packages:
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate yolov8copy 激活环境
#
# To deactivate an active environment, use
#
# $ conda deactivate 取消激活然后激活环境
bash
conda activate yolov8
1.2 没有Anaconda
没有下载一个 Anaconda官网
2. 下载代码
2.1 可以访问Github
如果可以访问Github情况下：
代码地址： https://github.com/ultralytics/ultralytics
有git情况
bash
# 找到合适的目录： 例如：D:\codeHub
# 继续用刚才的
# 依此输入下面内容 `>`后面
> D:
> cd D:\codeHub
> git clone [email protected]:ultralytics/ultralytics.git
没有git情况
点击=> 下载链接
解压到对应位置即可
2.2 无法访问Github
使用我的临时仓库： 代码地址：https://gitee.com/slienceme/ultralytics
同上面git流程
3. 开始项目
3.1 起步测试
如果有pycharm或者vscode都可以
打开项目路径 , 例如D:\codeHub\projectHub\ultralytics
打开后如下图左边vscode(插件Material Icon Theme)右侧pycharm
这里需要解释一下，为什么不直接安装这个包，直接安装就无法更改源码
首次使用需要安装，这里采用本地安装
bash
> D: # 切换盘符
> cd D:\codeHub\projectHub\ultralytics # 打开文件
# Install the package in editable mode for development
# 记得激活环境
conda env list # 查询虚拟环境列表
conda activate yolov8 # 激活环境
> pip install -e . # 本地安装
安装日志(由于我安装了，所以全是缓存，正常是下载)：
bash
(yolov8) D:\codeHub\projectHub\ultralytics>pip install -e .
Obtaining file:///D:/codeHub/projectHub/ultralytics
Installing build dependencies ... done
Checking if build backend supports build_editable ... done
Getting requirements to build editable ... done
Preparing editable metadata (pyproject.toml) ... done
Requirement already satisfied: numpy<=2.1.1,>=1.23.0 in c:\users\slien\.conda\envs\yolov8copy\lib\site-packages (from ultralytics==8.3.109) (2.1.1)
Collecting matplotlib>=3.3.0 (from ultralytics==8.3.109)
Using cached matplotlib-3.10.1-cp310-cp310-win_amd64.whl.metadata (11 kB)
..............
Using cached matplotlib-3.10.1-cp310-cp310-win_amd64.whl (8.1 MB)
Using cached pandas-2.2.3-cp310-cp310-win_amd64.whl (11.6 MB)
Using cached seaborn-0.13.2-py3-none-any.whl (294 kB)
Using cached torchvision-0.21.0-cp310-cp310-win_amd64.whl (1.6 MB)
Using cached ultralytics_thop-2.0.14-py3-none-any.whl (26 kB)
Building wheels for collected packages: ultralytics
Building editable for ultralytics (pyproject.toml) ... done
Created wheel for ultralytics: filename=ultralytics-8.3.109-0.editable-py3-none-any.whl size=23372 sha256=78af81ef074a647b44013777eee29f3e604344c268d6e1abe8a40cb1d5daa025
Stored in directory: C:\Users\slien\AppData\Local\Temp\pip-ephem-wheel-cache-hqfw8uc4\wheels\06\f8\73\62d38322a031ca64f183e0af65dafcdd52dc4ddd83fc920119
Successfully built ultralytics
Installing collected packages: pandas, matplotlib, ultralytics-thop, torchvision, seaborn, ultralytics
Successfully installed matplotlib-3.10.1 pandas-2.2.3 seaborn-0.13.2 torchvision-0.21.0 ultralytics-8.3.109 ultralytics-thop-2.0.14
(yolov8) D:\codeHub\projectHub\ultralytics>尝试执行：
bash
# 当前路径
(yolov8) D:\codeHub\projectHub\ultralytics>
> yolo predict model=yolov8n.pt source=ultralytics/assets/bus.jpg # > 运行指令 yolov8n.pt 没有首次会下载运行日志：
这里同样存在下载文件，这个有访问github问题再单独处理
bash
(yolov8) D:\codeHub\projectHub\ultralytics>yolo predict model=yolov8n.pt source=ultralytics/assets/bus.jpg
Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt to 'yolov8n.pt'...
100%|█████████████████████████████████████████████████████████████████████████████| 6.25M/6.25M [00:02<00:00, 3.19MB/s]
Ultralytics 8.3.109 🚀 Python-3.10.16 torch-2.6.0+cpu CPU (13th Gen Intel Core(TM) i5-13500H)
YOLOv8n summary (fused): 72 layers, 3,151,904 parameters, 0 gradients, 8.7 GFLOPs
image 1/1 D:\codeHub\projectHub\ultralytics\ultralytics\assets\bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign, 309.4ms
Speed: 9.8ms preprocess, 309.4ms inference, 4.4ms postprocess per image at shape (1, 3, 640, 480)
Results saved to D:\codeHub\projectHub\ultralytics\runs\detect\predict #### 《====== 保存路径 ###
💡 Learn more at https://docs.ultralytics.com/modes/predict
(yolov8) D:\codeHub\projectHub\ultralytics>
3.2 预测
模型预测基本使用
bash
yolo detect predict model=./yolov8n.pt source="ultralytics/assets/bus.jpg"python
from ultralytics import YOLO
yolo = YOLO("./yolov8n.pt", task="detect")
result = yolo(source="./ultralytics/assets/bus.jpg")
python
from ultralytics import YOLO
yolo = YOLO("./yolov8n.pt", task="detect")
# result = yolo(source="./ultralytics/assets/bus.jpg") # 图片
# result = yolo(source="./BVN.mp4") # 视频
# result = yolo(source="screen") # 屏幕检测
# result = yolo(source=0) # 摄像头检测
result = yolo(source="./ultralytics/assets/bus.jpg", save=True) # 图片 save保存结果为了好操作，安装一个jupyterlab
bash
pip install jupyterlab
python
# 检测结果可视化
import matplotlib.pyplot as plt
%matplotlib inline
plt.imshow(result[0].plot()[:,:,::-1]) # bgr -> rgb
3.3 数据集构建
- 准备数据
- labelimg 数据集标注
- make sence 数据集标注
- roboflow 公开数据集
数据准备
图片类型数据 ： 无需额外处理，直接标注
视频类型数据：进行抽帧处理，导出为图片
视频抽帧代码:
python
import cv2
import matplotlib.pyplot as plt
video = cv2.VideoCapture("./BVN.mp4")
num = 0 # 计数器
save_step = 30 # 间隔帧
while True:
ret, frame = video.read()
plt.imshow(frame)
if not ret:
break
num += 1
if num % save_step == 0:
cv2.imwrite("D:/codeHub/projectHub/ultralytics/images/"+str(num)+".jpg", frame) # 我用的绝对路径，相对路径也行labelimg
闪退问题
闪退：将canvas.py文件 526、530、531行的float改为int。（找不到文件的话，直接搜索就行）
pip list -v 查看路径
(yolov8) D:\codeHub\projectHub\ultralytics>pip list -v
Package Version Editable project location Location Installer
------------------------- -------------- ---------------------------------- ---------
anyio 4.9.0 c:\users\slien\.conda\envs\yolov8\lib\site-packages pip
进入c:\users\slien\.conda\envs\yolov8\lib\site-packages 搜索canvas.py
python
p.drawRect(int(left_top.x()), int(left_top.y()), int(rect_width), int(rect_height))
if self.drawing() and not self.prev_point.isNull() and not self.out_of_pixmap(self.prev_point):
p.setPen(QColor(0, 0, 0))
p.drawLine(int(self.prev_point.x()), 0, int(self.prev_point.x()), int(self.pixmap.height()))
p.drawLine(0, int(self.prev_point.y()), int(self.pixmap.width()), int(self.prev_point.y()))使用
安装
bash
pip install labelimg启动
bash
labelimg关键设置
bash
autosave
YOLO format
打开图片文件夹 Open Dir
以刚才视频抽帧的图片为例
设置自动保存模式 AutoSave mode
点击切换模式yolo mode
选择标注文件保存路径 Annotation
开始标记(闪退见上面)，右键选择 或者 快捷键W
快捷键W 标注框
AD是左右切换
make sence数据集标注
支持传入模型辅助标注
第二个标注软件 make sence 官网
再点一下选择标签
添加标签
整体
导出数据
公开数据集roboflow
地址 https://public.roboflow.com/object-detection
https://universe.roboflow.com/
3.4 模型训练
数据集处理
训练前准备
- images 存放图片
- train 训练集图片
- val 验证集图片
- labels 存放标签
- train 训练集标签文件，要与训练集图片名称一一对应 (名称要求一样)
- val 验证集标签文件，要与验证集图片名称一一对应
抽帧图片 + 标注文件
抽出一些图片到验证集中，注意标注需要相同
我用这些做验证集，其他作为训练集
最后给数据集起个名称，如SL，将其放到根项目的./datasets/下
找到描述文件
复制一份到项目根目录，改写，重命名
path 实在不行就用绝对路径
开始训练
bash
yolo detect train model=./yolov8n.pt data="SL.yaml" epochs=30 workers=1 batch=16 # 使用哪个模型迁移学习python
from ultralytics import YOLO
# load a model
yolo = YOLO("./yolov8n.pt")
# train the model
model.train(data="data.yaml", workers=1, epochs=50, batch=16)训练案例
python
from ultralytics import YOLO
# Load a COCO-pretrained YOLOv8n model
model = YOLO("yolov8n.pt")
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
