Add Docs glossary links (#16448)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@ -25,7 +25,7 @@ The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based soluti
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## Overview
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FastSAM is designed to address the limitations of the [Segment Anything Model (SAM)](sam.md), a heavy Transformer model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-instance segmentation and prompt-guided selection. The first stage uses [YOLOv8-seg](../tasks/segment.md) to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
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FastSAM is designed to address the limitations of the [Segment Anything Model (SAM)](sam.md), a heavy [Transformer](https://www.ultralytics.com/glossary/transformer) model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-[instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) and prompt-guided selection. The first stage uses [YOLOv8-seg](../tasks/segment.md) to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
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## Key Features
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@ -58,7 +58,7 @@ The FastSAM models are easy to integrate into your Python applications. Ultralyt
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### Predict Usage
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To perform object detection on an image, use the `predict` method as shown below:
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To perform [object detection](https://www.ultralytics.com/glossary/object-detection) on an image, use the `predict` method as shown below:
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!!! example
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@ -224,7 +224,7 @@ FastSAM is also available directly from the [https://github.com/CASIA-IVA-Lab/Fa
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python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --text_prompt "the yellow dog"
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```
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- Segment objects within a bounding box (provide box coordinates in xywh format):
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- Segment objects within a [bounding box](https://www.ultralytics.com/glossary/bounding-box) (provide box coordinates in xywh format):
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```shell
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python Inference.py --model_path ./weights/FastSAM.pt --img_path ./images/dogs.jpg --box_prompt "[570,200,230,400]"
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@ -262,7 +262,7 @@ The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12
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### What is FastSAM and how does it differ from SAM?
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FastSAM, short for Fast Segment Anything Model, is a real-time convolutional neural network (CNN)-based solution designed to reduce computational demands while maintaining high performance in object segmentation tasks. Unlike the Segment Anything Model (SAM), which uses a heavier Transformer-based architecture, FastSAM leverages [Ultralytics YOLOv8-seg](../tasks/segment.md) for efficient instance segmentation in two stages: all-instance segmentation followed by prompt-guided selection.
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FastSAM, short for Fast Segment Anything Model, is a real-time [convolutional neural network](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNN)-based solution designed to reduce computational demands while maintaining high performance in object segmentation tasks. Unlike the Segment Anything Model (SAM), which uses a heavier Transformer-based architecture, FastSAM leverages [Ultralytics YOLOv8-seg](../tasks/segment.md) for efficient instance segmentation in two stages: all-instance segmentation followed by prompt-guided selection.
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### How does FastSAM achieve real-time segmentation performance?
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@ -270,7 +270,7 @@ FastSAM achieves real-time segmentation by decoupling the segmentation task into
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### What are the practical applications of FastSAM?
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FastSAM is practical for a variety of computer vision tasks that require real-time segmentation performance. Applications include:
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FastSAM is practical for a variety of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks that require real-time segmentation performance. Applications include:
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- Industrial automation for quality control and assurance
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- Real-time video analysis for security and surveillance
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@ -17,7 +17,7 @@ Here are some of the key models supported:
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3. **[YOLOv5](yolov5.md)**: An improved version of the YOLO architecture by Ultralytics, offering better performance and speed trade-offs compared to previous versions.
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4. **[YOLOv6](yolov6.md)**: Released by [Meituan](https://about.meituan.com/) in 2022, and in use in many of the company's autonomous delivery robots.
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5. **[YOLOv7](yolov7.md)**: Updated YOLO models released in 2022 by the authors of YOLOv4.
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6. **[YOLOv8](yolov8.md) NEW 🚀**: The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
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6. **[YOLOv8](yolov8.md) NEW 🚀**: The latest version of the YOLO family, featuring enhanced capabilities such as [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose/keypoints estimation, and classification.
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7. **[YOLOv9](yolov9.md)**: An experimental model trained on the Ultralytics [YOLOv5](yolov5.md) codebase implementing Programmable Gradient Information (PGI).
