Projects sharing features with Nanodet

YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/

RF-DETR is a Python library for training and deploying object detection, instance segmentation, and keypoint detection models built on a vision transformer architecture. It provides a unified command-line interface and Python API for the full workflow, from fine-tuning pretrained checkpoints on custom datasets to running inference on images, video files, and live camera streams. The project supports training on datasets in COCO or YOLO format, with automatic format detection and configurable augmentation pipelines. Models can be exported to ONNX, TFLite, or TensorRT for deployment across edge

This is a real-time object detection framework built on the YOLOv3 architecture, implemented in PyTorch. It provides a complete pipeline for identifying and localizing objects in images and video using a single neural network pass, combining a Darknet-53 backbone with multi-scale feature pyramids and anchor-based bounding box prediction. The framework extends beyond basic detection to include instance segmentation, human pose estimation, and multi-object tracking across video frames. It offers a model export toolkit that converts trained models through ONNX to CoreML, TensorFlow Lite, and Ten

YOLOv9 is a real-time computer vision framework and deep learning model designed for image classification, object detection, and instance segmentation. It functions as both a vision model and a trainer, allowing for the optimization of neural network weights on custom datasets using single or multiple GPUs. The framework utilizes programmable gradient information to perform high-speed identification and location of multiple objects within images and video streams. It extends beyond bounding box detection to provide instance segmentation and panoptic segmentation, which labels every pixel in a

This project provides a suite of lightweight face detection models designed for high-speed inference on edge computing devices. It centers on a compact neural network architecture that enables human face detection within environments characterized by limited compute resources and power constraints. The system features quantized face detectors available in multiple formats to ensure compatibility across diverse hardware architectures. It includes utilities for model export and quantization, allowing trained weights to be converted into standardized formats for hardware-agnostic deployment. Th

YOLOv7 is a PyTorch vision library and real-time inference engine designed for object detection, human pose estimation, and instance segmentation. It provides a framework for detecting and locating multiple objects within images or video streams using neural networks. The system includes tools for custom model training and fine-tuning, allowing pre-trained weights to be adapted to specialized datasets via transfer learning. It also supports model weight export and format conversion to facilitate deployment on production servers and embedded edge devices.

YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images and video based on free-form text prompts without requiring predefined category labels. The system enables the identification of arbitrary objects by fusing image features with text embeddings. It includes a specialized tool for automated image labeling, which generates bounding box annotations for custom datasets using text-based prompts. The project provides a deployment pipeline for converting models into quantized ONNX and TFLite formats, supporting real-time inference on

YOLOv6 is a single-stage deep learning framework designed for industrial object detection. It serves as a computer vision model trainer for identifying and locating objects within images, as well as an instance segmentation tool that delineates precise object boundaries using masks. The project includes a specialized mobile inference optimizer and a model quantization toolkit. These components focus on reducing model size and resolution to improve execution speed on ARM-based chipsets and converting models to low-precision formats to decrease file size. The framework covers a broad range of

This is a PyTorch implementation of EfficientNet convolutional neural networks. It serves as a computer vision model library providing architectures for image classification and high-level feature extraction, including pre-trained weights for immediate image categorization. The library supports transfer learning by allowing the modification of model architectures and output layers to accommodate a custom number of classes for new datasets. It also includes a model exporter to convert trained PyTorch weights into the ONNX format for production inference. The system covers broader computer vis

This project is a PyTorch implementation of the EfficientDet architecture designed for real-time object detection. It provides a neural network and inference engine capable of identifying and locating multiple objects within images or video streams. The implementation includes pretrained computer vision models with optimized weights, enabling immediate inference and fine-tuning without the need for training from scratch. The project covers the full pipeline for computer vision model optimization, including custom object detection training and model weight optimization. It incorporates struct

Darknet is a high-performance C-based inference engine and computer vision library designed for real-time object identification and localization. It serves as a neural network framework for training and deploying detection models using the YOLO architecture, providing a toolset for deep learning training and deployment. The project differentiates itself through a C and CUDA implementation that enables hardware acceleration for matrix multiplication and inference speed optimization. It provides a shared library interface for embedding detection capabilities into external applications and suppo

This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time object detector and computer vision framework designed to identify and locate multiple objects within images using bounding boxes and class labels. The system allows for both the use of pretrained weights for immediate image analysis and the training of custom models using datasets with bounding box annotations. It provides a programmatic interface to integrate detection capabilities directly into other software applications. The framework includes tools for model evaluation to m

RT-DETR is a real-time object detection model based on the detection transformer architecture. It is implemented as a computer vision model for both the PyTorch and PaddlePaddle deep learning platforms, designed to identify and locate multiple objects in images and video streams. The model eliminates the need for anchor generation and non-maximum suppression by utilizing a transformer-based approach. It focuses on high-performance detection, balancing precision and low latency for live environment deployment. The system employs a hybrid encoder and multi-scale feature fusion to extract globa

