Command Line Interface
The Roboflow CLI will improve workflows and the ability to use Roboflow when building computer vision applications.
To install the Roboflow CLI globally on your system using
npm:npm i -g roboflow-cliAfter that you should have the Roboflow command available to you in your terminal of choice.
Roboflow CLI
Once you have the Roboflow CLI installed on your system, you will want to authorize it to access your workspaces. There is an auth flow built into Roboflow to do this, type:
roboflow auth
Authing the roboflow CLI to access your workspaces
This will open a browser window (or you can copy the url it prints into a browser if your terminal doesn’t support opening a browser).
You’ll sign in to Roboflow, if you are not already, and then a one-time auth token will be generated that you can paste into the CLI. If you have multiple workspaces, you can select exactly which ones you want to allow the CLI to access.
When you paste the auth token into the CLI, you will get your API keys and some basic workspace information. The CLI will store this info in a local config file at "
~/.config/roboflow/config.json"
You can see where the config is stored and the configuration data by running roboflow config. If you want to reset your CLI, or to delete the API keys from your local system, you can type
roboflow config reset.
It can be tricky to work on projects on your local machine, manage them with API keys, and navigate multiple workspaces. Storing the API keys locally makes it easier to manage and you can let the tool worry about using the right API key based on the workspaces or projects you're accessing. Remember that the API keys are stored in your local home directly and you may not want to authorize the CLI tool on a shared account or computer that you don’t control fully.
The workspace and project subcommands work similarly. You can use them to list your workspaces or projects using
roboflow workspace list or roboflow project list.
Display a list of your workspaces using the CLI
Note that most of the commands will use the default workspace for listing projects or making API calls. You can also pass a
-w <YOUR_WORKSPACE_ID> option to most of the commands to use a specific workspace; or you can change which workspace is the default workspace by running roboflow select. This is only applicable if you have multiple workspaces authorized. If there is only one workspace, it will always be the default.You can get the full info for a workspace or project using
roboflow workspace get or roboflow project get <PROJECT ID>. You can get project info for any public project, like from Roboflow Universe, or private projects in workspaces you have authorized.
The commands mostly print JSON to the command line which is in the same format as that from our REST API; this makes it easy to use for automation tasks or CI/CD integration needs you may have and also is pretty easy to read for us humans.
Let’s say you want to get the details for this mask wearing dataset on universe; you can just type:roboflow project get mask-wearingVideo tutorial of uploading images with the CLI. (Note: the token generated for your uploads should be kept secret and private. The one shown in this tutorial has been invalidated.)
You’ll especially want to use this if you find yourself with bulk (1000+) images or datasets on your local system that you want to upload to Roboflow or you have written custom upload scripts to use our API.
Let’s assume you have a file,
image.jpg, in you current directory: You can type:roboflow upload image1.jpgThis will ask you which of your projects to upload into, but you can also skip that by specifying it explicitly using the
-p option to the command.
If you have annotations for your image such as a file called
image.xml:roboflow upload image1.jpg -a image1.xml
If you have many images:
roboflow upload *.jpg
If you have many images with annotations:
You can pass a special “[filename]” value to the
-a option that will match the annotation file name based on the name of the image. This would upload image1.jpg with annotations from image1.xml, and image2.jpg with annotations from image2.xml, etcroboflow upload *.jpg -a “[filename].xml”This only works if you have one annotation file for each image. If you have an entire dataset in a common format, like one downloaded from Roboflow Universe, you can also use the
import command.You can import an entire dataset from your local file system using the import subcommand. This is a way to import your pre-existing datasets or any dataset from Roboflow Universe into your Roboflow Projects.
To try this out on an example dataset from Roboflow Universe, you can download the Aerial Maritime Dataset.
Let’s say you download the dataset in the COCO format. Just unzip the dataset you downloaded from Roboflow Universe, and then import the project into one of your own projects by pointing the roboflow import command at the folder:
roboflow import ./Aerial\ Maritime.v24i.coco/
This will understand most common computer vision dataset formats and assign train/valid/test splits and annotation metadata to the images being uploaded.You can also run inference on images from the command line:
roboflow infer -m mask-wearing/18 image.jpgHere the
-m option specifies which version to pull the model from. This specific example will run object detection against the latest model of this public universe face mask detection project.
If you have trained your own custom model you can substitute the model/version for one of your own. This will work for object-detection, classification, instance-segmentation, or semantic-segmentation models.If you have ideas or feedback that would help make this more useful, the CLI is open source and on Github for you to contribute. We'd welcome your involvement.
Last modified 1mo ago