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Welcome to the Ultralytics YOLOv3 🚀 wiki! Here you'll find useful tutorials, environments, and the current repo status. Please visit https://docs.ultralytics.com also for full YOLOv3 documentation.

Tutorials

• Train Custom Data 🚀 RECOMMENDED
• Tips for Best Training Results ☘️RECOMMENDED
• Multi-GPU Training
• PyTorch Hub 🌟 NEW
• TFLite, ONNX, CoreML, TensorRT Export 🚀
• NVIDIA Jetson platform Deployment 🌟 NEW
• Test-Time Augmentation (TTA)
• Model Ensembling
• Model Pruning/Sparsity
• Hyperparameter Evolution
• Transfer Learning with Frozen Layers
• Architecture Summary 🌟 NEW
• Roboflow for Datasets, Labeling, and Active Learning
• ClearML Logging 🌟 NEW
• YOLOv5 with Neural Magic's Deepsparse 🌟 NEW
• Comet Logging 🌟 NEW

Environments

YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv3 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv3 training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.