This project is a cross-platform machine learning inference engine designed to execute pre-trained models across diverse operating systems and hardware environments. It functions as a standardized execution framework that manages the entire lifecycle of model inference, from loading and graph optimization to hardware-accelerated execution and generative sequence management. The runtime distinguishes itself through a highly modular architecture that decouples model logic from hardware-specific kernels. By utilizing an execution provider abstraction, it enables developers to offload computation

GhostNet provides a set of efficient AI model architectures and neural network design patterns designed to reduce computation and memory overhead. It serves as a computer vision backbone and a lightweight vision transformer, optimizing the balance between predictive accuracy and inference speed. The project focuses on reducing resource consumption for deployment on mobile devices and edge hardware. It achieves this through the use of lightweight vision transformer implementations and architectures that minimize the total number of parameters. The codebase covers a range of capabilities for i

This project is a cross-platform mobile camera framework and real-time computer vision library. It provides a high-performance interface for mobile applications to handle hardware control, media capture, and live camera frame processing. The framework includes a dedicated system for running AI models and custom analysis on live camera streams using high-performance worklets. It also functions as a real-time detection and decoding system for QR codes and barcodes. Broad capabilities cover the capture of high-resolution photos and videos with controls for zoom, HDR, and frame rates. The projec

This project is an object detection framework implementing the YOLOv3 architecture using Keras and TensorFlow. It functions as a deep learning vision model and computer vision toolset designed to locate and classify multiple entities within images and video streams using bounding boxes. The system includes a multi-GPU inference engine to distribute computational loads across several graphics processing units. It also provides a pipeline for creating custom object detectors by retraining pre-trained weights on annotated datasets to recognize user-defined object classes. The framework covers m

A collection of reference implementations and code samples for integrating Android camera hardware and software APIs. The project provides demonstrations for using both the Jetpack CameraX library and the low-level Camera2 API to implement photo and video capture features. The repository includes specialized implementations for high-performance recording, such as high-frame-rate slow motion and high-dynamic-range video. It also features examples of machine learning vision, demonstrating how to analyze live camera frames for object detection and QR code scanning. The project covers broad imag

This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and localizing multiple objects within images and video. The project provides a neural network architecture designed for single-shot object detection, which predicts bounding boxes and class labels in one pass. The implementation includes a real-time object detector capable of processing live video streams to track and label objects across sequential frames. It also features a complete computer vision training pipeline for preparing image datasets and training model weights. The fra

This project is a TensorFlow object detection framework designed for training and deploying Single Shot MultiBox Detector models. It provides a neural network training toolkit for implementing the SSD architecture to achieve real-time image and video object localization. The framework includes a dedicated data pipeline for transforming object detection datasets into binary record formats to increase training speed and performance. It also features utilities for converting model weights between different checkpoint formats to facilitate the reuse of pre-trained networks. The system covers a b

This application is a real-time computer vision system designed to identify and label objects within live video feeds, recorded files, and static images. It functions as a comprehensive framework that integrates pre-trained machine learning models with video processing pipelines to perform multi-object localization and visual data tracking. The system distinguishes itself through a multithreaded architecture that decouples frame acquisition from detection logic, ensuring the interface remains responsive during continuous analysis. It provides specialized scripts for training and optimizing cu

lite.ai.toolkit is a C++ computer vision toolkit designed for edge AI deployment. It enables the execution of pre-trained models for object detection, image classification, and segmentation on resource-constrained devices. The project features a multi-backend inference engine that supports the ONNX model runtime, allowing AI models to run across different hardware targets. It includes a GPU-accelerated pipeline specifically for NVIDIA hardware to reduce latency and increase processing speed. The toolkit covers a broad range of facial analysis capabilities, including emotion detection, gender

This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a vision model for predicting precise bounding boxes around multiple objects within images and live video feeds. The system is optimized for multi-GPU training to reduce the time required for model convergence. It utilizes a GPU-accelerated design to handle the training and inference of complex detection networks. The framework covers the full object detection lifecycle, including custom network training and inference for static images and real-time video streams. It includes capa

Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in

YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time object detection, image classification, and instance segmentation. It provides a unified toolkit that manages the entire lifecycle of a model, from initial dataset configuration and hyperparameter tuning to high-speed inference and deployment. The framework utilizes a modular neural architecture, allowing users to swap backbone and head components to tailor models for specific visual tasks. What distinguishes this project is its focus on production-ready deployment and model ef

Anomalib is a PyTorch-based library for visual anomaly detection, offering a modular framework, a comprehensive model zoo, and a benchmarking suite designed for industrial defect detection. It provides a wide range of algorithms—including generative, discriminative, teacher-student, and vision-language approaches—that support unsupervised, few-shot, and zero-shot settings. The library enables deployment through model export to ONNX and OpenVINO for edge devices, and includes a no-code web application for training and inference. It also features a command-line interface for orchestrating multi