PyTorch

Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking

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

https://platform.ultralytics.com

Self-hosted forecasting + prediction service. Five zero-shot time-series foundation models (Chronos-2, TimesFM 2.5, Moirai-2, Toto-1, Sundial) across six forecast types, plus nine supervised tabular ML backends (LightGBM, XGBoost, sklearn family) with calibrated / stacking / diversified meta-learners. Unified REST API + MCP server.

Detect and classify objects in Panoramax pictures

A torch.compile backend for Qualcomm QNN on Windows ARM64 Snapdragon devices, enabling PyTorch inference acceleration on supported NPU and GPU backends.

ML engine for discrete circuit design: simulates and labels circuits, trains a GNN surrogate, then uses it to design, self-tune, and run inference on a microcontroller.

Tiny neural amp/pedal capture: a small GRU learns a guitar amp's sound, small enough to run in real time on a sub-$5 microcontroller.

Ultralytics YOLO26 quickstart for detection, instance and semantic segmentation, depth estimation, classification, pose, OBB, and tracking.

GitHub: https://github.com/ultralytics/yolo26

https://platform.ultralytics.com/ultralytics/yolo26

Ultralytics YOLO11 discovery and quickstart for detection, segmentation, classification, pose, OBB, and tracking.

GitHub: https://github.com/ultralytics/yolo11

https://platform.ultralytics.com/ultralytics/yolo11

Ultralytics YOLOv8 discovery and quickstart for detection, segmentation, classification, pose, OBB, and tracking.

GitHub: https://github.com/ultralytics/yolov8

https://platform.ultralytics.com/ultralytics/yolov8

YOLOE data pipeline for grounding and detection labels, predictions, text refinement, cache generation, and visualization.

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

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

Ultralytics fork of Apple MobileCLIP for fast image-text inference, training, evaluation, and an iOS demo.

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

https://docs.ultralytics.com

PyTorch model profiler for computing MACs and parameter counts to measure deep learning model complexity.

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

https://pypi.org/project/ultralytics-thop/

OpenAI CLIP for zero-shot image-text classification and embeddings with ResNet and Vision Transformer models.

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

https://www.ultralytics.com

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

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

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

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/

PyTorch sandbox for testing convolutional networks, ResNets, and other architectures on MNIST digits.

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

https://ultralytics.com

YOLOv3 training, preprocessing, validation, and inference for object detection in xView satellite imagery and the xView detection challenge.

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

https://docs.ultralytics.com

WAVE deep learning for signal readout and reconstruction in full-waveform time-of-flight particle detectors.

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

https://ultralytics.com

Machine learning and Structure-from-Motion tools for estimating vehicle speed from imagery for traffic monitoring, road safety, and autonomous systems research.

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

https://www.ultralytics.com

PyTorch/NumPy code for neural networks and backpropagation