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This project is a TensorFlow and Keras implementation of the Mask R-CNN architecture. It provides a framework for performing simultaneous object detection and instance segmentation, transforming raw images into segmented masks and bounding boxes for individual object identification. The toolset enables custom computer vision training through fine-tuning pre-trained weights and integrating user-provided datasets. It includes capabilities for distributed GPU training to accelerate the optimization of large vision models. The framework covers model evaluation using standard precision metrics an
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
PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti
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
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 main features of balancap/ssd-tensorflow are: Object Detection Models, Object Detection, Real-Time Object Detection, Detection Model Training, Neural Network Training Toolkits, TensorFlow Model Development, Dataset Preprocessing Tools, Model Fine-Tuning.
Projects with overlapping indexed features include: matterport/mask_rcnn — This project is a TensorFlow and Keras implementation of the Mask R-CNN architecture. It provides a framework for… wongkinyiu/yolov9 — YOLOv9 is a real-time computer vision framework and deep learning model designed for image classification, object… paddlepaddle/paddledetection — PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of… qqwweee/keras-yolo3 — This project is an object detection framework implementing the YOLOv3 architecture using Keras and TensorFlow. It… roboflow/rf-detr — RF-DETR is a Python library for training and deploying object detection, instance segmentation, and keypoint detection… eriklindernoren/pytorch-yolov3 — This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time…