# Run inference with the YOLOv8n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")全部可配置内容： 
复制配置文件yolo copy-cfg, 然后按照需要调参
bash
(yolov8) D:\codeHub\projectHub\ultralytics>yolo copy-cfg
D:\codeHub\projectHub\ultralytics\ultralytics\cfg\default.yaml copied to D:\codeHub\projectHub\ultralytics\default_copy.yaml
Example YOLO command with this new custom cfg:
yolo cfg='D:\codeHub\projectHub\ultralytics\default_copy.yaml' imgsz=320 batch=8
(yolov8) D:\codeHub\projectHub\ultralytics>
直接用配置文件训练
bash
yolo cfg=default_copy.yamlpython
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Global configuration YAML with settings and hyperparameters for YOLO training, validation, prediction and export
# For documentation see https://docs.ultralytics.com/usage/cfg/
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose, obb
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
# Train settings -------------------------------------------------------------------------------------------------------
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: # (str, optional) path to data file, i.e. coco8.yaml
epochs: 100 # (int) number of epochs to train for
time: # (float, optional) number of hours to train for, overrides epochs if supplied
patience: 100 # (int) epochs to wait for no observable improvement for early stopping of training
batch: 16 # (int) number of images per batch (-1 for AutoBatch)
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[h,w] for predict and export modes
save: True # (bool) save train checkpoints and predict results
save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
cache: False # (bool) True/ram, disk or False. Use cache for data loading
device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
project: # (str, optional) project name
name: # (str, optional) experiment name, results saved to 'project/name' directory
exist_ok: False # (bool) whether to overwrite existing experiment
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
verbose: True # (bool) whether to print verbose output
seed: 0 # (int) random seed for reproducibility
deterministic: True # (bool) whether to enable deterministic mode
single_cls: False # (bool) train multi-class data as single-class
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
cos_lr: False # (bool) use cosine learning rate scheduler
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
resume: False # (bool) resume training from last checkpoint
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
multi_scale: False # (bool) Whether to use multiscale during training
# Segmentation
overlap_mask: True # (bool) merge object masks into a single image mask during training (segment train only)
mask_ratio: 4 # (int) mask downsample ratio (segment train only)
# Classification
dropout: 0.0 # (float) use dropout regularization (classify train only)
# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True # (bool) validate/test during training
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
save_json: False # (bool) save results to JSON file
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
max_det: 300 # (int) maximum number of detections per image
half: False # (bool) use half precision (FP16)
dnn: False # (bool) use OpenCV DNN for ONNX inference
plots: True # (bool) save plots and images during train/val
# Predict settings -----------------------------------------------------------------------------------------------------
source: # (str, optional) source directory for images or videos
vid_stride: 1 # (int) video frame-rate stride
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
visualize: False # (bool) visualize model features
augment: False # (bool) apply image augmentation to prediction sources
agnostic_nms: False # (bool) class-agnostic NMS
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
retina_masks: False # (bool) use high-resolution segmentation masks
embed: # (list[int], optional) return feature vectors/embeddings from given layers
# Visualize settings ---------------------------------------------------------------------------------------------------
show: False # (bool) show predicted images and videos if environment allows
save_frames: False # (bool) save predicted individual video frames
save_txt: False # (bool) save results as .txt file
save_conf: False # (bool) save results with confidence scores
save_crop: False # (bool) save cropped images with results
show_labels: True # (bool) show prediction labels, i.e. 'person'
show_conf: True # (bool) show prediction confidence, i.e. '0.99'
show_boxes: True # (bool) show prediction boxes
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
# Export settings ------------------------------------------------------------------------------------------------------
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
keras: False # (bool) use Kera=s
optimize: False # (bool) TorchScript: optimize for mobile
int8: False # (bool) CoreML/TF INT8 quantization
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
simplify: True # (bool) ONNX: simplify model using `onnxslim`
opset: # (int, optional) ONNX: opset version
workspace: None # (float, optional) TensorRT: workspace size (GiB), `None` will let TensorRT auto-allocate memory
nms: False # (bool) CoreML: add NMS
# Hyperparameters ------------------------------------------------------------------------------------------------------
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lrf: 0.01 # (float) final learning rate (lr0 * lrf)
momentum: 0.937 # (float) SGD momentum/Adam beta1
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
warmup_momentum: 0.8 # (float) warmup initial momentum
warmup_bias_lr: 0.1 # (float) warmup initial bias lr
box: 7.5 # (float) box loss gain
cls: 0.5 # (float) cls loss gain (scale with pixels)
dfl: 1.5 # (float) dfl loss gain
pose: 12.0 # (float) pose loss gain
kobj: 1.0 # (float) keypoint obj loss gain
nbs: 64 # (int) nominal batch size
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
degrees: 0.0 # (float) image rotation (+/- deg)
translate: 0.1 # (float) image translation (+/- fraction)
scale: 0.5 # (float) image scale (+/- gain)
shear: 0.0 # (float) image shear (+/- deg)
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # (float) image flip up-down (probability)
fliplr: 0.5 # (float) image flip left-right (probability)
bgr: 0.0 # (float) image channel BGR (probability)
mosaic: 1.0 # (float) image mosaic (probability)
mixup: 0.0 # (float) image mixup (probability)
copy_paste: 0.0 # (float) segment copy-paste (probability)
copy_paste_mode: "flip" # (str) the method to do copy_paste augmentation (flip, mixup)
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
erasing: 0.4 # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.
