Upload Custom Weights
Roboflow offers the ability to upload model weights for your custom-trained models to your Roboflow projects for model deployment.
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Roboflow offers the ability to upload model weights for your custom-trained models to your Roboflow projects for model deployment.
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Once you've completed training your custom model, upload your model weights back to your Roboflow project to take advantage of .
YOLOv8 models must be trained on ultralytics==8.0.196
YOLOv11 models must be trained on ultralytics<=8.3.40
YOLOv12 models must be trained and uploaded using ultralytics
from
Model weights uploads are currently available for:
YOLOv5 (n, s, m, l, x) Object Detection and Instance Segmentation
YOLOv7 Instance Segmentation (yolov7-seg)
YOLOv8 (n, s, m, l, x) Object Detection, Instance Segmentation, Classification, and Keypoint Detection
YOLOv9 (n, s, m, l, x) Object Detection
YOLOv10 (n, s, m, l, x) Object Detection
YOLOv11 (n, s, m, l, x) Object Detection, Instance Segmentation
YOLOv12 (n, s, m, l, x) Object Detection
YOLO-NAS (s, m, l) Object Detection
RF-DETR (rfdetr-base, rfdetr-large) Object Detection
Florence-2 (base, large) Multimodal
PaliGemma (3b-pt-224, 3b-pt-448, 3b-pt-896) Multimodal
PaliGemma 2 (3b-pt-224, 3b-pt-448, 3b-pt-896) Multimodal
Roboflow provides two distinct approaches for deploying models to your projects, each serving different use cases and organizational needs. The choice between versioned and versionless deployments depends on whether you need to track model evolution alongside dataset versions or want to share models across multiple projects in your workspace.
Versionless Deployments (recommended)
Tied to the workspace level
Can be deployed to multiple projects simultaneously
Ideal for sharing models across different projects within the same workspace
Versioned Deployments
Tied to specific project versions
One model per dataset version
Ideal for tracking model evolution alongside dataset versions
Before starting, make sure you have roboflow>=1.1.53
to use the versionless deploy.
To upload versionless custom weights, use the workspace.deploy_model
method in the Python SDK.
model_type (str): The type of model being deployed (e.g., "yolov8", "yolov11")
model_path (str): File path to the directory containing the model weights
project_ids (list[str]): List of project IDs to deploy the model to
model_name (str): Name to identify the model
filename (str, optional): Name of the weights file (defaults to "weights/best.pt")
Check out your model in the "Models" tab of Roboflow
Prefer to use versionless upload, this way your model belongs to the workspace and not just the project, and the version. One versioned model must be linked to just one corresponding dataset version. If you do not have a version generated in your dataset, you can create on or via the .
See docs on or reference the example below.
Visit the URL shown in the terminal:
Run your model locally with .