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docs/models/yolov3.md
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---
comments: true
description: YOLOv3, YOLOv3-Ultralytics and YOLOv3u by Ultralytics explained. Learn the evolution of these models and their specifications.
---
# 🚧Page Under Construction ⚒
# YOLOv3, YOLOv3-Ultralytics, and YOLOv3u
This page is currently under construction!️👷Please check back later for updates. 😃🔜
## Overview
This document presents an overview of three closely related object detection models, namely [YOLOv3](https://pjreddie.com/darknet/yolo/), [YOLOv3-Ultralytics](https://github.com/ultralytics/yolov3), and [YOLOv3u](https://github.com/ultralytics/ultralytics).
1. **YOLOv3:** This is the third version of the You Only Look Once (YOLO) object detection algorithm. Originally developed by Joseph Redmon, YOLOv3 improved on its predecessors by introducing features such as multiscale predictions and three different sizes of detection kernels.
2. **YOLOv3-Ultralytics:** This is Ultralytics' implementation of the YOLOv3 model. It reproduces the original YOLOv3 architecture and offers additional functionalities, such as support for more pre-trained models and easier customization options.
3. **YOLOv3u:** This is an updated version of YOLOv3-Ultralytics that incorporates the anchor-free, objectness-free split head used in YOLOv8 models. YOLOv3u maintains the same backbone and neck architecture as YOLOv3 but with the updated detection head from YOLOv8.
## Key Features
- **YOLOv3:** Introduced the use of three different scales for detection, leveraging three different sizes of detection kernels: 13x13, 26x26, and 52x52. This significantly improved detection accuracy for objects of different sizes. Additionally, YOLOv3 added features such as multi-label predictions for each bounding box and a better feature extractor network.
- **YOLOv3-Ultralytics:** Ultralytics' implementation of YOLOv3 provides the same performance as the original model but comes with added support for more pre-trained models, additional training methods, and easier customization options. This makes it more versatile and user-friendly for practical applications.
- **YOLOv3u:** This updated model incorporates the anchor-free, objectness-free split head from YOLOv8. By eliminating the need for pre-defined anchor boxes and objectness scores, this detection head design can improve the model's ability to detect objects of varying sizes and shapes. This makes YOLOv3u more robust and accurate for object detection tasks.
## Supported Tasks
YOLOv3, YOLOv3-Ultralytics, and YOLOv3u all support the following tasks:
- Object Detection
## Supported Modes
All three models support the following modes:
- Inference
- Validation
- Training
- Export
## Performance
Below is a comparison of the performance of the three models. The performance is measured in terms of the Mean Average Precision (mAP) on the COCO dataset:
TODO
## Usage
You can use these models for object detection tasks using the Ultralytics YOLOv3 repository. The following is a sample code snippet showing how to use the YOLOv3u model for inference:
```python
from ultralytics import YOLO
# Load the model
model = YOLO('yolov3.pt') # load a pretrained model
# Perform inference
results = model('image.jpg')
# Print the results
results.print()
```
## Citations and Acknowledgments
If you use YOLOv3 in your research, please cite the original YOLO papers and the Ultralytics YOLOv3 repository:
```bibtex
@article{redmon2018yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal={arXiv preprint arXiv:1804.02767},
year={2018}
}
```
Thank you to Joseph Redmon and Ali Farhadi for developing the original YOLOv3.

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docs/models/yolov5.md
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---
comments: true
description: Detect objects faster and more accurately using Ultralytics YOLOv5u. Find pre-trained models for each task, including Inference, Validation and Training.
description: YOLOv5u by Ultralytics explained. Discover the evolution of this model and its key specifications. Experience faster and more accurate object detection.
---
# YOLOv5u
## Overview
YOLOv5u is an updated version of YOLOv5 that incorporates the anchor-free split Ultralytics head used in the YOLOv8 models. It retains the same backbone and neck architecture as YOLOv5 but offers improved accuracy-speed tradeoff for object detection tasks.
