https://github.com/ultralytics/yolov3
YOLOv3 in PyTorch > ONNX > CoreML > TFLite
https://github.com/ultralytics/yolov3
deep-learning machine-learning object-detection ultralytics yolo yolov3 yolov5
Last synced: about 1 month ago
JSON representation
YOLOv3 in PyTorch > ONNX > CoreML > TFLite
- Host: GitHub
- URL: https://github.com/ultralytics/yolov3
- Owner: ultralytics
- License: agpl-3.0
- Created: 2018-08-26T08:57:20.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2025-05-11T00:17:59.000Z (about 1 month ago)
- Last Synced: 2025-05-12T13:13:04.416Z (about 1 month ago)
- Topics: deep-learning, machine-learning, object-detection, ultralytics, yolo, yolov3, yolov5
- Language: Python
- Homepage: https://docs.ultralytics.com
- Size: 10.5 MB
- Stars: 10,395
- Watchers: 154
- Forks: 3,458
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- awesome-yolo - **Yolo v3**
- awesome-datascience - Yolov3
- StarryDivineSky - ultralytics/yolov3
- awesome-yolo-object-detection - ultralytics/yolov3
- awesome-yolo-object-detection - ultralytics/yolov3
README
[中文](https://docs.ultralytics.com/zh) | [한국어](https://docs.ultralytics.com/ko) | [日本語](https://docs.ultralytics.com/ja) | [Русский](https://docs.ultralytics.com/ru) | [Deutsch](https://docs.ultralytics.com/de) | [Français](https://docs.ultralytics.com/fr) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt) | [Türkçe](https://docs.ultralytics.com/tr) | [Tiếng Việt](https://docs.ultralytics.com/vi) | [العربية](https://docs.ultralytics.com/ar)
Ultralytics YOLOv3 is a robust and efficient [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) model developed by [Ultralytics](https://www.ultralytics.com/). Built on the [PyTorch](https://pytorch.org/) framework, this implementation extends the original YOLOv3 architecture, renowned for its improvements in [object detection](https://www.ultralytics.com/glossary/object-detection) speed and accuracy over earlier versions. It incorporates best practices and insights from extensive research, making it a reliable choice for a wide range of vision AI applications.
Explore the [Ultralytics Docs](https://docs.ultralytics.com/) for in-depth guidance (YOLOv3-specific docs may be limited, but general YOLO principles apply), open an issue on [GitHub](https://github.com/ultralytics/yolov5/issues/new/choose) for support, and join our [Discord community](https://discord.com/invite/ultralytics) for questions and discussions!
For Enterprise License requests, please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license).
## 🚀 YOLO11: The Next Evolution
We are thrilled to introduce **Ultralytics YOLO11** 🚀, the latest advancement in our state-of-the-art vision models! Available now at the [Ultralytics YOLO GitHub repository](https://github.com/ultralytics/ultralytics), YOLO11 continues our legacy of speed, precision, and user-friendly design. Whether you're working on [object detection](https://docs.ultralytics.com/tasks/detect/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [pose estimation](https://docs.ultralytics.com/tasks/pose/), [image classification](https://docs.ultralytics.com/tasks/classify/), or [oriented object detection (OBB)](https://docs.ultralytics.com/tasks/obb/), YOLO11 delivers the performance and flexibility needed for modern computer vision tasks.
Get started today and unlock the full potential of YOLO11! Visit the [Ultralytics Docs](https://docs.ultralytics.com/) for comprehensive guides and resources:
[](https://badge.fury.io/py/ultralytics) [](https://pepy.tech/projects/ultralytics)
```bash
# Install the ultralytics package
pip install ultralytics
```
## 📚 Documentation
See the [Ultralytics Docs for YOLOv3](https://docs.ultralytics.com/models/yolov3/) for full documentation on training, testing, and deployment using the Ultralytics framework. While YOLOv3-specific documentation may be limited, the general YOLO principles apply. Below are quickstart examples adapted for YOLOv3 concepts.
Install
Clone the repository and install dependencies from `requirements.txt` in a [**Python>=3.8.0**](https://www.python.org/) environment. Ensure you have [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) installed. (Note: This repo is originally YOLOv5, dependencies should be compatible but tailored testing for YOLOv3 is recommended).
```bash
# Clone the YOLOv3 repository
git clone https://github.com/ultralytics/yolov3
# Navigate to the cloned directory
cd yolov3
# Install required packages
pip install -r requirements.txt
```
Inference with PyTorch Hub
Use YOLOv3 via [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/) for inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) like `yolov3.pt`, `yolov3-spp.pt`, `yolov3-tiny.pt` can be loaded.
```python
import torch
# Load a YOLOv3 model (e.g., yolov3, yolov3-spp)
model = torch.hub.load("ultralytics/yolov3", "yolov3", pretrained=True) # specify 'yolov3' or other variants
# Define the input image source (URL, local file, PIL image, OpenCV frame, numpy array, or list)
img = "https://ultralytics.com/images/zidane.jpg" # Example image
# Perform inference
results = model(img)
# Process the results (options: .print(), .show(), .save(), .crop(), .pandas())
results.print() # Print results to console
results.show() # Display results in a window
results.save() # Save results to runs/detect/exp
```
Inference with detect.py
The `detect.py` script runs inference on various sources. Use `--weights yolov3.pt` or other YOLOv3 variants. It automatically downloads models and saves results to `runs/detect`.