# Custom config.yaml ---------------------------------------------------------------------------------------------------
cfg: # (str, optional) for overriding defaults.yaml
# Tracker settings ------------------------------------------------------------------------------------------------------
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
# Global configuration YAML with settings and hyperparameters for YOLO training, validation, prediction and export
# For documentation see https://docs.ultralytics.com/usage/cfg/
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose, obb
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
# Train settings -------------------------------------------------------------------------------------------------------
model: yolov8n.pt # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: SL.yaml # (str, optional) path to data file, i.e. coco8.yaml
epochs: 50 # (int) number of epochs to train for
time: # (float, optional) number of hours to train for, overrides epochs if supplied
patience: 50 # (int) epochs to wait for no observable improvement for early stopping of training
batch: 16 # (int) number of images per batch (-1 for AutoBatch)
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[h,w] for predict and export modes
save: True # (bool) save train checkpoints and predict results
save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
cache: False # (bool) True/ram, disk or False. Use cache for data loading
device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
project: # (str, optional) project name
name: # (str, optional) experiment name, results saved to 'project/name' directory
exist_ok: False # (bool) whether to overwrite existing experiment
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
verbose: True # (bool) whether to print verbose output
seed: 0 # (int) random seed for reproducibility
deterministic: True # (bool) whether to enable deterministic mode
single_cls: False # (bool) train multi-class data as single-class
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
cos_lr: False # (bool) use cosine learning rate scheduler
close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
resume: False # (bool) resume training from last checkpoint
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
multi_scale: False # (bool) Whether to use multiscale during training
# Segmentation
overlap_mask: True # (bool) merge object masks into a single image mask during training (segment train only)
mask_ratio: 4 # (int) mask downsample ratio (segment train only)
# Classification
dropout: 0.0 # (float) use dropout regularization (classify train only)
# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True # (bool) validate/test during training
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
save_json: False # (bool) save results to JSON file
conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
max_det: 300 # (int) maximum number of detections per image
half: False # (bool) use half precision (FP16)
dnn: False # (bool) use OpenCV DNN for ONNX inference
plots: True # (bool) save plots and images during train/val
# Predict settings -----------------------------------------------------------------------------------------------------
source: # (str, optional) source directory for images or videos
vid_stride: 1 # (int) video frame-rate stride
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
visualize: False # (bool) visualize model features
augment: False # (bool) apply image augmentation to prediction sources
agnostic_nms: False # (bool) class-agnostic NMS
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
retina_masks: False # (bool) use high-resolution segmentation masks
embed: # (list[int], optional) return feature vectors/embeddings from given layers
# Visualize settings ---------------------------------------------------------------------------------------------------
show: False # (bool) show predicted images and videos if environment allows
save_frames: False # (bool) save predicted individual video frames
save_txt: False # (bool) save results as .txt file
save_conf: False # (bool) save results with confidence scores
save_crop: False # (bool) save cropped images with results
show_labels: True # (bool) show prediction labels, i.e. 'person'
show_conf: True # (bool) show prediction confidence, i.e. '0.99'
show_boxes: True # (bool) show prediction boxes
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
# Export settings ------------------------------------------------------------------------------------------------------
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
keras: False # (bool) use Kera=s
optimize: False # (bool) TorchScript: optimize for mobile
int8: False # (bool) CoreML/TF INT8 quantization
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
simplify: True # (bool) ONNX: simplify model using `onnxslim`
opset: # (int, optional) ONNX: opset version
workspace: 2 # (float, optional) TensorRT: workspace size (GiB), `None` will let TensorRT auto-allocate memory
nms: False # (bool) CoreML: add NMS
# Hyperparameters ------------------------------------------------------------------------------------------------------
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lrf: 0.01 # (float) final learning rate (lr0 * lrf)
momentum: 0.937 # (float) SGD momentum/Adam beta1
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
warmup_momentum: 0.8 # (float) warmup initial momentum
warmup_bias_lr: 0.1 # (float) warmup initial bias lr
box: 7.5 # (float) box loss gain
cls: 0.5 # (float) cls loss gain (scale with pixels)
dfl: 1.5 # (float) dfl loss gain
pose: 12.0 # (float) pose loss gain
kobj: 1.0 # (float) keypoint obj loss gain
nbs: 64 # (int) nominal batch size
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
degrees: 0.0 # (float) image rotation (+/- deg)
translate: 0.1 # (float) image translation (+/- fraction)
scale: 0.5 # (float) image scale (+/- gain)
shear: 0.0 # (float) image shear (+/- deg)
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # (float) image flip up-down (probability)
fliplr: 0.5 # (float) image flip left-right (probability)
bgr: 0.0 # (float) image channel BGR (probability)
mosaic: 1.0 # (float) image mosaic (probability)
mixup: 0.0 # (float) image mixup (probability)
copy_paste: 0.0 # (float) segment copy-paste (probability)
copy_paste_mode: "flip" # (str) the method to do copy_paste augmentation (flip, mixup)
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
erasing: 0.4 # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.
# Custom config.yaml ---------------------------------------------------------------------------------------------------
cfg: # (str, optional) for overriding defaults.yaml
# Tracker settings ------------------------------------------------------------------------------------------------------
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]配置文件训练
bash
(yolov8) songbaoxian@ubuntu-Super-Server:~/codehub/ultralytics$ yolo cfg=default_copy.yaml
Overriding /home/songbaoxian/codehub/ultralytics/ultralytics/cfg/default.yaml with default_copy.yaml
Ultralytics 8.3.109 🚀 Python-3.10.16 torch-2.6.0+cu124 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 10825MiB)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=SL.yaml, epochs=50, time=None, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train2, 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, 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=True, 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, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train2
Overriding model.yaml nc=80 with nc=1
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 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]]
Model summary: 129 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs
Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Scanning /home/songbaoxian/codehub/ultralytics/datasets/SL/labels/train.cache... 85 images, 0 backgrounds, 0 corrupt: 100%|██████████| 85/85 [00:00<?, ?it/s]
val: Scanning /home/songbaoxian/codehub/ultralytics/datasets/SL/labels/val.cache... 8 images, 0 backgrounds, 0 corrupt: 100%|██████████| 8/8 [00:00<?, ?it/s]
Plotting labels to runs/detect/train2/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=0.002, 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 8 dataloader workers
Logging results to runs/detect/train2
Starting training for 50 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/50 2.02G 2.314 4.096 1.557 11 640: 100%|██████████| 6/6 [00:01<00:00, 4.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 8.19it/s]
all 8 8 0.00333 1 0.879 0.464
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/50 2.06G 1.781 3.4 1.2 8 640: 100%|██████████| 6/6 [00:00<00:00, 9.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.16it/s]
all 8 8 0.00333 1 0.995 0.668
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/50 2.08G 1.375 2.104 1.042 6 640: 100%|██████████| 6/6 [00:00<00:00, 9.67it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.21it/s]
all 8 8 0.00333 1 0.995 0.515
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/50 2.1G 1.349 1.687 1.107 11 640: 100%|██████████| 6/6 [00:00<00:00, 10.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.02it/s]
all 8 8 0.00333 1 0.995 0.464
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/50 2.11G 1.376 1.643 1.103 9 640: 100%|██████████| 6/6 [00:00<00:00, 10.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.62it/s]
all 8 8 0.00333 1 0.995 0.609
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/50 2.11G 1.299 1.669 1.063 7 640: 100%|██████████| 6/6 [00:00<00:00, 10.89it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.97it/s]
all 8 8 0.00333 1 0.995 0.453
