## Models
Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration
files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted
and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image
segmentation tasks.
These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like
instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms,
from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this
directory provides a great starting point for your custom model development needs.
To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've
selected a model, you can use the provided `*.yaml` file to train and deploy your custom YOLO model with ease. See full
details at the Ultralytics [Docs](https://docs.ultralytics.com), and if you need help or have any questions, feel free
to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!
### Usage
Model `*.yaml` files may be used directly in the Command Line Interface (CLI) with a `yolo` command:
```bash
yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
```
They may also be used directly in a Python environment, and accepts the same
[arguments](https://docs.ultralytics.com/config/) as in the CLI example above:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.yaml") # build a YOLOv8n model from scratch
model.info() # display model information
model.train(data="coco128.yaml", epochs=100) # train the model
```
没有合适的资源?快使用搜索试试~ 我知道了~
YOLOv8目标检测源码.rar
共151个文件
py:66个
yaml:39个
md:21个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
5星 · 超过95%的资源 40 下载量 67 浏览量
2023-05-11
17:44:12
上传
评论 20
收藏 919KB RAR 举报
温馨提示
YOLOv8目标检测源码.rar
资源推荐
资源详情
资源评论
收起资源包目录
YOLOv8目标检测源码.rar (151个子文件)
CITATION.cff 611B
setup.cfg 2KB
CNAME 20B
style.css 684B
Dockerfile 2KB
Dockerfile-arm64 2KB
Dockerfile-cpu 2KB
favicon.ico 5KB
MANIFEST.in 108B
tutorial.ipynb 42KB
bus.jpg 476KB
zidane.jpg 165KB
说明文档.md 20KB
config.md 17KB
segmentation.md 6KB
detection.md 5KB
classification.md 5KB
index.md 5KB
hub.md 5KB
python.md 4KB
说明文档.md 4KB
cli.md 3KB
ops.md 3KB
quickstart.md 3KB
engine.md 3KB
README.md 2KB
app.md 1KB
nn.md 459B
base_trainer.md 278B
base_pred.md 275B
base_val.md 274B
exporter.md 72B
model.md 34B
v5loader.py 54KB
exporter.py 39KB
augment.py 30KB
modules.py 29KB
ops.py 27KB
trainer.py 25KB
metrics.py 22KB
autobackend.py 20KB
tasks.py 18KB
v5augmentations.py 17KB
torch_utils.py 15KB
plotting.py 14KB
__init__.py 13KB
utils.py 13KB
val.py 12KB
predictor.py 12KB
val.py 12KB
instance.py 11KB
stream_loaders.py 11KB
checks.py 10KB
train.py 10KB
tal.py 9KB
dataset.py 9KB
validator.py 9KB
model.py 8KB
base.py 8KB
train.py 7KB
utils.py 7KB
train.py 6KB
downloads.py 6KB
__init__.py 5KB
build.py 5KB
predict.py 5KB
session.py 4KB
predict.py 4KB
files.py 4KB
hydra_patch.py 4KB
base.py 3KB
autobatch.py 3KB
test_engine.py 3KB
test_python.py 3KB
hub.py 3KB
predict.py 2KB
auth.py 2KB
setup.py 2KB
dist.py 2KB
loss.py 2KB
val.py 2KB
test_cli.py 2KB
cli.py 2KB
clearml.py 2KB
comet.py 2KB
dataset_wrappers.py 1KB
__init__.py 1KB
tensorboard.py 723B
__init__.py 296B
__init__.py 274B
__init__.py 271B
__init__.py 262B
__init__.py 184B
__init__.py 175B
__init__.py 63B
__init__.py 59B
__init__.py 0B
__init__.py 0B
__init__.py 0B
get_coco.sh 2KB
共 151 条
- 1
- 2
Matlab仿真实验室
- 粉丝: 2w+
- 资源: 2180
下载权益
C知道特权
VIP文章
课程特权
开通VIP
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
- 筷手引流工具.apk
- 论文(最终)_20240430235101.pdf
- 基于python编写的Keras深度学习框架开发,利用卷积神经网络CNN,快速识别图片并进行分类
- 最全空间计量实证方法(空间杜宾模型和检验以及结果解释文档).txt
- 5uonly.apk
- 蓝桥杯Python组的历年真题
- 2023-04-06-项目笔记 - 第一百十九阶段 - 4.4.2.117全局变量的作用域-117 -2024.04.30
- 2023-04-06-项目笔记 - 第一百十九阶段 - 4.4.2.117全局变量的作用域-117 -2024.04.30
- 前端开发技术实验报告:内含4四实验&实验报告
- Highlight Plus v20.0.1
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功
- 1
- 2
- 3
- 4
- 5
- 6
前往页