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8. **[YOLOv10](yolov10.md)**: By Tsinghua University, featuring NMS-free training and efficiency-accuracy driven architecture, delivering state-of-the-art performance and latency.
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9. **[Segment Anything Model (SAM)](sam.md)**: Meta's original Segment Anything Model (SAM).
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@ -25,7 +25,7 @@ Here are some of the key models supported:
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11. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
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12. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
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13. **[YOLO-NAS](yolo-nas.md)**: YOLO Neural Architecture Search (NAS) Models.
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14. **[Realtime Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
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14. **[Realtime Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection [Transformer](https://www.ultralytics.com/glossary/transformer) (RT-DETR) models.
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15. **[YOLO-World](yolo-world.md)**: Real-time Open Vocabulary Object Detection models from Tencent AI Lab.
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<p align="center">
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@ -43,13 +43,13 @@ Here are some of the key models supported:
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This example provides simple YOLO training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
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Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for object detection. For additional supported tasks see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md) and [Pose](../tasks/pose.md) docs.
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Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for [object detection](https://www.ultralytics.com/glossary/object-detection). For additional supported tasks see the [Segment](../tasks/segment.md), [Classify](../tasks/classify.md) and [Pose](../tasks/pose.md) docs.
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!!! example
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=== "Python"
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PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()`, `SAM()`, `NAS()` and `RTDETR()` classes to create a model instance in Python:
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[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()`, `SAM()`, `NAS()` and `RTDETR()` classes to create a model instance in Python:
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```python
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from ultralytics import YOLO
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@ -101,7 +101,7 @@ For detailed steps, consult our [Contributing Guide](../help/contributing.md).
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### What are the key advantages of using Ultralytics YOLOv8 for object detection?
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Ultralytics YOLOv8 offers enhanced capabilities such as real-time object detection, instance segmentation, pose estimation, and classification. Its optimized architecture ensures high-speed performance without sacrificing accuracy, making it ideal for a variety of applications. YOLOv8 also includes built-in compatibility with popular datasets and models, as detailed on the [YOLOv8 documentation page](../models/yolov8.md).
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Ultralytics YOLOv8 offers enhanced capabilities such as real-time object detection, instance segmentation, pose estimation, and classification. Its optimized architecture ensures high-speed performance without sacrificing [accuracy](https://www.ultralytics.com/glossary/accuracy), making it ideal for a variety of applications. YOLOv8 also includes built-in compatibility with popular datasets and models, as detailed on the [YOLOv8 documentation page](../models/yolov8.md).
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### How can I train a YOLOv8 model on custom data?
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@ -133,7 +133,7 @@ For more detailed instructions, visit the [Train](../modes/train.md) documentati
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Ultralytics supports a comprehensive range of YOLO (You Only Look Once) versions from YOLOv3 to YOLOv10, along with models like NAS, SAM, and RT-DETR. Each version is optimized for various tasks such as detection, segmentation, and classification. For detailed information on each model, refer to the [Models Supported by Ultralytics](../models/index.md) documentation.
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### Why should I use Ultralytics HUB for machine learning projects?
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### Why should I use Ultralytics HUB for [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) projects?
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Ultralytics HUB provides a no-code, end-to-end platform for training, deploying, and managing YOLO models. It simplifies complex workflows, enabling users to focus on model performance and application. The HUB also offers cloud training capabilities, comprehensive dataset management, and user-friendly interfaces. Learn more about it on the [Ultralytics HUB](../hub/index.md) documentation page.
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@ -122,7 +122,7 @@ If you find MobileSAM useful in your research or development work, please consid
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### What is MobileSAM and how does it differ from the original SAM model?
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MobileSAM is a lightweight, fast image segmentation model designed for mobile applications. It retains the same pipeline as the original SAM but replaces the heavyweight ViT-H encoder (632M parameters) with a smaller Tiny-ViT encoder (5M parameters). This change results in MobileSAM being approximately 5 times smaller and 7 times faster than the original SAM. For instance, MobileSAM operates at about 12ms per image, compared to the original SAM's 456ms. You can learn more about the MobileSAM implementation in various projects [here](https://github.com/ChaoningZhang/MobileSAM).