```bash
# Run inference using a webcam with yolov3-tiny
python detect.py --weights yolov3-tiny.pt --source 0
# Run inference on a local image file with yolov3
python detect.py --weights yolov3.pt --source img.jpg
# Run inference on a local video file with yolov3-spp
python detect.py --weights yolov3-spp.pt --source vid.mp4
# Run inference on a screen capture
python detect.py --weights yolov3.pt --source screen
# Run inference on a directory of images
python detect.py --weights yolov3.pt --source path/to/images/
# Run inference on a text file listing image paths
python detect.py --weights yolov3.pt --source list.txt
# Run inference on a text file listing stream URLs
python detect.py --weights yolov3.pt --source list.streams
# Run inference using a glob pattern for images
python detect.py --weights yolov3.pt --source 'path/to/*.jpg'
# Run inference on a YouTube video URL
python detect.py --weights yolov3.pt --source 'https://youtu.be/LNwODJXcvt4'
# Run inference on an RTSP, RTMP, or HTTP stream
python detect.py --weights yolov3.pt --source 'rtsp://example.com/media.mp4'
```
Training
The commands below show how to train YOLOv3 models on the [COCO dataset](https://docs.ultralytics.com/datasets/detect/coco/). Models and datasets are downloaded automatically. Use the largest `--batch-size` your hardware allows.
```bash
# Train YOLOv3-tiny on COCO for 300 epochs (example settings)
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-tiny.yaml --batch-size 64
# Train YOLOv3 on COCO for 300 epochs (example settings)
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3.yaml --batch-size 32
# Train YOLOv3-SPP on COCO for 300 epochs (example settings)
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-spp.yaml --batch-size 16
```
Tutorials
Note: These tutorials primarily use YOLOv5 examples but the principles often apply to YOLOv3 within the Ultralytics framework.
- **[Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/)** 🚀 **RECOMMENDED**: Learn how to train models on your own datasets.
- **[Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/)** ☘️: Improve your model's performance with expert tips.
- **[Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training/)**: Speed up training using multiple GPUs.
- **[PyTorch Hub Integration](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading/)** 🌟 **NEW**: Easily load models using PyTorch Hub.
- **[Model Export (TFLite, ONNX, CoreML, TensorRT)](https://docs.ultralytics.com/yolov5/tutorials/model_export/)** 🚀: Convert your models to various deployment formats.
- **[NVIDIA Jetson Deployment](https://docs.ultralytics.com/guides/nvidia-jetson/)** 🌟 **NEW**: Deploy models on NVIDIA Jetson devices.
- **[Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/)**: Enhance prediction accuracy with TTA.
- **[Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling/)**: Combine multiple models for better performance.
- **[Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity/)**: Optimize models for size and speed.
- **[Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)**: Automatically find the best training hyperparameters.
- **[Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers/)**: Adapt pretrained models to new tasks efficiently.
- **[Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description/)** 🌟 **NEW**: Understand the model architecture (focus on YOLOv3 principles).
- **[Ultralytics HUB Training](https://www.ultralytics.com/hub)** 🚀 **RECOMMENDED**: Train and deploy YOLO models using Ultralytics HUB.
- **[ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration/)**: Integrate with ClearML for experiment tracking.
- **[Neural Magic DeepSparse Integration](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization/)**: Accelerate inference with DeepSparse.
- **[Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration/)** 🌟 **NEW**: Log experiments using Comet ML.
## 🧩 Integrations
Ultralytics offers robust integrations with leading AI platforms to enhance your workflow, including dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/), [Comet ML](https://docs.ultralytics.com/integrations/comet/), [Roboflow](https://docs.ultralytics.com/integrations/roboflow/), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI projects. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations/).
| Ultralytics HUB 🌟 | Weights & Biases | Comet | Neural Magic |
| :-----------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |
| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://hub.ultralytics.com/). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/). | Free forever, [Comet ML](https://docs.ultralytics.com/integrations/comet/) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/). |
## ⭐ Ultralytics HUB
Experience seamless AI development with [Ultralytics HUB](https://www.ultralytics.com/hub) ⭐, the ultimate platform for building, training, and deploying computer vision models. Visualize datasets, train YOLOv3, YOLOv5, and YOLOv8 🚀 models, and deploy them to real-world applications without writing any code. Transform images into actionable insights using our advanced tools and user-friendly [Ultralytics App](https://www.ultralytics.com/app-install). Start your journey for **Free** today!
## 🤔 Why YOLOv3?
YOLOv3 marked a major leap forward in real-time object detection at its release. Key advantages include:
- **Improved Accuracy:** Enhanced detection of small objects compared to YOLOv2.
- **Multi-Scale Predictions:** Detects objects at three different scales, boosting performance across varied object sizes.
- **Class Prediction:** Uses logistic classifiers for object classes, enabling multi-label classification.
- **Feature Extractor:** Employs a deeper network (Darknet-53) versus the Darknet-19 used in YOLOv2.
While newer models like YOLOv5 and YOLO11 offer further advancements, YOLOv3 remains a reliable and widely adopted baseline, efficiently implemented in PyTorch by Ultralytics.
## ☁️ Environments
Get started quickly with our pre-configured environments. Click the icons below for setup details.
## 🤝 Contribute
We welcome your contributions! Making YOLO models accessible and effective is a community effort. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. Share your feedback through the [Ultralytics Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). Thank you to all our contributors for making Ultralytics YOLO better!
[](https://github.com/ultralytics/yolov5/graphs/contributors)
## 📜 License
Ultralytics provides two licensing options to meet different needs:
- **AGPL-3.0 License**: An [OSI-approved](https://opensource.org/license/agpl-v3) open-source license ideal for academic research, personal projects, and testing. It promotes open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details.
- **Enterprise License**: Tailored for commercial applications, this license allows seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. For commercial use cases, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
## 📧 Contact
For bug reports and feature requests related to Ultralytics YOLO implementations, please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For general questions, discussions, and community support, join our [Discord server](https://discord.com/invite/ultralytics)!