YOLOv3
YOLOv3 is an object detection algorithm developed by Joseph Redmon that performs real-time detection by predicting bounding boxes and class probabilities directly from full images in a single evaluation pass. It introduces multiscale predictions using three different detection kernel sizes, achieving 28.2 mAP on the COCO dataset while running at 22 milliseconds per frame on 320×320 input. This performance matches the accuracy of SSD but operates approximately three times faster. Ultralytics provides a PyTorch implementation of YOLOv3 that supports forward compatibility with YOLOv5 models and methods, including exporting models to ONNX, CoreML, and TFLite formats. The model can be trained on datasets such as COCO using Python or command-line interfaces.
YOLOv3 is a real-time object detection model that balances accuracy and speed using multiscale predictions and a single neural network evaluation.
Real-Time Object Detection
Detect objects in video streams or images with low latency for applications such as surveillance or robotics.
Edge Device Deployment
Export models to lightweight formats like TFLite or CoreML for deployment on mobile or embedded devices.
Custom Dataset Training
Train the model on specific datasets to tailor detection capabilities to specialized domains.
git clone https://github.com/ultralytics/yolov3 && cd yolov3 to get the codebase.pip install -r requirements.txt ensuring Python >=3.8 and PyTorch >=1.8 are installed.from ultralytics import YOLO; model = YOLO("yolov3u.pt") to load the pretrained model for inference.model.train(data="coco8.yaml", epochs=100, imgsz=640).