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/50 2.11G 1.257 1.401 1.043 11 640: 100%|██████████| 6/6 [00:00<00:00, 11.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.80it/s]
all 8 8 0.00333 1 0.995 0.534
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/50 2.11G 1.292 1.367 1.072 9 640: 100%|██████████| 6/6 [00:00<00:00, 11.06it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.62it/s]
all 8 8 0.00333 1 0.982 0.424
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/50 2.12G 1.256 1.273 1.078 10 640: 100%|██████████| 6/6 [00:00<00:00, 10.83it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.63it/s]
all 8 8 0.00333 1 0.995 0.397
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/50 2.14G 1.237 1.25 1.09 10 640: 100%|██████████| 6/6 [00:00<00:00, 11.19it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.84it/s]
all 8 8 0.00333 1 0.654 0.365
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/50 2.15G 1.246 1.382 1.088 9 640: 100%|██████████| 6/6 [00:00<00:00, 10.56it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.43it/s]
all 8 8 0.0141 1 0.995 0.716
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/50 2.17G 1.288 1.268 1.115 7 640: 100%|██████████| 6/6 [00:00<00:00, 10.68it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.34it/s]
all 8 8 0.896 1 0.995 0.625
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/50 2.19G 1.255 1.195 1.099 15 640: 100%|██████████| 6/6 [00:00<00:00, 10.24it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.63it/s]
all 8 8 0.962 1 0.995 0.669
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/50 2.21G 1.265 1.205 1.071 4 640: 100%|██████████| 6/6 [00:00<00:00, 10.52it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.81it/s]
all 8 8 0.973 1 0.995 0.571
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/50 2.22G 1.318 1.163 1.101 13 640: 100%|██████████| 6/6 [00:00<00:00, 11.22it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.10it/s]
all 8 8 0.977 1 0.995 0.549
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/50 2.24G 1.273 1.112 1.108 8 640: 100%|██████████| 6/6 [00:00<00:00, 10.86it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.09it/s]
all 8 8 0.981 1 0.995 0.66
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/50 2.26G 1.271 1.186 1.105 6 640: 100%|██████████| 6/6 [00:00<00:00, 11.47it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 13.02it/s]
all 8 8 0.99 1 0.995 0.709
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/50 2.28G 1.132 1.094 1.016 11 640: 100%|██████████| 6/6 [00:00<00:00, 10.69it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.85it/s]
all 8 8 0.991 1 0.995 0.671
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/50 2.28G 1.246 1.136 1.096 6 640: 100%|██████████| 6/6 [00:00<00:00, 11.23it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.67it/s]
all 8 8 0.993 1 0.995 0.671
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/50 2.28G 1.219 1.021 1.082 9 640: 100%|██████████| 6/6 [00:00<00:00, 10.70it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.02it/s]
all 8 8 0.993 1 0.995 0.669
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/50 2.28G 1.176 1.034 1.032 11 640: 100%|██████████| 6/6 [00:00<00:00, 11.28it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.07it/s]
all 8 8 0.993 1 0.995 0.631
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/50 2.28G 1.194 1.006 1.066 8 640: 100%|██████████| 6/6 [00:00<00:00, 10.70it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.69it/s]
all 8 8 0.993 1 0.995 0.607
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/50 2.28G 1.261 0.9534 1.089 6 640: 100%|██████████| 6/6 [00:00<00:00, 11.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.30it/s]
all 8 8 0.992 1 0.995 0.562
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/50 2.28G 1.214 0.9964 1.062 6 640: 100%|██████████| 6/6 [00:00<00:00, 11.15it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 19.50it/s]
all 8 8 0.994 1 0.995 0.565
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/50 2.28G 1.21 1.007 1.047 4 640: 100%|██████████| 6/6 [00:00<00:00, 12.10it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.31it/s]
all 8 8 0.994 1 0.995 0.564
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/50 2.28G 1.241 0.8818 1.073 11 640: 100%|██████████| 6/6 [00:00<00:00, 11.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.78it/s]
all 8 8 0.994 1 0.995 0.637
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/50 2.28G 1.214 0.8808 1.063 13 640: 100%|██████████| 6/6 [00:00<00:00, 11.74it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.04it/s]
all 8 8 0.994 1 0.995 0.605
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/50 2.28G 1.178 0.8998 1.061 9 640: 100%|██████████| 6/6 [00:00<00:00, 11.33it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 19.41it/s]
all 8 8 0.994 1 0.995 0.558
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/50 2.28G 1.201 0.8435 1.039 10 640: 100%|██████████| 6/6 [00:00<00:00, 11.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.78it/s]
all 8 8 0.994 1 0.995 0.552
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/50 2.28G 1.137 0.8773 1.038 8 640: 100%|██████████| 6/6 [00:00<00:00, 10.81it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.82it/s]
all 8 8 0.994 1 0.995 0.649
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/50 2.28G 1.127 0.839 1.04 6 640: 100%|██████████| 6/6 [00:00<00:00, 11.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.78it/s]
all 8 8 0.993 1 0.995 0.665
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/50 2.28G 1.135 0.8685 1.035 6 640: 100%|██████████| 6/6 [00:00<00:00, 10.71it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 19.51it/s]
all 8 8 0.992 1 0.995 0.76
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/50 2.28G 1.232 0.8533 1.052 10 640: 100%|██████████| 6/6 [00:00<00:00, 11.38it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.12it/s]
all 8 8 0.992 1 0.995 0.745
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/50 2.28G 1.179 0.8403 1.047 7 640: 100%|██████████| 6/6 [00:00<00:00, 9.82it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.64it/s]
all 8 8 0.992 1 0.995 0.587
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/50 2.28G 1.137 0.8485 1.025 6 640: 100%|██████████| 6/6 [00:00<00:00, 11.22it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.91it/s]
all 8 8 0.992 1 0.995 0.564
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/50 2.28G 1.175 0.7977 1.031 6 640: 100%|██████████| 6/6 [00:00<00:00, 10.48it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 20.41it/s]
all 8 8 0.992 1 0.995 0.532
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/50 2.28G 1.199 0.7938 1.05 8 640: 100%|██████████| 6/6 [00:00<00:00, 11.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.71it/s]
all 8 8 0.992 1 0.995 0.566
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/50 2.28G 1.14 0.7583 1.052 9 640: 100%|██████████| 6/6 [00:00<00:00, 11.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.83it/s]
all 8 8 0.992 1 0.995 0.613
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/50 2.28G 1.16 0.786 1.065 12 640: 100%|██████████| 6/6 [00:00<00:00, 11.94it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.05it/s]
all 8 8 0.993 1 0.995 0.635
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/50 2.28G 1.112 0.7539 1.045 8 640: 100%|██████████| 6/6 [00:00<00:00, 11.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 19.58it/s]
all 8 8 0.993 1 0.995 0.633
Closing dataloader mosaic
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/50 2.28G 1.148 1.132 1.033 5 640: 100%|██████████| 6/6 [00:01<00:00, 5.17it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.33it/s]
all 8 8 0.993 1 0.995 0.636
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/50 2.28G 1.113 1.056 1.012 3 640: 100%|██████████| 6/6 [00:00<00:00, 10.16it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.22it/s]
all 8 8 0.992 1 0.995 0.632
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/50 2.28G 1.153 1.109 1.053 4 640: 100%|██████████| 6/6 [00:00<00:00, 10.54it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.14it/s]
all 8 8 0.992 1 0.995 0.635
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/50 2.28G 1.134 1.043 1.018 5 640: 100%|██████████| 6/6 [00:00<00:00, 10.58it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.29it/s]
all 8 8 0.992 1 0.995 0.644
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/50 2.28G 1.205 0.9981 1.045 4 640: 100%|██████████| 6/6 [00:00<00:00, 11.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.09it/s]
all 8 8 0.993 1 0.995 0.65
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/50 2.28G 1.12 0.9871 1.025 5 640: 100%|██████████| 6/6 [00:00<00:00, 10.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.35it/s]
all 8 8 0.992 1 0.995 0.611
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/50 2.28G 1.131 0.9842 1.055 6 640: 100%|██████████| 6/6 [00:00<00:00, 11.12it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.86it/s]
all 8 8 0.992 1 0.995 0.611
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/50 2.28G 1.157 0.9498 1.058 4 640: 100%|██████████| 6/6 [00:00<00:00, 10.58it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.59it/s]
all 8 8 0.992 1 0.995 0.601
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/50 2.28G 1.134 0.9769 1.018 4 640: 100%|██████████| 6/6 [00:00<00:00, 11.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.33it/s]
all 8 8 0.992 1 0.995 0.636
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/50 2.28G 1.054 0.9468 1.016 4 640: 100%|██████████| 6/6 [00:00<00:00, 10.63it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.01it/s]
all 8 8 0.992 1 0.995 0.611
50 epochs completed in 0.014 hours.