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MobileSAM is a lightweight, fast [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) model designed for mobile applications. It retains the same pipeline as the original SAM but replaces the heavyweight ViT-H encoder (632M parameters) with a smaller Tiny-ViT encoder (5M parameters). This change results in MobileSAM being approximately 5 times smaller and 7 times faster than the original SAM. For instance, MobileSAM operates at about 12ms per image, compared to the original SAM's 456ms. You can learn more about the MobileSAM implementation in various projects [here](https://github.com/ChaoningZhang/MobileSAM).
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### How can I test MobileSAM using Ultralytics?
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MobileSAM is designed for fast and efficient image segmentation in mobile environments. Primary use cases include:
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- **Real-time object detection and segmentation** for mobile applications.
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- **Real-time [object detection](https://www.ultralytics.com/glossary/object-detection) and segmentation** for mobile applications.
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- **Low-latency image processing** in devices with limited computational resources.
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- **Integration in AI-driven mobile apps** for tasks such as augmented reality (AR) and real-time analytics.
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@ -4,11 +4,11 @@ description: Explore Baidu's RT-DETR, a Vision Transformer-based real-time objec
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keywords: RT-DETR, Baidu, Vision Transformer, real-time object detection, PaddlePaddle, Ultralytics, pre-trained models, AI, machine learning, computer vision
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---
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# Baidu's RT-DETR: A Vision Transformer-Based Real-Time Object Detector
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# Baidu's RT-DETR: A Vision [Transformer](https://www.ultralytics.com/glossary/transformer)-Based Real-Time Object Detector
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## Overview
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Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge end-to-end object detector that provides real-time performance while maintaining high accuracy. It is based on the idea of DETR (the NMS-free framework), meanwhile introducing conv-based backbone and an efficient hybrid encoder to gain real-time speed. RT-DETR efficiently processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. The model is highly adaptable, supporting flexible adjustment of inference speed using different decoder layers without retraining. RT-DETR excels on accelerated backends like CUDA with TensorRT, outperforming many other real-time object detectors.
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Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge end-to-end object detector that provides real-time performance while maintaining high [accuracy](https://www.ultralytics.com/glossary/accuracy). It is based on the idea of DETR (the NMS-free framework), meanwhile introducing conv-based backbone and an efficient hybrid encoder to gain real-time speed. RT-DETR efficiently processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. The model is highly adaptable, supporting flexible adjustment of inference speed using different decoder layers without retraining. RT-DETR excels on accelerated backends like CUDA with TensorRT, outperforming many other real-time object detectors.
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<p align="center">
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<br>
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### Key Features
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- **Efficient Hybrid Encoder:** Baidu's RT-DETR uses an efficient hybrid encoder that processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. This unique Vision Transformers-based design reduces computational costs and allows for real-time object detection.
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- **Efficient Hybrid Encoder:** Baidu's RT-DETR uses an efficient hybrid encoder that processes multiscale features by decoupling intra-scale interaction and cross-scale fusion. This unique Vision Transformers-based design reduces computational costs and allows for real-time [object detection](https://www.ultralytics.com/glossary/object-detection).
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- **IoU-aware Query Selection:** Baidu's 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, enhancing the detection accuracy.
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- **Adaptable Inference Speed:** Baidu's 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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@ -98,7 +98,7 @@ If you use Baidu's RT-DETR in your research or development work, please cite the
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}
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```
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We would like to acknowledge Baidu and the [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the computer vision community. Their contribution to the field with the development of the Vision Transformers-based real-time object detector, RT-DETR, is greatly appreciated.
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We would like to acknowledge Baidu and the [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. Their contribution to the field with the development of the Vision Transformers-based real-time object detector, RT-DETR, is greatly appreciated.
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## FAQ
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### How does RT-DETR support adaptable inference speed for different real-time applications?
