RF-DETR 1.3.0

What's new 🔥

Support for instance segmentation

RF-DETR 1.3.0 adds RF-DETR Seg (Preview), a new, state-of-the-art instance segmentation model.

RF-DETR Seg (Preview) is 3x faster and more accurate than the largest YOLO11 when evaluated on the Microsoft COCO Segmentation benchmark, defining a new real-time state-of-the-art for the industry-standard benchmark in segmentation model evaluation.

With the rfdetr Python package, you can train and run models with the new RFDETRSegPreview trainer.

The training API is as follows:

from rfdetr import RFDETRSegPreview

model = RFDETRSegPreview()

model.train(
    dataset_dir=<DATASET_PATH>,
    epochs=10,
    batch_size=4,
    grad_accum_steps=4,
    lr=1e-4,
    output_dir=<OUTPUT_PATH>
)

Trained models can also be deployed with Roboflow Inference with the new deploy_to_roboflow function. This allows you to provision a serverless cloud API for running your model, as well as deploy your model in a Roboflow Workflow or with a Roboflow Inference server:

from rfdetr import RFDETRSegPreview

x = RFDETRSegPreview(pretrain_weights="<path/to/prtrain/weights/dir>")
x.deploy_to_roboflow(
  workspace="<your-workspace>",
  project_ids=["<your-project-id>"],
  api_key="<YOUR_API_KEY>"
)

🏆 Contributors

@probicheaux @isaacrob-roboflow @Matvezy @SkalskiP @capjamesg