Roboflow Python Package

Roboflow provides everything you need to build and deploy computer vision models. roboflow-python is the official Roboflow Python package. roboflow-python enables you to interact with models, datasets, and projects hosted on Roboflow.

With this Python package, you can:

1. Create and manage projects;
2. Upload images, annotations, and datasets to manage in Roboflow;
3. Start training vision models on Roboflow;
4. Run inference on models hosted on Roboflow, or Roboflow models self-hosted via Roboflow Inference, and more.

The Python package is documented on the official Roboflow documentation site. If you are developing a feature for this Python package, or need a full Python library reference, refer to the package developer documentation.

💻 Installation

You will need to have Python 3.8 or higher set up to use the Roboflow Python package.

Run the following command to install the Roboflow Python package:

pip install roboflow

For desktop features, use:

pip install "roboflow[desktop]"

pip install roboflow-slim

git clone https://github.com/roboflow-ai/roboflow-python.git
cd roboflow-python
python3 -m venv env
source env/bin/activate
pip install .

🚀 Getting Started

To use the Roboflow Python package, you first need to authenticate with your Roboflow account. You can do this by running the following command:

import roboflow
roboflow.login()

import roboflow

rf = roboflow.Roboflow(api_key="")

Quickstart

Below are some common methods used with the Roboflow Python package, presented concisely for reference. For a full library reference, refer to the Roboflow API reference documentation.

import roboflow

# Pass API key or use roboflow.login()
rf = roboflow.Roboflow(api_key="MY_API_KEY")

workspace = rf.workspace()

# creating object detection model that will detect flowers
project = workspace.create_project(
    project_name="Flower detector",
    project_type="object-detection", # Or "classification", "instance-segmentation", "semantic-segmentation"
    project_license="MIT", # "private" for private projects, only available for paid customers
    annotation="flowers" # If you plan to annotate lillys, sunflowers, etc.
)

# upload a dataset
workspace.upload_dataset(
    dataset_path="./dataset/",
    num_workers=10,
    dataset_format="yolov8", # supports yolov8, yolov5, and Pascal VOC
    project_license="MIT",
    project_type="object-detection"
)

version = project.version("VERSION_NUMBER")

# upload model weights - yolov10
version.deploy(model_type="yolov10", model_path=f”{HOME}/runs/detect/train/”, filename="weights.pt")

# run inference (a version may own several trained models; models() returns all of them)
model = version.models()[0]

img_url = "https://media.roboflow.com/quickstart/aerial_drone.jpeg"

predictions = model.predict(img_url, hosted=True).json()

print(predictions)

Search and Export

Search for images across your workspace and export matching results as a ready-to-use dataset:

workspace = rf.workspace()

# Export images matching a search query
workspace.search_export(
    query="class:person",       # search query (e.g. "tag:review", "class:dog", "*")
    format="coco",              # annotation format: coco, yolov8, yolov5, voc, etc.
    dataset="my-project",       # optional: limit to a specific project
    location="./my-export",     # optional: output directory
)

Or from the CLI:

roboflow search-export "class:person" -f coco -d my-project -l ./my-export

Library Structure

The Roboflow Python library is structured using the same Workspace, Project, and Version ontology that you will see in the Roboflow application.

import roboflow

roboflow.login()

rf = roboflow.Roboflow()

workspace = rf.workspace("WORKSPACE_URL")
project = workspace.project("PROJECT_URL")
version = project.version("VERSION_NUMBER")

The workspace, project, and version parameters are the same as those you will find in the URL addresses at app.roboflow.com and universe.roboflow.com.

Within the workspace object you can perform actions like making a new project, listing your projects, or performing active learning where you are using predictions from one project's model to upload images to a new project.

Within the project object, you can retrieve metadata about the project, list versions, generate a new dataset version with preprocessing and augmentation settings, train a model in your project, and upload images and annotations to your project.

Within the version object, you can download the dataset version in any model format, train the version on Roboflow, and deploy your own external model to Roboflow.

🏆 Contributing

We would love your input on how we can improve the Roboflow Python package! Please see our contributing guide to get started. Thank you 🙏 to all our contributors!