Optimizer stripped from runs/detect/train2/weights/last.pt, 6.2MB
Optimizer stripped from runs/detect/train2/weights/best.pt, 6.2MB
Validating runs/detect/train2/weights/best.pt...
Ultralytics 8.3.109 🚀 Python-3.10.16 torch-2.6.0+cu124 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 10825MiB)
Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 23.14it/s]
all 8 8 0.992 1 0.995 0.76
Speed: 0.2ms preprocess, 1.2ms inference, 0.0ms loss, 0.9ms postprocess per image
Results saved to runs/detect/train2
💡 Learn more at https://docs.ultralytics.com/modes/train
(yolov8) songbaoxian@ubuntu-Super-Server:~/codehub/ultralytics$指令训练
bash
(yolov8) D:\codeHub\projectHub\ultralytics>yolo detect train model=yolov8n.pt data=SL.yaml epochs=30 workers=1 batch=16
Ultralytics 8.3.109 🚀 Python-3.10.16 torch-2.6.0+cpu CPU (13th Gen Intel Core(TM) i5-13500H)
engine\trainer: task=detect, mode=train, model=yolov8n.pt, data=SL.yaml, epochs=30, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=1, project=None, name=train11, 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, 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=True, opset=None, workspace=None, 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, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=D:\codeHub\projectHub\yolov8\runs\detect\train11
Downloading https://ultralytics.com/assets/Arial.ttf to 'C:\Users\slien\AppData\Roaming\Ultralytics\Arial.ttf'...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 755k/755k [00:01<00:00, 769kB/s]
Overriding model.yaml nc=80 with nc=1
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 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]]
Model summary: 129 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs
Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
train: Scanning D:\codeHub\projectHub\ultralytics\datasets\SL\labels\train... 85 images, 0 backgrounds, 0 corrupt: 100%|██████████| 85/85 [00:00<00:00, 347.21it/s]
train: New cache created: D:\codeHub\projectHub\ultralytics\datasets\SL\labels\train.cache
val: Scanning D:\codeHub\projectHub\ultralytics\datasets\SL\labels\val... 8 images, 0 backgrounds, 0 corrupt: 100%|██████████| 8/8 [00:00<00:00, 1043.33it/s]
val: New cache created: D:\codeHub\projectHub\ultralytics\datasets\SL\labels\val.cache
Plotting labels to D:\codeHub\projectHub\yolov8\runs\detect\train11\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=0.002, 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 0 dataloader workers
Logging results to D:\codeHub\projectHub\yolov8\runs\detect\train11
Starting training for 30 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/30 0G 2.048 3.887 1.388 9 640: 100%|██████████| 6/6 [00:58<00:00, 9.78s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:02<00:00, 2.63s/it]
all 8 8 0.00333 1 0.995 0.728
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/30 0G 1.611 2.602 1.001 8 640: 100%|██████████| 6/6 [00:52<00:00, 8.81s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.29s/it]
all 8 8 0.00333 1 0.995 0.51
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/30 0G 1.322 1.741 1.04 8 640: 100%|██████████| 6/6 [00:52<00:00, 8.76s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.66s/it]
all 8 8 0.00333 1 0.995 0.68
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/30 0G 1.288 1.61 1.025 10 640: 100%|██████████| 6/6 [00:49<00:00, 8.30s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.15s/it]
all 8 8 0.00333 1 0.995 0.669
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/30 0G 1.275 1.527 1.03 7 640: 100%|██████████| 6/6 [00:48<00:00, 8.07s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.44s/it]
all 8 8 0.00333 1 0.995 0.59
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/30 0G 1.243 1.371 1.046 12 640: 100%|██████████| 6/6 [00:51<00:00, 8.61s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:02<00:00, 2.03s/it]
all 8 8 0.00333 1 0.995 0.562
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/30 0G 1.35 1.356 1.047 11 640: 100%|██████████| 6/6 [00:51<00:00, 8.60s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.38s/it]
all 8 8 0.00333 1 0.995 0.56
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/30 0G 1.327 1.374 1.072 7 640: 100%|██████████| 6/6 [00:52<00:00, 8.71s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.47s/it]
all 8 8 0.00333 1 0.995 0.503
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/30 0G 1.195 1.234 1.008 11 640: 100%|██████████| 6/6 [00:50<00:00, 8.45s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.47s/it]
all 8 8 0.00333 1 0.995 0.499
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/30 0G 1.236 1.241 1.008 8 640: 100%|██████████| 6/6 [00:51<00:00, 8.58s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.42s/it]
all 8 8 0.00333 1 0.995 0.419
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/30 0G 1.284 1.242 1.083 9 640: 100%|██████████| 6/6 [00:51<00:00, 8.58s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.51s/it]
all 8 8 0.0101 1 0.995 0.583
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/30 0G 1.261 1.286 1.058 6 640: 100%|██████████| 6/6 [00:51<00:00, 8.66s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.20s/it]
all 8 8 0.894 1 0.995 0.639
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/30 0G 1.244 1.175 1.039 12 640: 100%|██████████| 6/6 [00:51<00:00, 8.52s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.66s/it]
all 8 8 0.96 1 0.995 0.581
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/30 0G 1.234 1.219 1.066 7 640: 100%|██████████| 6/6 [00:51<00:00, 8.52s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.54s/it]
all 8 8 0.975 1 0.995 0.563
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/30 0G 1.241 1.13 1.036 11 640: 100%|██████████| 6/6 [00:50<00:00, 8.37s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.22s/it]
all 8 8 0.984 1 0.995 0.525
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/30 0G 1.267 1.084 1.046 8 640: 100%|██████████| 6/6 [00:50<00:00, 8.38s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.38s/it]
all 8 8 0.982 1 0.995 0.562
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/30 0G 1.253 1.077 1.048 9 640: 100%|██████████| 6/6 [00:51<00:00, 8.64s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.68s/it]
all 8 8 0.985 1 0.995 0.66
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/30 0G 1.182 1.063 1.031 9 640: 100%|██████████| 6/6 [00:50<00:00, 8.38s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.48s/it]
all 8 8 0.989 1 0.995 0.662
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/30 0G 1.207 1.172 0.9802 4 640: 100%|██████████| 6/6 [00:50<00:00, 8.40s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.16s/it]
all 8 8 0.986 1 0.995 0.653
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/30 0G 1.205 1.074 1.007 13 640: 100%|██████████| 6/6 [00:49<00:00, 8.28s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.39s/it]
all 8 8 0.99 1 0.995 0.57
Closing dataloader mosaic
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/30 0G 1.169 1.619 1.013 4 640: 100%|██████████| 6/6 [00:48<00:00, 8.04s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.51s/it]
all 8 8 0.989 1 0.995 0.516
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/30 0G 1.165 1.612 0.9998 3 640: 100%|██████████| 6/6 [00:48<00:00, 8.16s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.50s/it]
all 8 8 0.985 1 0.995 0.463
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/30 0G 1.27 1.481 1.042 4 640: 100%|██████████| 6/6 [00:50<00:00, 8.35s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.55s/it]
all 8 8 0.989 1 0.995 0.471
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/30 0G 1.215 1.374 1.033 5 640: 100%|██████████| 6/6 [00:49<00:00, 8.23s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.23s/it]
all 8 8 0.989 1 0.995 0.522
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/30 0G 1.197 1.391 1.035 5 640: 100%|██████████| 6/6 [00:49<00:00, 8.21s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.24s/it]
all 8 8 0.988 1 0.995 0.57
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/30 0G 1.222 1.474 1.014 4 640: 100%|██████████| 6/6 [00:48<00:00, 8.10s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.50s/it]
all 8 8 0.988 1 0.995 0.637
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/30 0G 1.11 1.416 0.9869 2 640: 100%|██████████| 6/6 [00:46<00:00, 7.82s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.35s/it]
all 8 8 0.99 1 0.995 0.647
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/30 0G 1.122 1.308 0.9948 3 640: 100%|██████████| 6/6 [00:49<00:00, 8.25s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.41s/it]
all 8 8 0.992 1 0.995 0.658
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/30 0G 1.214 1.389 1.037 4 640: 100%|██████████| 6/6 [00:49<00:00, 8.18s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.26s/it]
all 8 8 0.992 1 0.995 0.681
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/30 0G 1.116 1.236 0.9808 4 640: 100%|██████████| 6/6 [00:48<00:00, 8.00s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.28s/it]
all 8 8 0.993 1 0.995 0.671
30 epochs completed in 0.440 hours.
