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[Ultralytics](https://www.ultralytics.com) YOLOv3 is a PyTorch implementation of the YOLOv3 (You Only Look Once, version 3) real-time [object detection](https://docs.ultralytics.com/tasks/detect) model. YOLOv3 frames detection as a single regression problem, predicting bounding boxes and class probabilities directly from full images in one forward pass — making it fast, accurate, and straightforward to train and deploy. This repository packages the three classic YOLOv3 detection models — **YOLOv3**, **YOLOv3-SPP**, and **YOLOv3-tiny** — with training, validation, inference, and export tooling, and reuses shared utilities from the [`ultralytics`](https://github.com/ultralytics/ultralytics) package. Find detailed guidance in the [Ultralytics YOLOv3 Docs](https://docs.ultralytics.com/models/yolov3). Get support via [GitHub Issues](https://github.com/ultralytics/yolov3/issues/new/choose), and join the conversation on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Forums](https://community.ultralytics.com). For commercial use, request an Enterprise License at [Ultralytics Licensing](https://www.ultralytics.com/license).

## 📚 Documentation See the [Ultralytics YOLOv3 Docs](https://docs.ultralytics.com/models/yolov3) for full documentation. The quickstart examples below cover installation, inference, and training with this repository.

Install

Clone the repository and install the dependencies from `requirements.txt` in a [**Python>=3.8.0**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). ```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

Load YOLOv3 directly through [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch-hub-model-loading). Weights download automatically on first use. ```python import torch # Load a YOLOv3 model (choices: 'yolov3', 'yolov3_spp', 'yolov3_tiny') model = torch.hub.load("ultralytics/yolov3", "yolov3", pretrained=True) # Run inference on an image (local file, URL, PIL image, OpenCV frame, or numpy array) results = model("https://ultralytics.com/images/zidane.jpg") # Inspect the results results.print() # print detections to the console results.show() # display the annotated image results.save() # save the annotated image to runs/detect/exp ```

Inference with detect.py

`detect.py` runs inference on a wide range of sources, downloading models automatically and saving results to `runs/detect`. ```bash python detect.py --weights yolov3.pt --source 0 # webcam python detect.py --weights yolov3.pt --source img.jpg # image python detect.py --weights yolov3.pt --source vid.mp4 # video python detect.py --weights yolov3.pt --source screen # screenshot python detect.py --weights yolov3.pt --source path/ # directory python detect.py --weights yolov3.pt --source 'path/*.jpg' # glob python detect.py --weights yolov3.pt --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ```

Training

Train YOLOv3 on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset. Models and datasets download automatically. Use the largest `--batch-size` your hardware allows. ```bash # Train YOLOv3-tiny python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-tiny.yaml --batch-size 64 # Train YOLOv3 python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3.yaml --batch-size 32 # Train YOLOv3-SPP python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov3-spp.yaml --batch-size 16 ``` Validate accuracy with `python val.py --weights yolov3.pt --data coco.yaml`, and export to other formats (TorchScript, ONNX, OpenVINO, TensorRT, CoreML, and PaddlePaddle) with `python export.py --weights yolov3.pt --include onnx`.

Tutorials

These guides cover the shared Ultralytics training framework and apply to YOLOv3: - [Train Custom Data](https://docs.ultralytics.com/modes/train) — train on your own dataset. - [Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips) — get the most out of training. - [Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi-gpu-training) — scale training across GPUs. - [PyTorch Hub Loading](https://docs.ultralytics.com/yolov5/tutorials/pytorch-hub-model-loading) — load models programmatically. - [Model Export](https://docs.ultralytics.com/modes/export) — deploy to ONNX, TensorRT, CoreML, and more. - [Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test-time-augmentation) — improve accuracy at inference. - [Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model-ensembling) — combine models for better results. - [Hyperparameter Tuning](https://docs.ultralytics.com/guides/hyperparameter-tuning) — tune hyperparameters automatically. - [Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer-learning-with-frozen-layers) — adapt pretrained models efficiently.

