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| comments | description | keywords |
|---|---|---|
| true | Explore Ultralytics integrations with tools for dataset management, model optimization, ML workflows automation, experiment tracking, version control, and more. Learn about our support for various model export formats for deployment. | Ultralytics integrations, Roboflow, Neural Magic, ClearML, Comet ML, DVC, Ultralytics HUB, MLFlow, Neptune, Ray Tune, TensorBoard, W&B, model export formats, PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, TF GraphDef, TF Lite, TF Edge TPU, TF.js, PaddlePaddle, NCNN |
Ultralytics Integrations
Welcome to the Ultralytics Integrations page! This page provides an overview of our partnerships with various tools and platforms, designed to streamline your machine learning workflows, enhance dataset management, simplify model training, and facilitate efficient deployment.
Datasets Integrations
- Roboflow: Facilitate seamless dataset management for Ultralytics models, offering robust annotation, preprocessing, and augmentation capabilities.
Training Integrations
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Comet ML: Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.
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ClearML: Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration.
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DVC: Implement version control for your Ultralytics machine learning projects, synchronizing data, code, and models effectively.
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Ultralytics HUB: Access and contribute to a community of pre-trained Ultralytics models.
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MLFlow: Streamline the entire ML lifecycle of Ultralytics models, from experimentation and reproducibility to deployment.
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Neptune: Maintain a comprehensive log of your ML experiments with Ultralytics in this metadata store designed for MLOps.
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Ray Tune: Optimize the hyperparameters of your Ultralytics models at any scale.
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TensorBoard: Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration.
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Weights & Biases (W&B): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects.
Deployment Integrations
- Neural Magic: Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size.
Export Formats
We also support a variety of model export formats for deployment in different environments. Here are the available formats:
| Format | format Argument |
Model | Metadata | Arguments |
|---|---|---|---|---|
| PyTorch | - | yolov8n.pt |
✅ | - |
| TorchScript | torchscript |
yolov8n.torchscript |
✅ | imgsz, optimize |
| ONNX | onnx |
yolov8n.onnx |
✅ | imgsz, half, dynamic, simplify, opset |
| OpenVINO | openvino |
yolov8n_openvino_model/ |
✅ | imgsz, half |
| TensorRT | engine |
yolov8n.engine |
✅ | imgsz, half, dynamic, simplify, workspace |
| CoreML | coreml |
yolov8n.mlmodel |
✅ | imgsz, half, int8, nms |
| TF SavedModel | saved_model |
yolov8n_saved_model/ |
✅ | imgsz, keras |
| TF GraphDef | pb |
yolov8n.pb |
❌ | imgsz |
| TF Lite | tflite |
yolov8n.tflite |
✅ | imgsz, half, int8 |
| TF Edge TPU | edgetpu |
yolov8n_edgetpu.tflite |
✅ | imgsz |
| TF.js | tfjs |
yolov8n_web_model/ |
✅ | imgsz |
| PaddlePaddle | paddle |
yolov8n_paddle_model/ |
✅ | imgsz |
| NCNN | ncnn |
yolov8n_ncnn_model/ |
✅ | imgsz, half |
Explore the links to learn more about each integration and how to get the most out of them with Ultralytics.