Optimizer stripped from D:\codeHub\projectHub\yolov8\runs\detect\train11\weights\last.pt, 6.2MB
Optimizer stripped from D:\codeHub\projectHub\yolov8\runs\detect\train11\weights\best.pt, 6.2MB
Validating D:\codeHub\projectHub\yolov8\runs\detect\train11\weights\best.pt...
Ultralytics 8.3.109 🚀 Python-3.10.16 torch-2.6.0+cpu CPU (13th Gen Intel Core(TM) i5-13500H)
Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.73s/it]
all 8 8 0.00333 1 0.995 0.728
Speed: 39.8ms preprocess, 123.1ms inference, 0.0ms loss, 6.8ms postprocess per image
Results saved to D:\codeHub\projectHub\yolov8\runs\detect\train11
💡 Learn more at https://docs.ultralytics.com/modes/train
(yolov8) D:\codeHub\projectHub\ultralytics>bash
(base) slience_me@ubuntu-Super-Server:~$ cd codehub/ultralytics/
(base) slience_me@ubuntu-Super-Server:~/codehub/ultralytics$ ls
BVN.mp4 CONTRIBUTING.md docker examples mkdocs.yml pyproject.toml README.zh-CN.md tests ultralytics.egg-info
CITATION.cff datasets docs LICENSE predict.ipynb README.md SL.yaml ultralytics yolov8n.pt
(base) slience_me@ubuntu-Super-Server:~/codehub/ultralytics$ conda create -n yolov8 python=3.10.16
Retrieving notices: ...working... done
Collecting package metadata (current_repodata.json): done
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 23.7.4
latest version: 25.3.1
Please update conda by running
$ conda update -n base -c defaults conda
Or to minimize the number of packages updated during conda update use
conda install conda=25.3.1
## Package Plan ##
environment location: /home/slience_me/anaconda3/envs/yolov8
added / updated specs:
- python=3.10.16
The following packages will be downloaded:
package | build
---------------------------|-----------------
ca-certificates-2025.2.25 | h06a4308_0 129 KB
openssl-3.0.16 | h5eee18b_0 5.2 MB
pip-25.0 | py310h06a4308_0 2.3 MB
python-3.10.16 | he870216_1 26.9 MB
setuptools-75.8.0 | py310h06a4308_0 1.6 MB
tzdata-2025a | h04d1e81_0 117 KB
wheel-0.45.1 | py310h06a4308_0 115 KB
xz-5.6.4 | h5eee18b_1 567 KB
------------------------------------------------------------
Total: 37.0 MB
The following NEW packages will be INSTALLED:
_libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main
_openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu
bzip2 pkgs/main/linux-64::bzip2-1.0.8-h5eee18b_6
ca-certificates pkgs/main/linux-64::ca-certificates-2025.2.25-h06a4308_0
ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.40-h12ee557_0
libffi pkgs/main/linux-64::libffi-3.4.4-h6a678d5_1
libgcc-ng pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1
libgomp pkgs/main/linux-64::libgomp-11.2.0-h1234567_1
libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1
libuuid pkgs/main/linux-64::libuuid-1.41.5-h5eee18b_0
ncurses pkgs/main/linux-64::ncurses-6.4-h6a678d5_0
openssl pkgs/main/linux-64::openssl-3.0.16-h5eee18b_0
pip pkgs/main/linux-64::pip-25.0-py310h06a4308_0
python pkgs/main/linux-64::python-3.10.16-he870216_1
readline pkgs/main/linux-64::readline-8.2-h5eee18b_0
setuptools pkgs/main/linux-64::setuptools-75.8.0-py310h06a4308_0
sqlite pkgs/main/linux-64::sqlite-3.45.3-h5eee18b_0
tk pkgs/main/linux-64::tk-8.6.14-h39e8969_0
tzdata pkgs/main/noarch::tzdata-2025a-h04d1e81_0
wheel pkgs/main/linux-64::wheel-0.45.1-py310h06a4308_0
xz pkgs/main/linux-64::xz-5.6.4-h5eee18b_1
zlib pkgs/main/linux-64::zlib-1.2.13-h5eee18b_1
Proceed ([y]/n)? y
Downloading and Extracting Packages
Preparing transaction: done
Verifying transaction: |
SafetyError: The package for ncurses located at /home/slience_me/anaconda3/pkgs/ncurses-6.4-h6a678d5_0
appears to be corrupted. The path 'bin/clear'
has an incorrect size.
reported size: 14312 bytes
actual size: 22719 bytes
SafetyError: The package for ncurses located at /home/slience_me/anaconda3/pkgs/ncurses-6.4-h6a678d5_0
appears to be corrupted. The path 'bin/infocmp'
has an incorrect size.
reported size: 63536 bytes
actual size: 71943 bytes
SafetyError: The package for ncurses located at /home/slience_me/anaconda3/pkgs/ncurses-6.4-h6a678d5_0
appears to be corrupted. The path 'bin/tabs'
has an incorrect size.
reported size: 22424 bytes
actual size: 30831 bytes
SafetyError: The package for ncurses located at /home/slience_me/anaconda3/pkgs/ncurses-6.4-h6a678d5_0
appears to be corrupted. The path 'bin/tic'
has an incorrect size.
reported size: 92248 bytes
actual size: 100655 bytes
SafetyError: The package for ncurses located at /home/slience_me/anaconda3/pkgs/ncurses-6.4-h6a678d5_0
appears to be corrupted. The path 'bin/toe'
has an incorrect size.
reported size: 22424 bytes
actual size: 30831 bytes
SafetyError: The package for ncurses located at /home/slience_me/anaconda3/pkgs/ncurses-6.4-h6a678d5_0
appears to be corrupted. The path 'bin/tput'
has an incorrect size.
reported size: 22528 bytes
actual size: 30935 bytes
SafetyError: The package for ncurses located at /home/slience_me/anaconda3/pkgs/ncurses-6.4-h6a678d5_0
appears to be corrupted. The path 'bin/tset'
has an incorrect size.
reported size: 30696 bytes
actual size: 39103 bytes
SafetyError: The package for tk located at /home/slience_me/anaconda3/pkgs/tk-8.6.14-h39e8969_0
appears to be corrupted. The path 'bin/tclsh8.6'
has an incorrect size.
reported size: 15984 bytes
actual size: 24391 bytes
SafetyError: The package for tk located at /home/slience_me/anaconda3/pkgs/tk-8.6.14-h39e8969_0
appears to be corrupted. The path 'bin/wish8.6'
has an incorrect size.
reported size: 16136 bytes
actual size: 24543 bytes
SafetyError: The package for sqlite located at /home/slience_me/anaconda3/pkgs/sqlite-3.45.3-h5eee18b_0
appears to be corrupted. The path 'bin/sqlite3'
has an incorrect size.
reported size: 1777144 bytes
actual size: 1785551 bytes
do ne
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate yolov8
#
# To deactivate an active environment, use
#
# $ conda deactivate
(base) slience_me@ubuntu-Super-Server:~/codehub/ultralytics$ conda activate yolov8
(yolov8) slience_me@ubuntu-Super-Server:~/codehub/ultralytics$ pip install -e .
