Description

<div align="center"> <p> <a align="center" href="" target="_blank"> <img width="100%" src="https://github.com/roboflow/roboflow-python/assets/37276661/528ed065-d5ac-4f9a-942e-0d211b8d97de" > </a> </p> <br> [notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [collect](https://github.com/roboflow/roboflow-collect) | [supervision](https://github.com/roboflow/supervision) <br> [](https://badge.fury.io/py/roboflow) [](https://pypistats.org/packages/roboflow) [](https://github.com/roboflow/roboflow-python/blob/main/LICENSE.md) [](https://badge.fury.io/py/roboflow) </div> # Roboflow Python Package [Roboflow](https://roboflow.com) 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](https://github.com/roboflow/inference), and more. The Python package is documented on the [official Roboflow documentation site](https://docs.roboflow.com/api-reference/introduction). If you are developing a feature for this Python package, or need a full Python library reference, refer to the [package developer documentation](https://roboflow.github.io/roboflow-python/). ## 💻 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: ```bash pip install roboflow ``` For desktop features, use: ```bash pip install "roboflow[desktop]" ``` <details> <summary>Lightweight install (roboflow-slim)</summary> If you only need vision events, workspace management, and the CLI (no image processing, inference, or training), install the lightweight package: ```bash pip install roboflow-slim ``` This skips heavy dependencies like OpenCV, NumPy, Matplotlib, and Pillow, reducing install size from ~400MB to ~50MB. Useful for embedded devices, CI pipelines, and serverless environments. Both packages share the same codebase and version. `pip install roboflow` includes everything. </details> <details> <summary>Install from source</summary> You can also install the Roboflow Python package from source using the following commands: ```bash git clone https://github.com/roboflow-ai/roboflow-python.git cd roboflow-python python3 -m venv env source env/bin/activate pip install . ``` </details> <details> <summary>Command line tool</summary> By installing roboflow python package you can use some of its functionality in the command line (without having to write python code). See [CLI-COMMANDS.md](CLI-COMMANDS.md) </details> ## 🚀 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: ```python import roboflow roboflow.login() ``` <details> <summary>Authenticate with an API key</summary> You can also authenticate with an API key by using the following code: ```python import roboflow rf = roboflow.Roboflow(api_key="") ``` [Learn how to retrieve your Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key). </details> ## 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](https://docs.roboflow.com/api-reference). ```python 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