退购1.1定位算法
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docs/models/index.md
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---
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comments: true
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description: Learn about the supported models and architectures, such as YOLOv3, YOLOv5, and YOLOv8, and how to contribute your own model to Ultralytics.
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---
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# Models
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Ultralytics supports many models and architectures with more to come in the future. Want to add your model architecture? [Here's](../help/contributing.md) how you can contribute.
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In this documentation, we provide information on four major models:
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1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family, known for its efficient real-time object detection capabilities.
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2. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture, offering better performance and speed tradeoffs compared to previous versions.
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3. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
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4. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
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5. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's RT-DETR model.
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You can use these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python:
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## CLI Example
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```bash
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yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
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```
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## Python Example
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```python
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from ultralytics import YOLO
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model = YOLO("model.yaml") # build a YOLOv8n model from scratch
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# YOLO("model.pt") use pre-trained model if available
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model.info() # display model information
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model.train(data="coco128.yaml", epochs=100) # train the model
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```
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For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.
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docs/models/rtdetr.md
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docs/models/rtdetr.md
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---
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comments: true
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description: Explore RT-DETR, a high-performance real-time object detector. Learn how to use pre-trained models with Ultralytics Python API for various tasks.
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---
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# RT-DETR
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## Overview
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Real-Time Detection Transformer (RT-DETR) is an end-to-end object detector that provides real-time performance while maintaining high accuracy. It efficiently processes multi-scale features by decoupling intra-scale interaction and cross-scale fusion, and supports flexible adjustment of inference speed using different decoder layers without retraining. RT-DETR outperforms many real-time object detectors on accelerated backends like CUDA with TensorRT.
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### Key Features
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- **Efficient Hybrid Encoder:** RT-DETR uses an efficient hybrid encoder that processes multi-scale features by decoupling intra-scale interaction and cross-scale fusion. This design reduces computational costs and allows for real-time object detection.
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- **IoU-aware Query Selection:** RT-DETR improves object query initialization by utilizing IoU-aware query selection. This allows the model to focus on the most relevant objects in the scene.
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- **Adaptable Inference Speed:** RT-DETR supports flexible adjustments of inference speed by using different decoder layers without the need for retraining. This adaptability facilitates practical application in various real-time object detection scenarios.
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## Pre-trained Models
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Ultralytics RT-DETR provides several pre-trained models with different scales:
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- RT-DETR-L: 53.0% AP on COCO val2017, 114 FPS on T4 GPU
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- RT-DETR-X: 54.8% AP on COCO val2017, 74 FPS on T4 GPU
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## Usage
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### Python API
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```python
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from ultralytics import RTDETR
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model = RTDETR("rtdetr-l.pt")
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model.info() # display model information
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model.predict("path/to/image.jpg") # predict
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```
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### Supported Tasks
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| Model Type | Pre-trained Weights | Tasks Supported |
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|---------------------|---------------------|------------------|
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| RT-DETR Large | `rtdetr-l.pt` | Object Detection |
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| RT-DETR Extra-Large | `rtdetr-x.pt` | Object Detection |
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### Supported Modes
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| Mode | Supported |
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|------------|--------------------|
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| Inference | :heavy_check_mark: |
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| Validation | :heavy_check_mark: |
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| Training | :x: (Coming soon) |
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# Citations and Acknowledgements
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If you use RT-DETR in your research or development work, please cite the [original paper](https://arxiv.org/abs/2304.08069):
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```bibtex
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@misc{lv2023detrs,
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title={DETRs Beat YOLOs on Real-time Object Detection},
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author={Wenyu Lv and Shangliang Xu and Yian Zhao and Guanzhong Wang and Jinman Wei and Cheng Cui and Yuning Du and Qingqing Dang and Yi Liu},
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year={2023},
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eprint={2304.08069},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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We would like to acknowledge Baidu's [PaddlePaddle]((https://github.com/PaddlePaddle/PaddleDetection)) team for creating and maintaining this valuable resource for the computer vision community.
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docs/models/sam.md
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docs/models/sam.md
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---
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comments: true
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description: Learn about the Segment Anything Model (SAM) and how it provides promptable image segmentation through an advanced architecture and the SA-1B dataset.
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---
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# Segment Anything Model (SAM)
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## Overview
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The Segment Anything Model (SAM) is a groundbreaking image segmentation model that enables promptable segmentation with real-time performance. It forms the foundation for the Segment Anything project, which introduces a new task, model, and dataset for image segmentation. SAM is designed to be promptable, allowing it to transfer zero-shot to new image distributions and tasks. The model is trained on the [SA-1B dataset](https://ai.facebook.com/datasets/segment-anything/), which contains over 1 billion masks on 11 million licensed and privacy-respecting images. SAM has demonstrated impressive zero-shot performance, often surpassing prior fully supervised results.