Obtaining file:///home/slience_me/codehub/ultralytics
Installing build dependencies ... done
Checking if build backend supports build_editable ... done
Getting requirements to build editable ... done
Preparing editable metadata (pyproject.toml) ... done
Collecting numpy<=2.1.1,>=1.23.0 (from ultralytics==8.3.109)
Downloading numpy-2.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)
Collecting matplotlib>=3.3.0 (from ultralytics==8.3.109)
Downloading matplotlib-3.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
Collecting opencv-python>=4.6.0 (from ultralytics==8.3.109)
Downloading opencv_python-4.11.0.86-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)
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Collecting requests>=2.23.0 (from ultralytics==8.3.109)
Using cached requests-2.32.3-py3-none-any.whl.metadata (4.6 kB)
Collecting scipy>=1.4.1 (from ultralytics==8.3.109)
Downloading scipy-1.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)
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Collecting py-cpuinfo (from ultralytics==8.3.109)
Using cached py_cpuinfo-9.0.0-py3-none-any.whl.metadata (794 bytes)
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Downloading pandas-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)
Collecting seaborn>=0.11.0 (from ultralytics==8.3.109)
Using cached seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)
Collecting ultralytics-thop>=2.0.0 (from ultralytics==8.3.109)
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Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)
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Collecting pytz>=2020.1 (from pandas>=1.1.4->ultralytics==8.3.109)
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Building wheels for collected packages: ultralytics
Building editable for ultralytics (pyproject.toml) ... done
Created wheel for ultralytics: filename=ultralytics-8.3.109-0.editable-py3-none-any.whl size=23153 sha256=47c384a6db5351c3cd8268d0daddba89e449e047c8b11cbf8cd0420 6524c01ef
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Successfully built ultralytics
Installing collected packages: triton, pytz, py-cpuinfo, nvidia-cusparselt-cu12, mpmath, urllib3, tzdata, typing-extensions, tqdm, sympy, six, pyyaml, pyparsing, p sutil, pillow, packaging, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-n vrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, MarkupSafe, kiwisolver, idna, fsspec, fonttools, filelock, cycler, charset-normalizer, cert ifi, scipy, requests, python-dateutil, opencv-python, nvidia-cusparse-cu12, nvidia-cudnn-cu12, jinja2, contourpy, pandas, nvidia-cusolver-cu12, matplotlib, torch, seaborn, ultralytics-thop, torchvision, ultralytics
Successfully installed MarkupSafe-3.0.2 certifi-2025.1.31 charset-normalizer-3.4.1 contourpy-1.3.2 cycler-0.12.1 filelock-3.18.0 fonttools-4.57.0 fsspec-2025.3.2 i dna-3.10 jinja2-3.1.6 kiwisolver-1.4.8 matplotlib-3.10.1 mpmath-1.3.0 networkx-3.4.2 numpy-2.1.1 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia -cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu1 2-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-cusparselt-cu12-0.6.2 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.4.127 opencv-pyt hon-4.11.0.86 packaging-24.2 pandas-2.2.3 pillow-11.2.1 psutil-7.0.0 py-cpuinfo-9.0.0 pyparsing-3.2.3 python-dateutil-2.9.0.post0 pytz-2025.2 pyyaml-6.0.2 requests -2.32.3 scipy-1.15.2 seaborn-0.13.2 six-1.17.0 sympy-1.13.1 torch-2.6.0 torchvision-0.21.0 tqdm-4.67.1 triton-3.2.0 typing-extensions-4.13.2 tzdata-2025.2 ultralyt ics-8.3.109 ultralytics-thop-2.0.14 urllib3-2.4.0
(yolov8) slience_me@ubuntu-Super-Server:~/codehub/ultralytics$ pip list
Package Version Editable project location
------------------------ ----------- -------------------------------------
certifi 2025.1.31
charset-normalizer 3.4.1
contourpy 1.3.2
cycler 0.12.1
filelock 3.18.0
fonttools 4.57.0
fsspec 2025.3.2
idna 3.10
Jinja2 3.1.6
kiwisolver 1.4.8
MarkupSafe 3.0.2
matplotlib 3.10.1
mpmath 1.3.0
networkx 3.4.2
numpy 2.1.1
nvidia-cublas-cu12 12.4.5.8
nvidia-cuda-cupti-cu12 12.4.127
nvidia-cuda-nvrtc-cu12 12.4.127
nvidia-cuda-runtime-cu12 12.4.127
nvidia-cudnn-cu12 9.1.0.70
nvidia-cufft-cu12 11.2.1.3
nvidia-curand-cu12 10.3.5.147
nvidia-cusolver-cu12 11.6.1.9
nvidia-cusparse-cu12 12.3.1.170
nvidia-cusparselt-cu12 0.6.2
nvidia-nccl-cu12 2.21.5
nvidia-nvjitlink-cu12 12.4.127
nvidia-nvtx-cu12 12.4.127
opencv-python 4.11.0.86
packaging 24.2
pandas 2.2.3
pillow 11.2.1
pip 25.0
psutil 7.0.0
py-cpuinfo 9.0.0
pyparsing 3.2.3
python-dateutil 2.9.0.post0
pytz 2025.2
PyYAML 6.0.2
requests 2.32.3
scipy 1.15.2
seaborn 0.13.2
setuptools 75.8.0
six 1.17.0
sympy 1.13.1
torch 2.6.0
torchvision 0.21.0
tqdm 4.67.1
triton 3.2.0
typing_extensions 4.13.2
tzdata 2025.2
ultralytics 8.3.109 /home/slience_me/codehub/ultralytics
ultralytics-thop 2.0.14
urllib3 2.4.0
wheel 0.45.1
(yolov8) slience_me@ubuntu-Super-Server:~/codehub/ultralytics$ yolo detect train model=./yolov8n.pt data="SL.yaml" epochs=30 workers=1 batch=16
Creating new Ultralytics Settings v0.0.6 file ✅
View Ultralytics Settings with 'yolo settings' or at '/home/slience_me/.config/Ultralytics/settings.json'
Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settin gs.
Ultralytics 8.3.109 🚀 Python-3.10.16 torch-2.6.0+cu124 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 10825MiB)
engine/trainer: task=detect, mode=train, model=./yolov8n.pt, data=SL.yaml, epochs=30, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cach e=False, device=None, workers=1, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=Fa lse, 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, 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=F alse, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=Fal se, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_b ias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, 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, perspectiv e=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botso rt.yaml, save_dir=runs/detect/train
Downloading https://ultralytics.com/assets/Arial.ttf to '/home/slience_me/.config/Ultralytics/Arial.ttf'...
