D

deep-learning

Real-time theft detection system using YOLOv8/v11 pose estimation on CCTV feeds

Sign Language Recognition using Computer Vision and Deep Learning

Manga Colorization Studio — ONNX manga colorization engine (v3): smart style routing across 7 official colored manga domains (One Piece, Naruto, HxH, Golden Kamuy, Bleach, JoJo, Dragon Ball), semantic skin/sky/shadow memories, hybrid hint coloring, full-chapter batch pipeline + Next.js live studio. Model released as ONNX.

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

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

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

https://www.ultralytics.com

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

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 organization-wide GitHub community health files, issue and pull request templates, and the public org profile.

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

https://ultralytics.com

Ultralytics iSky iOS app for real-time neural style transfer, transforming live iPhone and iPad camera video with five famous painting styles.

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

https://apps.apple.com/us/app/isky/id1445737240

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

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

https://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

Ce projet porte sur le développement d'un modèle de scoring bancaire destiné à prédire le risque associé à une demande de crédit à partir des données historiques de prêts du dataset Lending Club.

L'objectif est de construire une chaîne complète de Data Science, allant de l'analyse et de la préparation des données jusqu'à la modélisation prédictive et l'évaluation des performances.

Travaux réalisés :

Analyse exploratoire du dataset et étude des différentes variables disponibles. Nettoyage et préparation des données. Traitement des valeurs manquantes et des variables catégorielles. Sélection et transformation des variables pertinentes. Réalisation de Feature Engineering afin de construire des variables adaptées à la prédiction. Préparation des jeux de données pour l'entraînement et l'évaluation. Expérimentation de différents modèles de Machine Learning. Expérimentation d'un modèle de Deep Learning avec TensorFlow Évaluation des modèles à l'aide de métriques de classification. Analyse comparative des performances afin d'identifier l'approche la plus pertinente

The AI and the Automobile collection brings together multiple specifications that define how AI integrates into modern vehicle systems, treating the automobile as a software-defined platform where intelligence spans perception, control, navigation, and energy management. It emphasizes real-time performance, safety-critical architecture, and the transition from purely mechanical systems to continuously evolving software-driven mobility. Across the collection, the specs also describe how automotive AI should be built through open and interoperable systems with strong safety practices, including simulation, validation pipelines, and human-in-the-loop oversight to handle edge cases. Together, they frame vehicle intelligence as a layered system combining autonomy, redundancy, diagnostics, and secure update mechanisms to enable reliable and scalable deployment. https://roxanneardary.com/ai-and-the-automobile/

IF-Net fork + complete preprocessed ShapeNet archive (tar.xz bundles via git LFS). ShapeNet derivatives: non-commercial research use only.

PyTorch reimplementation of CheXNet: multi-label classification and CAM/Grad-CAM localization of 14 thoracic diseases on ChestX-ray14, with full-resolution (1024px) ResNet50 training.

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