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Example images with overlaid masks from our newly introduced dataset, SA-1B. SA-1B contains 11M diverse, high-resolution, licensed, and privacy protecting images and 1.1B high-quality segmentation masks. These masks were annotated fully automatically by SAM, and as verified by human ratings and numerous experiments, are of high quality and diversity. Images are grouped by number of masks per image for visualization (there are ∼100 masks per image on average).
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## Key Features
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- **Promptable Segmentation Task:** SAM is designed for a promptable segmentation task, enabling it to return a valid segmentation mask given any segmentation prompt, such as spatial or text information identifying an object.
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- **Advanced Architecture:** SAM utilizes a powerful image encoder, a prompt encoder, and a lightweight mask decoder. This architecture enables flexible prompting, real-time mask computation, and ambiguity awareness in segmentation.
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- **SA-1B Dataset:** The Segment Anything project introduces the SA-1B dataset, which contains over 1 billion masks on 11 million images. This dataset is the largest segmentation dataset to date, providing SAM with a diverse and large-scale source of data for training.
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- **Zero-Shot Performance:** SAM demonstrates remarkable zero-shot performance across a range of segmentation tasks, allowing it to be used out-of-the-box with prompt engineering for various applications.
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For more information about the Segment Anything Model and the SA-1B dataset, please refer to the [Segment Anything website](https://segment-anything.com) and the research paper [Segment Anything](https://arxiv.org/abs/2304.02643).
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## Usage
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SAM can be used for a variety of downstream tasks involving object and image distributions beyond its training data. Examples include edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. By employing prompt engineering, SAM can adapt to new tasks and data distributions in a zero-shot manner, making it a versatile and powerful tool for image segmentation tasks.
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```python
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from ultralytics.vit import SAM
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model = SAM('sam_b.pt')
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model.info() # display model information
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model.predict('path/to/image.jpg') # predict
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```
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## Supported Tasks
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| Model Type | Pre-trained Weights | Tasks Supported |
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|------------|---------------------|-----------------------|
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| sam base | `sam_b.pt` | Instance Segmentation |
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| sam large | `sam_l.pt` | Instance Segmentation |
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## Supported Modes
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| Mode | Supported |
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|------------|--------------------|
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| Inference | :heavy_check_mark: |
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| Validation | :x: |
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| Training | :x: |
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## Auto-Annotation
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Auto-annotation is an essential feature that allows you to generate a [segmentation dataset](https://docs.ultralytics.com/datasets/segment) using a pre-trained detection model. It enables you to quickly and accurately annotate a large number of images without the need for manual labeling, saving time and effort.
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### Generate Segmentation Dataset Using a Detection Model
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To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
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```python
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from ultralytics.yolo.data import auto_annotate
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auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
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```
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| Argument | Type | Description | Default |
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|------------|---------------------|---------------------------------------------------------------------------------------------------------|--------------|
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| data | str | Path to a folder containing images to be annotated. | |
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| det_model | str, optional | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'. | 'yolov8x.pt' |
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| sam_model | str, optional | Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'. | 'sam_b.pt' |
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| device | str, optional | Device to run the models on. Defaults to an empty string (CPU or GPU, if available). | |
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| output_dir | str, None, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | None |
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The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and SAM segmentation models, the device to run the models on, and the output directory for saving the annotated results.
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By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
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## Citations and Acknowledgements
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If you use SAM in your research or development work, please cite the following paper:
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```bibtex
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@misc{kirillov2023segment,
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title={Segment Anything},
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author={Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
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year={2023},
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eprint={2304.02643},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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We would like to acknowledge Meta AI for creating and maintaining this valuable resource for the computer vision community.
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docs/models/yolov3.md
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---
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comments: true
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---
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# 🚧Page Under Construction ⚒
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This page is currently under construction!️👷Please check back later for updates. 😃🔜
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docs/models/yolov5.md
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docs/models/yolov5.md
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---
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comments: true
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description: Detect objects faster and more accurately using Ultralytics YOLOv5u. Find pre-trained models for each task, including Inference, Validation and Training.
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---
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# YOLOv5u
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## Overview
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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.
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## Key Features
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- **Anchor-free Split Ultralytics Head:** YOLOv5u replaces the traditional anchor-based detection head with an anchor-free split Ultralytics head, resulting in improved performance.
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- **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.
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- **Variety of Pre-trained Models:** YOLOv5u offers a range of pre-trained models tailored for various tasks, including Inference, Validation, and Training.