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Baidu's RT-DETR allows flexible adjustments of inference speed by using different decoder layers without requiring retraining. This adaptability is crucial for scaling performance across various real-time object detection tasks. Whether you need faster processing for lower precision needs or slower, more accurate detections, RT-DETR can be tailored to meet your specific requirements.
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Baidu's RT-DETR allows flexible adjustments of inference speed by using different decoder layers without requiring retraining. This adaptability is crucial for scaling performance across various real-time object detection tasks. Whether you need faster processing for lower [precision](https://www.ultralytics.com/glossary/precision) needs or slower, more accurate detections, RT-DETR can be tailored to meet your specific requirements.
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### Can I use RT-DETR models with other Ultralytics modes, such as training, validation, and export?
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@ -38,18 +38,18 @@ For a deeper understanding of SAM 2's architecture and capabilities, explore the
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SAM 2 sets a new benchmark in the field, outperforming previous models on various metrics:
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| Metric | SAM 2 | Previous SOTA |
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| ---------------------------------- | ------------- | ------------- |
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| **Interactive Video Segmentation** | **Best** | - |
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| **Human Interactions Required** | **3x fewer** | Baseline |
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| **Image Segmentation Accuracy** | **Improved** | SAM |
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| **Inference Speed** | **6x faster** | SAM |
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| Metric | SAM 2 | Previous SOTA |
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| ------------------------------------------------------------------------------------------ | ------------- | ------------- |
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| **Interactive Video Segmentation** | **Best** | - |
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| **Human Interactions Required** | **3x fewer** | Baseline |
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| **[Image Segmentation](https://www.ultralytics.com/glossary/image-segmentation) Accuracy** | **Improved** | SAM |
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| **Inference Speed** | **6x faster** | SAM |
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## Model Architecture
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### Core Components
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- **Image and Video Encoder**: Utilizes a transformer-based architecture to extract high-level features from both images and video frames. This component is responsible for understanding the visual content at each timestep.
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- **Image and Video Encoder**: Utilizes a [transformer](https://www.ultralytics.com/glossary/transformer)-based architecture to extract high-level features from both images and video frames. This component is responsible for understanding the visual content at each timestep.
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- **Prompt Encoder**: Processes user-provided prompts (points, boxes, masks) to guide the segmentation task. This allows SAM 2 to adapt to user input and target specific objects within a scene.
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- **Memory Mechanism**: Includes a memory encoder, memory bank, and memory attention module. These components collectively store and utilize information from past frames, enabling the model to maintain consistent object tracking over time.
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- **Mask Decoder**: Generates the final segmentation masks based on the encoded image features and prompts. In video, it also uses memory context to ensure accurate tracking across frames.
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- **Tracking Stability**: SAM 2 may lose track of objects during extended sequences or significant viewpoint changes.
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- **Object Confusion**: The model can sometimes confuse similar-looking objects, particularly in crowded scenes.
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- **Efficiency with Multiple Objects**: Segmentation efficiency decreases when processing multiple objects simultaneously due to the lack of inter-object communication.
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- **Detail Accuracy**: May miss fine details, especially with fast-moving objects. Additional prompts can partially address this issue, but temporal smoothness is not guaranteed.
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- **Detail [Accuracy](https://www.ultralytics.com/glossary/accuracy)**: May miss fine details, especially with fast-moving objects. Additional prompts can partially address this issue, but temporal smoothness is not guaranteed.
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## Citations and Acknowledgements
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### How does SAM 2 compare to other segmentation models like YOLOv8?
