Upload Custom Model Weights

Upload your own trained model weights to Roboflow for hosted inference.

Roboflow can host model weights you trained outside the platform - locally with Ultralytics, in a Sagemaker job, or in any framework that exports to PyTorch / ONNX. Once uploaded, the model is reachable through every Roboflow inference path: the SDK's model.predict(), the REST API, hosted Workflows, and Dedicated Deployments.

The SDK exposes two equivalent paths:

• Workspace.deploy_model() - for one-shot uploads against an arbitrary set of projects.

• Version.deploy() - for attaching weights to a specific dataset version.

Upload weights to a version

Use Version.deploy() when the weights correspond to a specific Roboflow dataset version (recommended - keeps the dataset → weights → inference URL chain coherent).

import roboflow

rf = roboflow.Roboflow(api_key="YOUR_API_KEY")
version = rf.workspace().project("my-detector").version(3)

version.deploy(
    model_type="yolov8",                  # or yolov5, yolov11, rfdetr-nano, etc.
    model_path="./training-runs/exp42",    # directory containing weights/best.pt
    filename="weights/best.pt",            # optional, default
)

model_path is the run directory; filename is the path within that directory to the .pt weights file. The SDK uploads the weights and registers them against the version, after which version.model.predict(...) runs against the uploaded weights instead of any prior Roboflow-trained version.

Upload weights to one or more projects

Use Workspace.deploy_model() when the weights aren't tied to a specific dataset version (e.g. an externally trained generalist model that you want to make available across several projects).

ws = rf.workspace()

ws.deploy_model(
    model_type="yolov8",
    model_path="./generalist-run",
    project_ids=["my-detector", "my-classifier"],
    model_name="generalist-v3",
    filename="weights/best.pt",
)

Parameters

• model_type (str) - model architecture identifier. Pass an invalid value to get the full list back as an error.

• model_path (str) - directory containing the trained weights.

• project_ids (list[str]) - projects to attach the model to.

• model_name (str) - name for the uploaded model in the web app.

• filename (str, default "weights/best.pt") - path within model_path to the weights file.

Where it ends up

After upload completes, the model is available:

• In the Roboflow web app under the project's Versions → Deploy tab.

• Through the SDK as version.model.predict(...).

• Through the REST API at https://serverless.roboflow.com/infer/....

• As a target for Dedicated Deployments.

For a deeper walk-through of supported architectures and edge formats, see the product documentation on uploading custom weights.