YOLOv5u is an enhanced version of the [YOLOv5](https://github.com/ultralytics/yolov5) object detection model from Ultralytics. This iteration incorporates the anchor-free, objectness-free split head that is featured in the [YOLOv8](./yolov8.md) models. Although it maintains the same backbone and neck architecture as YOLOv5, YOLOv5u provides an improved accuracy-speed tradeoff for object detection tasks, making it a robust choice for numerous applications.
## Key Features
- **Anchor-free Split Ultralytics Head:** YOLOv5u replaces the traditional anchor-based detection head with an anchor-free split Ultralytics head, resulting in improved performance.
- **Optimized Accuracy-Speed Tradeoff:** The updated model offers a better balance between accuracy and speed, making it more suitable for a wider range of applications.
- **Variety of Pre-trained Models:** YOLOv5u offers a range of pre-trained models tailored for various tasks, including Inference, Validation, and Training.
- **Anchor-free Split Ultralytics Head:** YOLOv5u replaces the conventional anchor-based detection head with an anchor-free split Ultralytics head, boosting performance in object detection tasks.
- **Optimized Accuracy-Speed Tradeoff:** By delivering a better balance between accuracy and speed, YOLOv5u is suitable for a diverse range of real-time applications, from autonomous driving to video surveillance.
- **Variety of Pre-trained Models:** YOLOv5u includes numerous pre-trained models for tasks like Inference, Validation, and Training, providing the flexibility to tackle various object detection challenges.
## Supported Tasks
@ -45,4 +47,40 @@ YOLOv5u is an updated version of YOLOv5 that incorporates the anchor-free split
| [YOLOv5s6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5s6u.pt) | 1280 | 48.6 | - | - | 15.3 | 24.6 |
| [YOLOv5m6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5m6u.pt) | 1280 | 53.6 | - | - | 41.2 | 65.7 |
| [YOLOv5l6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5l6u.pt) | 1280 | 55.7 | - | - | 86.1 | 137.4 |
| [YOLOv5x6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5x6u.pt) | 1280 | 56.8 | - | - | 155.4 | 250.7 |
| [YOLOv5x6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5x6u.pt) | 1280 | 56.8 | - | - | 155.4 | 250.7 |
## Usage
You can use YOLOv5u for object detection tasks using the Ultralytics repository. The following is a sample code snippet showing how to use YOLOv5u model for inference:
```python
from ultralytics import YOLO
# Load the model
model = YOLO('yolov5n.pt') # load a pretrained model
# Perform inference
results = model('image.jpg')
# Print the results
results.print()
```
## Citations and Acknowledgments
If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv5 repository as follows:
```bibtex
@software{yolov5,
title = {YOLOv5 by Ultralytics},
author = {Glenn Jocher},
year = {2020},
version = {7.0},
license = {AGPL-3.0},
url = {https://github.com/ultralytics/yolov5},
doi = {10.5281/zenodo.3908559},
orcid = {0000-0001-5950-6979}
}
```
Special thanks to Glenn Jocher and the Ultralytics team for their work on developing and maintaining the YOLOv5 and YOLOv5u models.

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docs/models/yolov8.md
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@ -74,4 +74,41 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | 65.0 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 |
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | 67.6 | 90.0 | 784.5 | 2.59 | 44.4 | 168.6 |
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |
## Usage
You can use YOLOv8 for object detection tasks using the Ultralytics pip package. The following is a sample code snippet showing how to use YOLOv8 models for inference:
```python
from ultralytics import YOLO
# Load the model
model = YOLO('yolov8n.pt') # load a pretrained model
# Perform inference
results = model('image.jpg')
# Print the results
results.print()
```
## Citation
If you use the YOLOv8 model or any other software from this repository in your work, please cite it using the following format:
```bibtex
@software{yolov8_ultralytics,
author = {Glenn Jocher and Ayush Chaurasia and Jing Qiu},
title = {YOLO by Ultralytics},
version = {8.0.0},
year = {2023},
url = {https://github.com/ultralytics/ultralytics},
orcid = {0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069},
license = {AGPL-3.0}
}
```
Please note that the DOI is pending and will be added to the citation once it is available. The usage of the software is in accordance with the AGPL-3.0 license.