ultralytics/yolov3

Features

• YOLOv3 Frameworks - Implements the YOLOv3 architecture for real-time object detection in images and video.
• YOLOv3 Implementations - Provides a complete real-time object detection framework built on the YOLOv3 architecture.
• Anchor Box Systems - Predicts object locations relative to predefined anchor box shapes at each grid cell.
• Non-Maximum Suppression - Removes duplicate detections by keeping only the highest-confidence bounding box among overlapping predictions.
• Object Detection - Runs a pretrained neural network on an image to locate and classify objects within it.
• Real-Time Object Detection - Provides real-time object detection on images and video streams using a single neural network pass.
• Detection Model Training - Fine-tunes a pretrained detection model on a user-provided labeled dataset to recognize new object categories.
• Multi-Scale Feature Pyramids - Extracts object features at three spatial scales to detect both small and large objects.
• Darknet-53 Backbones - Provides the Darknet-53 backbone for hierarchical feature extraction in real-time detection.
• Object Detection - Runs inference on images and videos to locate and classify objects using trained detection models.
• PyTorch Training Frameworks - Provides a PyTorch training pipeline for fine-tuning YOLOv3 models on custom datasets.
• Performance Validators - Provides built-in validation of precision, recall, and mean average precision for trained detection models.
• Single Shot Detectors - Runs a single neural network pass per image to simultaneously predict bounding boxes and class probabilities.
• Human - Ships a human pose estimation model that detects body keypoints and reconstructs poses in images and video.
• Instance Segmentation Engines - Assigns a pixel-level mask to each detected object, separating it from the background and other objects.
• Video Stream Detections - Processes video frames sequentially to identify and track objects across a video stream.
• Detection Model Validation - Evaluates precision, recall, and mean average precision against a validation dataset.
• Experiment Tracking Integrations - Integrates with Weights & Biases and Roboflow for automated experiment tracking and dataset management.
• Model Architecture Selection - Selects between pretrained and scratch model variants to balance accuracy, speed, and memory.
• Hyperparameter Tuning - Adjusts epochs, resolution, batch size, and augmentation to improve model convergence and accuracy.
• Weights and Biases Training Logging - Streams training metrics to Weights & Biases for real-time visualization and experiment comparison.
• Object Tracking - Assigns a persistent ID to each detected object and follows its movement through a video sequence.
• Pose Estimation - Detects key body joints and limbs for each person in an image or video to reconstruct their pose.
• PyTorch-to-ONNX Pipelines - Converts trained models through ONNX to CoreML, TensorFlow Lite, and TensorRT formats.
• Image Augmentations - Applies random transformations like scaling, cropping, and color jittering to training images.
• Multi-Object Trackers - Assigns persistent IDs to detected objects and tracks their movement across video frames.
• Video Object Tracking - Assigns persistent IDs to detected objects and follows their movement across video frames.
• Model Export Formats - Exports trained models to ONNX, CoreML, TFLite, and TensorRT for production deployment.
• Multi-Format Exporters - Converts trained models to ONNX, CoreML, TFLite, and TensorRT for deployment across platforms.
• Computer Vision - Classic real-time object detection model.
• Computer Vision Libraries - YOLOv3 implementation for PyTorch and mobile export.
• Object Detection - Listed in the “Object Detection” section of the The Incredible Pytorch awesome list.

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