The package provides templates for fine-tuning and inference with the D-FINE model, enabling advanced object detection tasks. It includes:

• DFINETraining: A highly flexible template for fine-tuning D-FINE models on custom data.
• DFINEInference: A streamlined and efficient template for running trained D-FINE models.

For specific instructions and further details, see the README.md.

The package provides templates for training, inference, and export with the RF-DETR model, enabling advanced object detection tasks. It includes:

• RFDETRExport and RFDETRLargeExport: Templates for exporting the RFDETRBase and RFDETRLarge models to ONNX format.
• RFDETRInference and RFDETRLargeInference: Templates designed to perform inference on a set of images using the RFDETRBase and RFDETRLarge models.
• RFDETRTrain and RFDETRLargeTrain: Templates for training the RFDETRBase and RFDETRLarge models.

For specific instructions and further details, see the README.md.

The package provides templates for training, inference, validation, and exporting models with Ultralytics. It includes:

• UltralyticsTrain: Template for training Ultralytics models.
• UltralyticsVal: Template for validating Ultralytics models.
• UltralyticsPredict: Template for generating inference predictions with trained models.
• UltralyticsExport: Template for exporting models to deployment-ready format.

For specific instructions and further details, see the README.md.

IMPORTANT This docker image depends on the sinapsis-nvidia:base image. Please refer to the official sinapsis instructions to Build with Docker.

1. Build the sinapsis-object-detection image:

docker compose -f docker/compose.yaml build

2. Start the app container:

• For D-FINE:

docker compose -f docker/compose_apps.yaml up sinapsis-dfine-gradio -d

• For RF-DETR:

docker compose -f docker/compose_apps.yaml up sinapsis-rfdetr-gradio -d

• For Ultralytics Inference:

docker compose -f docker/compose_apps.yaml up sinapsis-ultralytics-inference -d

• For Ultralytics Training:

docker compose -f docker/compose_apps.yaml up sinapsis-ultralytics-train -d

3. Check the logs:

• For D-FINE:

docker logs -f sinapsis-dfine-gradio

• For RF-DETR:

docker logs -f sinapsis-rfdetr-gradio

• For Ultralytics Inference:

docker logs -f sinapsis-ultralytics-inference

• For Ultralytics Training:

docker logs -f sinapsis-ultralytics-train

4. The logs will display the URL to access the webapp, e.g.:

Running on local URL:  http://127.0.0.1:7860

NOTE: The url can be different, check the output of logs.

5. To stop the app:

docker compose -f docker/compose_apps.yaml down

To run the webapp using the uv package manager, follow these steps:

1. Create the virtual environment and sync the dependencies:

uv sync --frozen

2. Install the sinapsis-object-detection package:

uv pip install sinapsis-object-detection[all] --extra-index-url https://pypi.sinapsis.tech

3. Run the webapp:

• For D-FINE:

uv run webapps/detection_demo.py

• For RF-DETR:

export AGENT_CONFIG_PATH=packages/sinapsis_rfdetr/src/sinapsis_rfdetr/configs/rfdetr_demo.yml
uv run webapps/detection_demo.py

• For Ultralytics Inference:

uv run webapps/inference_app.py

• For Ultralytics Training:

uv run webapps/training_app.py

4. The terminal will display the URL to access the webapp, e.g.:

Running on local URL:  http://127.0.0.1:7860

NOTE: The URL may vary; check the terminal output for the correct address.