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SAM 2 and Ultralytics YOLOv8 serve different purposes and excel in different areas. While SAM 2 is designed for comprehensive object segmentation with advanced features like zero-shot generalization and real-time performance, YOLOv8 is optimized for speed and efficiency in object detection and segmentation tasks. Here's a comparison:
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SAM 2 and Ultralytics YOLOv8 serve different purposes and excel in different areas. While SAM 2 is designed for comprehensive object segmentation with advanced features like zero-shot generalization and real-time performance, YOLOv8 is optimized for speed and efficiency in [object detection](https://www.ultralytics.com/glossary/object-detection) and segmentation tasks. Here's a comparison:
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| Model | Size<br><sup>(MB)</sup> | Parameters<br><sup>(M)</sup> | Speed (CPU)<br><sup>(ms/im)</sup> |
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| ---------------------------------------------- | ----------------------- | ---------------------------- | --------------------------------- |
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@ -6,7 +6,7 @@ keywords: Segment Anything, SAM, image segmentation, promptable segmentation, ze
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# Segment Anything Model (SAM)
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Welcome to the frontier of image segmentation with the Segment Anything Model, or SAM. This revolutionary model has changed the game by introducing promptable image segmentation with real-time performance, setting new standards in the field.
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Welcome to the frontier of [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) with the Segment Anything Model, or SAM. This revolutionary model has changed the game by introducing promptable image segmentation with real-time performance, setting new standards in the field.
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## Introduction to SAM: The Segment Anything Model
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- **Promptable Segmentation Task:** SAM was designed with a promptable segmentation task in mind, allowing it to generate valid segmentation masks from any given prompt, such as spatial or text clues identifying an object.
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- **Advanced Architecture:** The Segment Anything Model employs a powerful image encoder, a prompt encoder, and a lightweight mask decoder. This unique architecture enables flexible prompting, real-time mask computation, and ambiguity awareness in segmentation tasks.
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- **The SA-1B Dataset:** Introduced by the Segment Anything project, the SA-1B dataset features over 1 billion masks on 11 million images. As the largest segmentation dataset to date, it provides SAM with a diverse and large-scale training data source.
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- **Zero-Shot Performance:** SAM displays outstanding zero-shot performance across various segmentation tasks, making it a ready-to-use tool for diverse applications with minimal need for prompt engineering.
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- **Zero-Shot Performance:** SAM displays outstanding zero-shot performance across various segmentation tasks, making it a ready-to-use tool for diverse applications with minimal need for [prompt engineering](https://www.ultralytics.com/glossary/prompt-engineering).
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For an in-depth look at the Segment Anything Model and the SA-1B dataset, please visit the [Segment Anything website](https://segment-anything.com/) and check out the research paper [Segment Anything](https://arxiv.org/abs/2304.02643).
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## How to Use SAM: Versatility and Power in Image Segmentation
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The Segment Anything Model can be employed for a multitude of downstream tasks that go beyond its training data. This includes edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. With prompt engineering, SAM can swiftly adapt to new tasks and data distributions in a zero-shot manner, establishing it as a versatile and potent tool for all your image segmentation needs.
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The Segment Anything Model can be employed for a multitude of downstream tasks that go beyond its training data. This includes edge detection, object proposal generation, [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), and preliminary text-to-mask prediction. With prompt engineering, SAM can swiftly adapt to new tasks and data distributions in a zero-shot manner, establishing it as a versatile and potent tool for all your image segmentation needs.
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### SAM prediction example
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@ -222,7 +222,7 @@ If you find SAM useful in your research or development work, please consider cit
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}
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```
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We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the computer vision community.
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We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community.
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## FAQ
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@ -273,4 +273,4 @@ This function takes the path to your images and optional arguments for pre-train
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|
||||
### What datasets are used to train the Segment Anything Model (SAM)?
|
||||
|
||||
SAM is trained on the extensive [SA-1B dataset](https://ai.facebook.com/datasets/segment-anything/) which comprises over 1 billion masks across 11 million images. SA-1B is the largest segmentation dataset to date, providing high-quality and diverse training data, ensuring impressive zero-shot performance in varied segmentation tasks. For more details, visit the [Dataset section](#key-features-of-the-segment-anything-model-sam).
|
||||
SAM is trained on the extensive [SA-1B dataset](https://ai.facebook.com/datasets/segment-anything/) which comprises over 1 billion masks across 11 million images. SA-1B is the largest segmentation dataset to date, providing high-quality and diverse [training data](https://www.ultralytics.com/glossary/training-data), ensuring impressive zero-shot performance in varied segmentation tasks. For more details, visit the [Dataset section](#key-features-of-the-segment-anything-model-sam).
