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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 main features of eriklindernoren/pytorch-yolov3 are: Real-Time Object Detection, Object Detection, Detection Model Training, PyTorch Tensor Operations, Darknet-53 Backbones, Object Detection Models, PyTorch Implementations, YOLOv3 Implementations.
Projects with overlapping indexed features include: ultralytics/yolov3 — This is a real-time object detection framework built on the YOLOv3 architecture, implemented in PyTorch. It provides a… amdegroot/ssd.pytorch — This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and… zylo117/yet-another-efficientdet-pytorch — This project is a PyTorch implementation of the EfficientDet architecture designed for real-time object detection. It… qqwweee/keras-yolo3 — This project is an object detection framework implementing the YOLOv3 architecture using Keras and TensorFlow. It… alexeyab/darknet — Darknet is a high-performance C-based inference engine and computer vision library designed for real-time object… jwyang/faster-rcnn.pytorch — This project is a PyTorch object detection framework that implements the Faster R-CNN architecture. It serves as a…
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
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 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
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