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Published directory entries matching your search.
Awesome Detection Transformer is a GitHub-hosted public collection of research papers focused on transformer-based approaches for computer vision detection and segmentation. The repository organizes papers by research field, and invites community contributions of overlooked papers or resources via issues or pull requests. It also references related toolboxes for transformer-based object detection work.
DeepDetect is a deep learning runtime, command-line tool and REST server for training and inference. It provides Python wheel variants for CPU and GPU environments and a command-line interface with profiles such as object detection and semantic segmentation. YAML configuration files make runs repeatable. It handles images, text, tabular data, time series, object-detection boxes and segmentation masks through a single API surface.
NExT-Chat is a large multimodal model trained using the pixel2emb method, in which the model outputs location embeddings decoded by different decoders. This allows bounding boxes and masks in multimodal conversations and supports tasks such as visual grounding, region caption and grounded reasoning.
Detectron2 is Facebook AI Research's next generation library providing detection and segmentation algorithms, succeeding Detectron and maskrcnn-benchmark. It supports computer vision research projects and production applications at Facebook and includes capabilities such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, ViTDet and MViTv2. Models can be exported to TorchScript or Caffe2 format for deployment.
Repository of Ultralytics YOLO models, including YOLO27, YOLO26, YOLO11, and YOLOv8, covering object detection, instance segmentation, semantic segmentation, image classification, pose estimation, and object tracking. Models can be installed as the ultralytics package, used through a CLI yolo command or Python, and exported to formats such as ONNX.
PyTorchCV is a PyTorch-based framework for deep learning in computer vision. The repository provides source code for computer vision problems, with implemented papers covering image classification, semantic segmentation, object detection, pose estimation and instance segmentation.
Feast is an end-to-end open source feature store for machine learning. It allows teams to define, manage, discover, and serve features, delivering structured data to AI and LLM applications during training and inference. Documented use cases include real-time recommendations, fraud detection, risk scoring, and customer segmentation.
Open-source Python library for data-centric AI that helps clean data and labels by automatically detecting issues in machine learning datasets. It uses existing models to estimate dataset problems that can be fixed to train better models. It runs on Python 3.10+ and works with any model across classification, regression, segmentation and object detection tasks.
Apache OpenNLP is a machine learning based toolkit for the processing of natural language text. It supports tasks such as sentence segmentation, tokenization, lemmatization, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution. The project is developed by volunteers and welcomes contributions, from small documentation fixes to new components.