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ keywords: YOLO-NAS, Deci AI, object detection, deep learning, Neural Architectur
|
|||
|
||||
## Overview
|
||||
|
||||
Developed by Deci AI, YOLO-NAS is a groundbreaking object detection foundational model. It is the product of advanced Neural Architecture Search technology, meticulously designed to address the limitations of previous YOLO models. With significant improvements in quantization support and accuracy-latency trade-offs, YOLO-NAS represents a major leap in object detection.
|
||||
Developed by Deci AI, YOLO-NAS is a groundbreaking object detection foundational model. It is the product of advanced Neural Architecture Search technology, meticulously designed to address the limitations of previous YOLO models. With significant improvements in quantization support and [accuracy](https://www.ultralytics.com/glossary/accuracy)-latency trade-offs, YOLO-NAS represents a major leap in object detection.
|
||||
|
||||
 **Overview of YOLO-NAS.** YOLO-NAS employs quantization-aware blocks and selective quantization for optimal performance. The model, when converted to its INT8 quantized version, experiences a minimal precision drop, a significant improvement over other models. These advancements culminate in a superior architecture with unprecedented object detection capabilities and outstanding performance.
|
||||
|
||||
|
|
@ -31,7 +31,7 @@ Experience the power of next-generation object detection with the pre-trained YO
|
|||
| YOLO-NAS M INT-8 | 51.0 | 3.78 |
|
||||
| YOLO-NAS L INT-8 | 52.1 | 4.78 |
|
||||
|
||||
Each model variant is designed to offer a balance between Mean Average Precision (mAP) and latency, helping you optimize your object detection tasks for both performance and speed.
|
||||
Each model variant is designed to offer a balance between [Mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) and latency, helping you optimize your object detection tasks for both performance and speed.
|
||||
|
||||
## Usage Examples
|
||||
|
||||
|
|
@ -49,7 +49,7 @@ In this example we validate YOLO-NAS-s on the COCO8 dataset.
|
|||
|
||||
=== "Python"
|
||||
|
||||
PyTorch pretrained `*.pt` models files can be passed to the `NAS()` class to create a model instance in python:
|
||||
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models files can be passed to the `NAS()` class to create a model instance in python:
|
||||
|
||||
```python
|
||||
from ultralytics import NAS
|
||||
|
|
@ -84,7 +84,7 @@ In this example we validate YOLO-NAS-s on the COCO8 dataset.
|
|||
We offer three variants of the YOLO-NAS models: Small (s), Medium (m), and Large (l). Each variant is designed to cater to different computational and performance needs:
|
||||
|
||||
- **YOLO-NAS-s**: Optimized for environments where computational resources are limited but efficiency is key.
|
||||
- **YOLO-NAS-m**: Offers a balanced approach, suitable for general-purpose object detection with higher accuracy.
|
||||
- **YOLO-NAS-m**: Offers a balanced approach, suitable for general-purpose [object detection](https://www.ultralytics.com/glossary/object-detection) with higher accuracy.
|
||||
- **YOLO-NAS-l**: Tailored for scenarios requiring the highest accuracy, where computational resources are less of a constraint.
|
||||
|
||||
Below is a detailed overview of each model, including links to their pre-trained weights, the tasks they support, and their compatibility with different operating modes.
|
||||
|
|
@ -115,7 +115,7 @@ If you employ YOLO-NAS in your research or development work, please cite SuperGr
|
|||
}
|
||||
```
|
||||
|
||||
We express our gratitude to Deci AI's [SuperGradients](https://github.com/Deci-AI/super-gradients/) team for their efforts in creating and maintaining this valuable resource for the computer vision community. We believe YOLO-NAS, with its innovative architecture and superior object detection capabilities, will become a critical tool for developers and researchers alike.
