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feat: HSAP platform v2 — modular navigation, quality review, audit log, world model simulation

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Major changes:
- New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind
- 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理
- Data catalog with charts (DMS/ADAS/Lane 3-tab view)
- Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance
- Audit enhancements: batch operations, rejection categories, Feishu notifications
- Operation audit log (操作日志)
- World model simulation studio (仿真工坊)
- Dataset version management with snapshots and diff
- ADAS 7-class dataset integration (138K images organized + compressed)
- User management with Feishu integration and pagination
- CRUD/search/filter on all pages, card layout redesign
- PIL-optimized image overlay rendering
- Auto-snapshot on build, in_review workflow stage
- Removed embedded algorithm code (now in workspace)
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Chengfang Lu
2026-06-03 11:40:21 +08:00
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---
comments: true
description: Explore the YOLO command line interface (CLI) for easy execution of detection tasks without needing a Python environment.
keywords: YOLO CLI, command line interface, YOLO commands, detection tasks, Ultralytics, model training, model prediction
---
# Command Line Interface
The Ultralytics command line interface (CLI) provides a straightforward way to use Ultralytics YOLO models without needing a Python environment. The CLI supports running various tasks directly from the terminal using the `yolo` command, requiring no customization or Python code.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/GsXGnb-A4Kc?start=19"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Mastering Ultralytics YOLO: CLI
</p>
!!! example
=== "Syntax"
Ultralytics `yolo` commands use the following syntax:
```bash
yolo TASK MODE ARGS
```
Where:
- `TASK` (optional) is one of [detect, segment, classify, pose, obb]
- `MODE` (required) is one of [train, val, predict, export, track, benchmark]
- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults.
See all ARGS in the full [Configuration Guide](cfg.md) or with `yolo cfg`.
=== "Train"
Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial [learning rate](https://www.ultralytics.com/glossary/learning-rate) of 0.01:
```bash
yolo train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01
```
=== "Predict"
Predict using a pretrained segmentation model on a YouTube video at image size 320:
```bash
yolo predict model=yolo26n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
```
=== "Val"
Validate a pretrained detection model with a [batch size](https://www.ultralytics.com/glossary/batch-size) of 1 and image size 640:
```bash
yolo val model=yolo26n.pt data=coco8.yaml batch=1 imgsz=640
```
=== "Export"
Export a YOLO classification model to ONNX format with image size 224x128 (no TASK required):
```bash
yolo export model=yolo26n-cls.pt format=onnx imgsz=224,128
```
=== "Special"
Run special commands to view version, settings, run checks, and more:
```bash
yolo help
yolo checks
yolo version
yolo settings
yolo copy-cfg
yolo cfg
```
Where:
- `TASK` (optional) is one of `[detect, segment, classify, pose, obb]`. If not explicitly passed, YOLO will attempt to infer the `TASK` from the model type.
- `MODE` (required) is one of `[train, val, predict, export, track, benchmark]`
- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults. For a full list of available `ARGS`, see the [Configuration](cfg.md) page and `default.yaml`.
!!! warning
Arguments must be passed as `arg=val` pairs, separated by an equals `=` sign and delimited by spaces between pairs. Do not use `--` argument prefixes or commas `,` between arguments.
- `yolo predict model=yolo26n.pt imgsz=640 conf=0.25` &nbsp; ✅
- `yolo predict model yolo26n.pt imgsz 640 conf 0.25` &nbsp; ❌
- `yolo predict --model yolo26n.pt --imgsz 640 --conf 0.25` &nbsp; ❌
## Train
Train YOLO on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments, see the [Configuration](cfg.md) page.
!!! example
=== "Train"
Start training YOLO26n on COCO8 for 100 epochs at image size 640:
```bash
yolo detect train data=coco8.yaml model=yolo26n.pt epochs=100 imgsz=640
```
=== "Resume"
Resume an interrupted training session:
```bash
yolo detect train resume model=last.pt
```
## Val
Validate the [accuracy](https://www.ultralytics.com/glossary/accuracy) of the trained model on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
!!! example
=== "Official"
Validate an official YOLO26n model:
```bash
yolo detect val model=yolo26n.pt
```
=== "Custom"
Validate a custom-trained model:
```bash
yolo detect val model=path/to/best.pt
```
## Predict
Use a trained model to run predictions on images.
!!! example
=== "Official"
Predict with an official YOLO26n model:
```bash
yolo detect predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
```
=== "Custom"
Predict with a custom model:
```bash
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'
```
## Export
Export a model to a different format like ONNX or CoreML.
!!! example
=== "Official"
Export an official YOLO26n model to ONNX format:
```bash
yolo export model=yolo26n.pt format=onnx
```
=== "Custom"
Export a custom-trained model to ONNX format:
```bash
yolo export model=path/to/best.pt format=onnx
```
Available Ultralytics export formats are in the table below. You can export to any format using the `format` argument, i.e., `format='onnx'` or `format='engine'`.
{% include "macros/export-table.md" %}
See full `export` details on the [Export](../modes/export.md) page.
## Overriding Default Arguments
Override default arguments by passing them in the CLI as `arg=value` pairs.