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## Supported Tasks
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| Model Type | Pre-trained Weights | Task |
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|------------|-----------------------------------------------------------------------------------------------------------------------------|-----------|
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| YOLOv5u | `yolov5nu`, `yolov5su`, `yolov5mu`, `yolov5lu`, `yolov5xu`, `yolov5n6u`, `yolov5s6u`, `yolov5m6u`, `yolov5l6u`, `yolov5x6u` | Detection |
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## Supported Modes
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| Mode | Supported |
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|------------|--------------------|
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| Inference | :heavy_check_mark: |
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| Validation | :heavy_check_mark: |
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| Training | :heavy_check_mark: |
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??? Performance
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=== "Detection"
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ---------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv5nu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5nu.pt) | 640 | 34.3 | 73.6 | 1.06 | 2.6 | 7.7 |
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| [YOLOv5su](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5su.pt) | 640 | 43.0 | 120.7 | 1.27 | 9.1 | 24.0 |
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| [YOLOv5mu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5mu.pt) | 640 | 49.0 | 233.9 | 1.86 | 25.1 | 64.2 |
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| [YOLOv5lu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5lu.pt) | 640 | 52.2 | 408.4 | 2.50 | 53.2 | 135.0 |
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| [YOLOv5xu](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5xu.pt) | 640 | 53.2 | 763.2 | 3.81 | 97.2 | 246.4 |
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| | | | | | | |
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| [YOLOv5n6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5n6u.pt) | 1280 | 42.1 | - | - | 4.3 | 7.8 |
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| [YOLOv5s6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5s6u.pt) | 1280 | 48.6 | - | - | 15.3 | 24.6 |
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| [YOLOv5m6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5m6u.pt) | 1280 | 53.6 | - | - | 41.2 | 65.7 |
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| [YOLOv5l6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5l6u.pt) | 1280 | 55.7 | - | - | 86.1 | 137.4 |
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| [YOLOv5x6u](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov5x6u.pt) | 1280 | 56.8 | - | - | 155.4 | 250.7 |
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docs/models/yolov8.md
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docs/models/yolov8.md
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---
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comments: true
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description: Learn about YOLOv8's pre-trained weights supporting detection, instance segmentation, pose, and classification tasks. Get performance details.
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---
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# YOLOv8
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## Overview
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YOLOv8 is the latest iteration in the YOLO series of real-time object detectors, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduces new features and optimizations that make it an ideal choice for various object detection tasks in a wide range of applications.
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## Key Features
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- **Advanced Backbone and Neck Architectures:** YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved feature extraction and object detection performance.
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- **Anchor-free Split Ultralytics Head:** YOLOv8 adopts an anchor-free split Ultralytics head, which contributes to better accuracy and a more efficient detection process compared to anchor-based approaches.
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- **Optimized Accuracy-Speed Tradeoff:** With a focus on maintaining an optimal balance between accuracy and speed, YOLOv8 is suitable for real-time object detection tasks in diverse application areas.
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- **Variety of Pre-trained Models:** YOLOv8 offers a range of pre-trained models to cater to various tasks and performance requirements, making it easier to find the right model for your specific use case.
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## Supported Tasks
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| Model Type | Pre-trained Weights | Task |
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|-------------|------------------------------------------------------------------------------------------------------------------|-----------------------|
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| YOLOv8 | `yolov8n.pt`, `yolov8s.pt`, `yolov8m.pt`, `yolov8l.pt`, `yolov8x.pt` | Detection |
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| YOLOv8-seg | `yolov8n-seg.pt`, `yolov8s-seg.pt`, `yolov8m-seg.pt`, `yolov8l-seg.pt`, `yolov8x-seg.pt` | Instance Segmentation |
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| YOLOv8-pose | `yolov8n-pose.pt`, `yolov8s-pose.pt`, `yolov8m-pose.pt`, `yolov8l-pose.pt`, `yolov8x-pose.pt` ,`yolov8x-pose-p6` | Pose/Keypoints |
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| YOLOv8-cls | `yolov8n-cls.pt`, `yolov8s-cls.pt`, `yolov8m-cls.pt`, `yolov8l-cls.pt`, `yolov8x-cls.pt` | Classification |
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## Supported Modes
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| Mode | Supported |
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|------------|--------------------|
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| Inference | :heavy_check_mark: |
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| Validation | :heavy_check_mark: |
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| Training | :heavy_check_mark: |
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??? Performance
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=== "Detection"
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
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=== "Segmentation"
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
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| [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
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| [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
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| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
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| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
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=== "Classification"
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| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
|
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| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
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| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
|
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| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |
|
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| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |
|
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| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
|
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| [YOLOv8x-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
|
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|
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=== "Pose"
|
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|
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| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|
||||
| ---------------------------------------------------------------------------------------------------- | --------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
|
||||
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | 50.4 | 80.1 | 131.8 | 1.18 | 3.3 | 9.2 |
|
||||
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | 60.0 | 86.2 | 233.2 | 1.42 | 11.6 | 30.2 |
|
||||
| [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 |
|
||||
Reference in New Issue
Block a user