|
||||
We express our gratitude to Deci AI's [SuperGradients](https://github.com/Deci-AI/super-gradients/) team for their efforts in creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. We believe YOLO-NAS, with its innovative architecture and superior object detection capabilities, will become a critical tool for developers and researchers alike.
|
||||
|
||||
## FAQ
|
||||
|
||||
|
|
@ -146,7 +146,7 @@ For more information, refer to the [Inference and Validation Examples](#inferenc
|
|||
|
||||
YOLO-NAS introduces several key features that make it a superior choice for object detection tasks:
|
||||
|
||||
- **Quantization-Friendly Basic Block:** Enhanced architecture that improves model performance with minimal precision drop post quantization.
|
||||
- **Quantization-Friendly Basic Block:** Enhanced architecture that improves model performance with minimal [precision](https://www.ultralytics.com/glossary/precision) drop post quantization.
|
||||
- **Sophisticated Training and Quantization:** Employs advanced training schemes and post-training quantization techniques.
|
||||
- **AutoNAC Optimization and Pre-training:** Utilizes AutoNAC optimization and is pre-trained on prominent datasets like COCO, Objects365, and Roboflow 100.
|
||||
These features contribute to its high accuracy, efficient performance, and suitability for deployment in production environments. Learn more in the [Key Features](#key-features) section.
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ The YOLO-World Model introduces an advanced, real-time [Ultralytics](https://www
|
|||
|
||||
## Overview
|
||||
|
||||
YOLO-World tackles the challenges faced by traditional Open-Vocabulary detection models, which often rely on cumbersome Transformer models requiring extensive computational resources. These models' dependence on pre-defined object categories also restricts their utility in dynamic scenarios. YOLO-World revitalizes the YOLOv8 framework with open-vocabulary detection capabilities, employing vision-language modeling and pre-training on expansive datasets to excel at identifying a broad array of objects in zero-shot scenarios with unmatched efficiency.
|
||||
YOLO-World tackles the challenges faced by traditional Open-Vocabulary detection models, which often rely on cumbersome [Transformer](https://www.ultralytics.com/glossary/transformer) models requiring extensive computational resources. These models' dependence on pre-defined object categories also restricts their utility in dynamic scenarios. YOLO-World revitalizes the YOLOv8 framework with open-vocabulary detection capabilities, employing vision-[language modeling](https://www.ultralytics.com/glossary/language-modeling) and pre-training on expansive datasets to excel at identifying a broad array of objects in zero-shot scenarios with unmatched efficiency.
|
||||
|
||||
## Key Features
|
||||
|
||||
|
|
@ -81,13 +81,13 @@ The YOLO-World models are easy to integrate into your Python applications. Ultra
|
|||
|
||||
We strongly recommend to use `yolov8-worldv2` model for custom training, because it supports deterministic training and also easy to export other formats i.e onnx/tensorrt.
|
||||
|
||||
Object detection is straightforward with the `train` method, as illustrated below:
|
||||
[Object detection](https://www.ultralytics.com/glossary/object-detection) is straightforward with the `train` method, as illustrated below:
|
||||
|
||||
!!! example
|
||||
|
||||
=== "Python"
|
||||
|
||||
PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLOWorld()` class to create a model instance in python:
|
||||
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLOWorld()` class to create a model instance in python:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLOWorld
|
||||
|
|
@ -197,7 +197,7 @@ Object tracking with YOLO-World model on a video/images is streamlined as follow
|
|||
|
||||
|
||||
|
||||
The YOLO-World framework allows for the dynamic specification of classes through custom prompts, empowering users to tailor the model to their specific needs **without retraining**. This feature is particularly useful for adapting the model to new domains or specific tasks that were not originally part of the training data. By setting custom prompts, users can essentially guide the model's focus towards objects of interest, enhancing the relevance and accuracy of the detection results.
