I

inference

PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.

GitHub: https://github.com/ultralytics/yolov3

https://docs.ultralytics.com/models/yolov3/

A self-hosted AI platform — inference, tool use, browser automation, image generation, speech synthesis, transcription, object storage, agentic code execution, and more — behind a single OpenAI-compatible endpoint. One docker-compose up.

Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.

GitHub: https://github.com/ultralytics/yolov5

https://docs.ultralytics.com/yolov5/

Docker container, inference pipeline, and submission tooling for deploying trained YOLOv3 object detection models in the xView satellite imagery challenge.

GitHub: https://github.com/ultralytics/xview-docker

https://challenge.xviewdataset.org/

Mock embedded inference-accelerator runtime a small C++/Python SDK and Linux daemon used as a test fixture for build automation and CI tooling.