D

deep-learning

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

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/

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

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

Intelligent VRAM/RAM swapping for LLM inference - Extension of KVortex | Offloading intelligent VRAM/RAM pour l'inference

https://ayinedjimi-consultants.fr

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

对不同尺寸的交通信号灯图片做红黄绿分类

Pinned PyTorch CPU and CUDA base images. Build the damn stack once.

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/

UMSI-Wikipedia Text Difficulty Prediction (NLP Project)

In SupeRAuGAN we implement a novel data augmentation technique tailored to Generative Adversarial Networks in order to reduce discriminator overfitting and stabilize training

Learn from books in statistics, econometrics, ML and DL. Follows https://trics.me/textbook/index.html, but takes a current selection of books and goes through TOC and code examples.

Delievered at https://epogrebnyak.gitlab.io/statbooks/

Photo-realistic single-image super-resolution (SRGAN, x4) in TensorFlow/TensorLayer — refactored OO training/evaluation pipeline with research experiment branches.

Capstone 1: Caterpillar Tube Pricing Prediction & Categorization. Capstone 2:Pipeline Multi-Leak Classification.

Agent skills for Ultralytics YOLO, covering models, datasets, training, tuning, inference, tracking, and export.

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

https://www.ultralytics.com

Sign Language Recognition using Computer Vision and Deep Learning

Deep learning system for seismic velocity inversion using a SincNet-GAT-UNet architecture, physics-informed temporal encoding, graph attention fusion, and FiLM-conditioned spatial prediction