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 755k/755k [00:03<00:00, 244kB/s]
Overriding model.yaml nc=80 with nc=1
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 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]]
Model summary: 129 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs
Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.35M/5.35M [00:24<00:00, 232kB/s]
AMP: checks passed ✅
train: Scanning /home/slience_me/codehub/ultralytics/datasets/SL/labels/train... 85 images, 0 backgrounds, 0 corrupt: 100%|██████████| 85/85 [00:01<00:00, 83.24it/s]
train: New cache created: /home/slience_me/codehub/ultralytics/datasets/SL/labels/train.cache
val: Scanning /home/slience_me/codehub/ultralytics/datasets/SL/labels/val... 8 images, 0 backgrounds, 0 corrupt: 100%|██████████| 8/8 [00:00<00:00, 53.09it/s]
val: New cache created: /home/slience_me/codehub/ultralytics/datasets/SL/labels/val.cache
Plotting labels to runs/detect/train/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=0.002, 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 1 dataloader workers
Logging results to runs/detect/train
Starting training for 30 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/30 2.02G 2.311 4.153 1.502 7 640: 100%|██████████| 6/6 [00:01<00:00, 3.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 1.19it/s]
all 8 8 0.00333 1 0.296 0.146
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/30 2.05G 1.822 3.028 1.154 9 640: 100%|██████████| 6/6 [00:00<00:00, 7.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.52it/s]
all 8 8 0.00333 1 0.995 0.634
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/30 2.06G 1.429 2.044 1.016 4 640: 100%|██████████| 6/6 [00:00<00:00, 8.39it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.31it/s]
all 8 8 0.00333 1 0.995 0.684
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/30 2.08G 1.285 1.539 1.034 7 640: 100%|██████████| 6/6 [00:00<00:00, 8.17it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.39it/s]
all 8 8 0.00333 1 0.995 0.618
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/30 2.1G 1.182 1.416 0.9996 12 640: 100%|██████████| 6/6 [00:00<00:00, 8.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.08it/s]
all 8 8 0.00333 1 0.0652 0.028
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/30 2.1G 1.202 1.378 0.9993 8 640: 100%|██████████| 6/6 [00:00<00:00, 8.21it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.15it/s]
all 8 8 0.00333 1 0.995 0.597
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/30 2.1G 1.264 1.329 1.03 6 640: 100%|██████████| 6/6 [00:00<00:00, 8.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.17it/s]
all 8 8 0.00333 1 0.995 0.476
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/30 2.1G 1.274 1.289 1.064 9 640: 100%|██████████| 6/6 [00:00<00:00, 8.14it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.74it/s]
all 8 8 0.00333 1 0.955 0.765
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/30 2.12G 1.245 1.266 1.051 13 640: 100%|██████████| 6/6 [00:00<00:00, 8.53it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.15it/s]
all 8 8 0.00333 1 0.995 0.74
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/30 2.14G 1.196 1.247 1.02 10 640: 100%|██████████| 6/6 [00:00<00:00, 7.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.02it/s]
all 8 8 0.00333 1 0.995 0.667
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/30 2.16G 1.234 1.232 1.023 9 640: 100%|██████████| 6/6 [00:00<00:00, 8.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.28it/s]
all 8 8 0.00333 1 0.995 0.736
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/30 2.17G 1.292 1.142 1.024 16 640: 100%|██████████| 6/6 [00:00<00:00, 8.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.08it/s]
all 8 8 0.928 1 0.995 0.662
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/30 2.19G 1.254 1.163 1.024 8 640: 100%|██████████| 6/6 [00:00<00:00, 8.55it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.15it/s]
all 8 8 0.00868 1 0.995 0.583
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/30 2.21G 1.259 1.241 1.031 5 640: 100%|██████████| 6/6 [00:00<00:00, 8.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.54it/s]
all 8 8 0.967 1 0.995 0.583
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/30 2.22G 1.283 1.169 1.04 12 640: 100%|██████████| 6/6 [00:00<00:00, 8.62it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.20it/s]
all 8 8 0.973 1 0.995 0.611
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/30 2.24G 1.256 1.186 1.059 7 640: 100%|██████████| 6/6 [00:00<00:00, 8.47it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.20it/s]
all 8 8 0.981 1 0.995 0.582
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/30 2.26G 1.278 1.124 1.041 11 640: 100%|██████████| 6/6 [00:00<00:00, 8.83it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.14it/s]
all 8 8 0.987 1 0.995 0.629
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/30 2.26G 1.19 1.085 1.019 11 640: 100%|██████████| 6/6 [00:00<00:00, 8.38it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.77it/s]
all 8 8 0.987 1 0.995 0.652
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/30 2.26G 1.231 1.127 1.073 12 640: 100%|██████████| 6/6 [00:00<00:00, 8.76it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 11.92it/s]
all 8 8 0.984 1 0.995 0.615
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/30 2.26G 1.226 1.065 1.043 8 640: 100%|██████████| 6/6 [00:00<00:00, 8.58it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.43it/s]
all 8 8 0.991 1 0.995 0.646
Closing dataloader mosaic
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/30 2.26G 1.164 1.579 1.017 5 640: 100%|██████████| 6/6 [00:01<00:00, 3.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.73it/s]
all 8 8 0.99 1 0.995 0.689
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/30 2.26G 1.251 1.526 1.042 4 640: 100%|██████████| 6/6 [00:00<00:00, 8.66it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.92it/s]
all 8 8 0.99 1 0.995 0.61
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/30 2.26G 1.208 1.478 1.02 5 640: 100%|██████████| 6/6 [00:00<00:00, 9.05it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.58it/s]
all 8 8 0.991 1 0.995 0.597
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/30 2.26G 1.309 1.653 1.061 2 640: 100%|██████████| 6/6 [00:00<00:00, 8.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 18.22it/s]
all 8 8 0.992 1 0.995 0.567
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/30 2.26G 1.184 1.457 1.001 4 640: 100%|██████████| 6/6 [00:00<00:00, 9.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 12.45it/s]
all 8 8 0.992 1 0.995 0.58
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/30 2.26G 1.274 1.411 1.06 5 640: 100%|██████████| 6/6 [00:00<00:00, 7.51it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.13it/s]
all 8 8 0.992 1 0.995 0.557
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/30 2.26G 1.189 1.446 1.008 4 640: 100%|██████████| 6/6 [00:00<00:00, 9.55it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.40it/s]
all 8 8 0.992 1 0.995 0.621
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/30 2.26G 1.165 1.438 1.024 3 640: 100%|██████████| 6/6 [00:00<00:00, 8.86it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.85it/s]
all 8 8 0.992 1 0.995 0.641
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/30 2.26G 1.212 1.454 1.059 4 640: 100%|██████████| 6/6 [00:00<00:00, 9.73it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 10.89it/s]
all 8 8 0.992 1 0.995 0.641
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/30 2.26G 1.061 1.304 1.01 4 640: 100%|██████████| 6/6 [00:00<00:00, 9.17it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 17.80it/s]
all 8 8 0.993 1 0.995 0.626
30 epochs completed in 0.010 hours.
Optimizer stripped from runs/detect/train/weights/last.pt, 6.2MB
Optimizer stripped from runs/detect/train/weights/best.pt, 6.2MB
Validating runs/detect/train/weights/best.pt...
Ultralytics 8.3.109 🚀 Python-3.10.16 torch-2.6.0+cu124 CUDA:0 (NVIDIA GeForce RTX 2080 Ti, 10825MiB)
Model summary (fused): 72 layers, 3,005,843 parameters, 0 gradients, 8.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:00<00:00, 16.92it/s]
all 8 8 0.00333 1 0.955 0.759
Speed: 0.2ms preprocess, 1.7ms inference, 0.0ms loss, 1.3ms postprocess per image
Results saved to runs/detect/train
💡 Learn more at https://docs.ultralytics.com/modes/train
(yolov8) slience_me@ubuntu-Super-Server:~/codehub/ultralytics$训练完成
- best.pt 最好的模型
- last.pt 最后一轮的模型
测试效果
bash
yolo detect predict model=runs/detect/train/weights/best.pt source=./BVN.mp4 show=True简单训练，效果不太好，使用开源模型试试
bash
yolo detect predict model=./yolov8n.pt source=./BVN.mp4 show=True
导出onnx
bash
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained model
# Export the model
model.export(format="onnx")