Upload Weights

Roboflow offers the ability to upload model weights for your custom-trained models to your Roboflow projects for model deployment.
Overview
Once you've completed training your custom model, upload your model weights back to your Roboflow project to take advantage of Roboflow Inference.
Model weights uploads are currently available for:
YOLOv5 (n, s, m, l, x) Object Detection & Instance Segmentation
YOLOv7 Instance Segmentation (yolov7-seg)
YOLOv8 (n, s, m, l, x) Object Detection, Instance Segmentation, & Classification
You need the roboflow>=1.0.1 to use the .deploy() command successfully
How to Train YOLOv5 On a Custom Dataset
Roboflow Blog
YOLOv5 Object Detection Model Training Guide
How to Train YOLOv7 Instance Segmentation on a Custom Dataset
Roboflow Blog
YOLOv7 Instance Segmentation Model Training Guide
How to Train YOLOv8 Object Detection on a Custom Dataset
Roboflow Blog
YOLOv8 Object Detection Model Training Guide
After successfully training your custom YOLOv5, YOLOv7 or YOLOv8 model, use the .deploy() function to upload your model weights back to your Roboflow Object Detection project.
Model weights uploads are only available for dataset versions without a trained model (i.e not yet trained with Roboflow Train, and no YOLOv5, YOLOv7 or YOLOv8 model weights uploaded).
For YOLOv5, the.deploy()command must be run from the root of the YOLOv5 repository.
Dataset version Export UI for versions without a trained model
How to Deploy YOLOv5 Models with Roboflow
Roboflow Blog
Deploy YOLOv5 Models with Roboflow
Launch: Deploy Custom Model Weights with Roboflow
Roboflow Blog
Accepted Model Weights
1.Model weights trained with YOLOv5n or YOLOv8n - Object Detection, Classification, Instance Segmentation
2.Model weights trained with YOLOv5s or YOLOv8v8s - Object Detection, Classification, Instance Segmentation
3.Model weights trained with YOLOv5m or YOLOv8vm - Object Detection, Classification, Instance Segmentation
4.Model weights trained with YOLOv5l or YOLOv8vl - Object Detection, Classification, Instance Segmentation
5.Model weights trained with YOLOv5x or YOLOv8vx - Object Detection, Classification, Instance Segmentation
6.Model Weights trained with YOLOv7 - Instance Segmentation Only
NOTE: Larger model sizes provide better training results. However, the larger the model size, the slower the training time, and inference (model prediction) speed. Consider whether you're looking for real-time inference on fast-moving objects or video feeds (better to use a smaller model), or you are processing data after it is collected, and more concerned with higher prediction accuracy (choose a larger model). 
​Ultralytics YOLOv5 model architecture benchmarks (COCO dataset)
​Ultralytics YOLOv8 model architecture benchmarks (COCO dataset)
​YOLOv7-seg model architecture benchmarks (COCO dataset)
Code Snippet for Model Weights Upload
The dataset export code you receive after selecting the "Export" button on your dataset/project's Version page will look like this:
​
​
​
Roboflow Python package - YOLOv5 Dataset Export Code Snippet
The dataset export code you receive from the "Get Snippet" button for YOLOv8 will look like this:
Roboflow Python package - YOLOv8 Dataset Export Code Snippet
You will need to set the version variable to use the .deploy() function:
In this case, since v27 (version 27) was exported, the variable assignment will look like this:
# setting the version variable
version = project.version(27)
The .deploy() function takes 2 arguments:
version.deploy(model_type, model_path)
1.The model architecture for the model weights you are uploading to Roboflow (i.e model_type = "yolov5" or model_type = "yolov8")
2.The path to your model weights (i.e model_path = runs/train/yolov5s_results/ for YOLOv5s or model_path = runs/detect/train/ for YOLOv8). NOTE: weights/best.pt is appended to the model_path provided.
​Model upload appends weights/best.pt to model_path​
As a reminder, this will only work for dataset versions that do not yet have a trained model.
In the example below, weights upload is performed for v27 (version 27) of this Construction Site Safety dataset: https://universe.roboflow.com/roboflow-universe-projects/construction-site-safety/.
Once your weights are uploaded, you will be able to utilize Roboflow features for model deployment. You won't be able to export model weights at this time.
​Saving a Copy of Your Model Weights​
Upload YOLOv5 Model Weights
Object Detection, Classification, Instance Segmentation:
# upload model weights for YOLOv5 Object Detection deployment
# set the version number to the version you export
# ensure version number does not yet have a trained model
version = project.version(27)
version.deploy("yolov5", "runs/train/yolov5s_results/") #auto-appends weights/best.pt to model_path
Note: If you are training YOLOv5s then the path to your weights will be "runs/train/yolov5s_results/", (e.g, version.deploy("yolov5", "runs/train/yolov5s_results/"))
YOLOv5n: "runs/train/yolov5n_results/"
YOLOv5m: "runs/train/yolov5m_results/"
YOLOv5l: "runs/train/yolov5l_results/"
YOLOv5x: "runs/train/yolov5x_results/"
Upload YOLOv8 Model Weights
Object Detection:
# upload model weights for YOLOv8 Object Detection deployment
# set the version number to the version you export
# ensure version number does not yet have a trained model
version = project.version(27)
version.deploy("yolov8", "runs/detect/train/") #auto-appends weights/best.pt to model_path
Classification:
# upload model weights for YOLOv8 Object Detection deployment
# set the version number to the version you export
# ensure version number does not yet have a trained model
version = project.version(27)
version.deploy("yolov8", "runs/classify/train/") #auto-appends weights/best.pt to model_path
Instance Segmentation:
# upload model weights for YOLOv8 Object Detection deployment
# set the version number to the version you export
# ensure version number does not yet have a trained model
version = project.version(27)
version.deploy("yolov8", "runs/segment/train/") #auto-appends weights/best.pt to model_path
Upload YOLOv7 Instance Segmentation Model Weights
yolov7-seg.pt
# upload model weights for YOLOv8 Object Detection deployment
# set the version number to the version you export
# ensure version number does not yet have a trained model
version = project.version(27)
version.deploy("yolov8", "runs/detect/train/") #auto-appends weights/best.pt to model_path
If weights upload is attempted for dataset versions that already have a trained model (a Roboflow Train model or uploaded YOLOv5, YOLOv7 or YOLOv8 weights) then you will receive a RuntimeError - "This version already has a trained model. Please generate and train a new version in order to upload model to Roboflow."
If upload is attempted for a model architecture that is not yet supported for weights upload, then you will receive a ValueError.
Next Steps
1.Test out your model in the Deploy Tab.
2.Deploy Your Model with Roboflow Deploy.
3.Improve Your Model with Active Learning | What is Active Learning?​
While other architectures are not yet available for weights upload, if you have interest in training and deploying a different custom architecture on your own, check out the Roboflow Notebooks Github Repository for full written and video guides.
GitHub - roboflow/notebooks: Set of Jupyter Notebooks linked to Roboflow Blogpost and used in our YouTube videos.
GitHub
Roboflow Notebooks GitHub Repository