Readme
RF-DETR-COG
A serverless deployment of Roboflow’s state-of-the-art object detection model (RF-DETR) on Replicate.
Overview
This repository contains the code necessary to deploy the RF-DETR (Roboflow Detection Transformer) model as a serverless endpoint on Replicate. RF-DETR represents one of the most advanced object detection architectures available, combining the strengths of transformer-based models with efficient detection capabilities. Get more information about the model here.
How to Use Custom Models
Customizing the deployment with your own RF-DETR model requires only two simple steps:
- Add your model weights file to the root of this repository
- Update the model initialization in the code:
# Change this line in predict.py
self.model = RFDETRBase(pretrain_weights="your-custom-model.pth")- Follow the Replicate deployment guide to publish your model
How to use with API:
Get more information about various available api from here.
Local Development and Testing
To test the model locally before deployment:
# Install cog if you haven't already
pip install cog
# Run a prediction with a local image
cog predict -i image=@/path/to/your/image.jpgRequirements
- Python 3.8+
- PyTorch 1.10+
- Cog
- rfdetr
Citation
If you use RF-DETR in your research or applications, please cite the original paper:
@software{rf-detr,
author = {Robinson, Isaac and Robicheaux, Peter and Popov, Matvei},
license = {Apache-2.0},
title = {RF-DETR},
howpublished = {\url{https://github.com/roboflow/rf-detr}},
year = {2025},
note = {SOTA Real-Time Object Detection Model}
}License
This project is licensed under the Apache-2.0 License - see the LICENSE file for details.