## 🧠 Architecture YOLOv3 builds detection on a few core ideas that make it both accurate and fast: - **Darknet-53 backbone** — a 53-layer convolutional feature extractor with residual (skip) connections, deeper and more accurate than the Darknet-19 backbone of YOLOv2 while staying efficient. - **Multi-scale detection** — predictions are made at three feature-map scales using a feature-pyramid-style design (upsampling and concatenating earlier feature maps), so the model detects small, medium, and large objects well. - **Anchor boxes** — boxes are predicted relative to dimension-cluster anchor priors, with three anchors per scale (nine total) and a sigmoid offset parameterization for stable training. - **Independent class prediction** — each class is scored with an independent logistic classifier rather than a softmax, so one box can carry multiple non-mutually-exclusive labels. The repository ships three variants of this architecture: - **YOLOv3** — the full Darknet-53 model; the best balance of speed and accuracy. - **YOLOv3-SPP** — adds a Spatial Pyramid Pooling block that pools features at multiple kernel sizes for a larger effective receptive field and a small accuracy gain. - **YOLOv3-tiny** — a compact backbone with detection at two scales, optimized for CPU and edge devices where speed matters most. Models are defined declaratively in [`models/*.yaml`](https://github.com/ultralytics/yolov3/tree/master/models) and built by `parse_model()` in `models/yolo.py`, so the architecture can be inspected and modified without writing Python. ## 🏋️ Pretrained Checkpoints All three models are trained on [COCO](https://docs.ultralytics.com/datasets/detect/coco) (80 classes) and download automatically from the [YOLOv3 release assets](https://github.com/ultralytics/yolov3/releases) on first use. | Model | Description | | ----------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | | [yolov3-tiny.pt](https://github.com/ultralytics/yolov3/releases/download/v9.6.0/yolov3-tiny.pt) | Lightweight two-scale model — the fastest option, ideal for CPU and edge devices. | | [yolov3.pt](https://github.com/ultralytics/yolov3/releases/download/v9.6.0/yolov3.pt) | The original Darknet-53 model — a strong balance of speed and accuracy. | | [yolov3-spp.pt](https://github.com/ultralytics/yolov3/releases/download/v9.6.0/yolov3-spp.pt) | Adds Spatial Pyramid Pooling for a larger receptive field and improved accuracy. | ## 🧩 Integrations Ultralytics integrates with leading AI platforms to extend dataset labeling, training, visualization, and model management. Explore how partners such as [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 streamline your workflow at [Ultralytics Integrations](https://docs.ultralytics.com/integrations).

Ultralytics active learning integrations

## 🤔 Why YOLOv3? YOLOv3 was a landmark in real-time object detection and remains a dependable, well-understood baseline: - **Real-time single-stage detection** — one forward pass produces all detections, with no separate region-proposal stage. - **Strong across object sizes** — multi-scale predictions handle small, medium, and large objects. - **Multi-label friendly** — independent logistic classifiers allow overlapping class labels. - **Simple and portable** — a fully-convolutional design that trains and exports cleanly to many deployment formats. For the broader family of Ultralytics YOLO models, see the [Ultralytics repository](https://github.com/ultralytics/ultralytics). ## ☁️ Environments Get started quickly with pre-configured environments. Click an icon below for setup details.

## 🤝 Contribute Contributions are welcome! Please see the [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started, and 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! [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/yolov3/graphs/contributors) ## 📜 License Ultralytics offers two licensing options: - **AGPL-3.0 License**: An [OSI-approved](https://opensource.org/license/agpl-3.0) open-source license ideal for research and collaboration. See the [LICENSE](https://github.com/ultralytics/yolov3/blob/master/LICENSE) file for details. - **Enterprise License**: For commercial use, this license allows integration of Ultralytics software and models into commercial products without AGPL-3.0 obligations. Contact us via [Ultralytics Licensing](https://www.ultralytics.com/license). ## 📧 Contact For bug reports and feature requests, please use [GitHub Issues](https://github.com/ultralytics/yolov3/issues). For questions and discussion, join our [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Forums](https://community.ultralytics.com).