!!! tip
=== "Train"
Train a detection model for 10 epochs with a learning rate of 0.01:
```bash
yolo detect train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01
```
=== "Predict"
Predict using a pretrained segmentation model on a YouTube video at image size 320:
```bash
yolo segment predict model=yolo26n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
```
=== "Val"
Validate a pretrained detection model with a batch size of 1 and image size 640:
```bash
yolo detect val model=yolo26n.pt data=coco8.yaml batch=1 imgsz=640
```
## Overriding Default Config File
Override the `default.yaml` configuration file entirely by passing a new file with the `cfg` argument, such as `cfg=custom.yaml`.
To do this, first create a copy of `default.yaml` in your current working directory with the `yolo copy-cfg` command, which creates a `default_copy.yaml` file.
You can then pass this file as `cfg=default_copy.yaml` along with any additional arguments, like `imgsz=320` in this example:
!!! example
=== "CLI"
```bash
yolo copy-cfg
yolo cfg=default_copy.yaml imgsz=320
```
## Solutions Commands
Ultralytics provides ready-to-use solutions for common computer vision applications through the CLI. These solutions simplify the implementation of complex tasks like object counting, workout monitoring, and queue management.
!!! example
=== "Count"
Count objects in a video or live stream:
```bash
yolo solutions count show=True
yolo solutions count source="path/to/video.mp4" # specify video file path
```
=== "Workout"
Monitor workout exercises using a pose model:
```bash
yolo solutions workout show=True
yolo solutions workout source="path/to/video.mp4" # specify video file path
# Use keypoints for ab-workouts
yolo solutions workout kpts=[5, 11, 13] # left side
yolo solutions workout kpts=[6, 12, 14] # right side
```
=== "Queue"
Count objects in a designated queue or region:
```bash
yolo solutions queue show=True
yolo solutions queue source="path/to/video.mp4" # specify video file path
yolo solutions queue region="[(20, 400), (1080, 400), (1080, 360), (20, 360)]" # configure queue coordinates
```
=== "Inference"
Perform object detection, instance segmentation, or pose estimation in a web browser using Streamlit:
```bash
yolo solutions inference
yolo solutions inference model="path/to/model.pt" # use custom model
```
=== "Help"
View available solutions and their options:
```bash
yolo solutions help
```
For more information on Ultralytics solutions, visit the [Solutions](../solutions/index.md) page.
## FAQ
### How do I use the Ultralytics YOLO command line interface (CLI) for model training?
To train a model using the CLI, execute a single-line command in the terminal. For example, to train a detection model for 10 epochs with a [learning rate](https://www.ultralytics.com/glossary/learning-rate) of 0.01, run:
```bash
yolo train data=coco8.yaml model=yolo26n.pt epochs=10 lr0=0.01
```
This command uses the `train` mode with specific arguments. For a full list of available arguments, refer to the [Configuration Guide](cfg.md).
### What tasks can I perform with the Ultralytics YOLO CLI?
The Ultralytics YOLO CLI supports various tasks, including [detection](../tasks/detect.md), [segmentation](../tasks/segment.md), [classification](../tasks/classify.md), [pose estimation](../tasks/pose.md), and [oriented bounding box detection](../tasks/obb.md). You can also perform operations like:
- **Train a Model**: Run `yolo train data=<data.yaml> model=<model.pt> epochs=<num>`.
- **Run Predictions**: Use `yolo predict model=<model.pt> source=<data_source> imgsz=<image_size>`.
- **Export a Model**: Execute `yolo export model=<model.pt> format=<export_format>`.
- **Use Solutions**: Run `yolo solutions <solution_name>` for ready-made applications.
Customize each task with various arguments. For detailed syntax and examples, see the respective sections like [Train](#train), [Predict](#predict), and [Export](#export).
### How can I validate the accuracy of a trained YOLO model using the CLI?
To validate a model's [accuracy](https://www.ultralytics.com/glossary/accuracy), use the `val` mode. For example, to validate a pretrained detection model with a [batch size](https://www.ultralytics.com/glossary/batch-size) of 1 and an image size of 640, run:
```bash
yolo val model=yolo26n.pt data=coco8.yaml batch=1 imgsz=640
```
This command evaluates the model on the specified dataset and provides performance metrics like [mAP](https://www.ultralytics.com/glossary/mean-average-precision-map), [precision](https://www.ultralytics.com/glossary/precision), and [recall](https://www.ultralytics.com/glossary/recall). For more details, refer to the [Val](#val) section.
### What formats can I export my YOLO models to using the CLI?
You can export YOLO models to various formats including ONNX, TensorRT, CoreML, TensorFlow, and more. For instance, to export a model to ONNX format, run:
```bash
yolo export model=yolo26n.pt format=onnx
```
The export command supports numerous options to optimize your model for specific deployment environments. For complete details on all available export formats and their specific parameters, visit the [Export](../modes/export.md) page.
### How do I use the pre-built solutions in the Ultralytics CLI?
Ultralytics provides ready-to-use solutions through the `solutions` command. For example, to count objects in a video:
```bash
yolo solutions count source="path/to/video.mp4"
```
These solutions require minimal configuration and provide immediate functionality for common computer vision tasks. To see all available solutions, run `yolo solutions help`. Each solution has specific parameters that can be customized to fit your needs.