|
||||
The YOLO-World framework allows for the dynamic specification of classes through custom prompts, empowering users to tailor the model to their specific needs **without retraining**. This feature is particularly useful for adapting the model to new domains or specific tasks that were not originally part of the [training data](https://www.ultralytics.com/glossary/training-data). By setting custom prompts, users can essentially guide the model's focus towards objects of interest, enhancing the relevance and accuracy of the detection results.
|
||||
|
||||
For instance, if your application only requires detecting 'person' and 'bus' objects, you can specify these classes directly:
|
||||
|
||||
|
|
@ -262,7 +262,7 @@ You can also save a model after setting custom classes. By doing this you create
|
|||
- **Efficiency**: Streamlines the detection process by focusing on relevant objects, reducing computational overhead and speeding up inference.
|
||||
- **Flexibility**: Allows for easy adaptation of the model to new or niche detection tasks without the need for extensive retraining or data collection.
|
||||
- **Simplicity**: Simplifies deployment by eliminating the need to repeatedly specify custom classes at runtime, making the model directly usable with its embedded vocabulary.
|
||||
- **Performance**: Enhances detection accuracy for specified classes by focusing the model's attention and resources on recognizing the defined objects.
|
||||
- **Performance**: Enhances detection [accuracy](https://www.ultralytics.com/glossary/accuracy) for specified classes by focusing the model's attention and resources on recognizing the defined objects.
|
||||
|
||||
This approach provides a powerful means of customizing state-of-the-art object detection models for specific tasks, making advanced AI more accessible and applicable to a broader range of practical applications.
|
||||
|
||||
|
|
@ -345,7 +345,7 @@ The YOLO-World model is an advanced, real-time object detection approach based o
|
|||
|
||||
### How does YOLO-World handle inference with custom prompts?
|
||||
|
||||
YOLO-World supports a "prompt-then-detect" strategy, which utilizes an offline vocabulary to enhance efficiency. Custom prompts like captions or specific object categories are pre-encoded and stored as offline vocabulary embeddings. This approach streamlines the detection process without the need for retraining. You can dynamically set these prompts within the model to tailor it to specific detection tasks, as shown below:
|
||||
YOLO-World supports a "prompt-then-detect" strategy, which utilizes an offline vocabulary to enhance efficiency. Custom prompts like captions or specific object categories are pre-encoded and stored as offline vocabulary [embeddings](https://www.ultralytics.com/glossary/embeddings). This approach streamlines the detection process without the need for retraining. You can dynamically set these prompts within the model to tailor it to specific detection tasks, as shown below:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLOWorld
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ description: Discover YOLOv10, the latest in real-time object detection, elimina
|
|||
keywords: YOLOv10, real-time object detection, NMS-free, deep learning, Tsinghua University, Ultralytics, machine learning, neural networks, performance optimization
|
||||
---
|
||||
|
||||
# YOLOv10: Real-Time End-to-End Object Detection
|
||||
# YOLOv10: Real-Time End-to-End [Object Detection](https://www.ultralytics.com/glossary/object-detection)
|
||||
|
||||
YOLOv10, built on the [Ultralytics](https://www.ultralytics.com/) [Python package](https://pypi.org/project/ultralytics/) by researchers at [Tsinghua University](https://www.tsinghua.edu.cn/en/), introduces a new approach to real-time object detection, addressing both the post-processing and model architecture deficiencies found in previous YOLO versions. By eliminating non-maximum suppression (NMS) and optimizing various model components, YOLOv10 achieves state-of-the-art performance with significantly reduced computational overhead. Extensive experiments demonstrate its superior accuracy-latency trade-offs across multiple model scales.
|
||||
|
||||
|
|
@ -29,7 +29,7 @@ Real-time object detection aims to accurately predict object categories and posi
|
|||
|
||||
The architecture of YOLOv10 builds upon the strengths of previous YOLO models while introducing several key innovations. The model architecture consists of the following components:
|
||||
|
||||
1. **Backbone**: Responsible for feature extraction, the backbone in YOLOv10 uses an enhanced version of CSPNet (Cross Stage Partial Network) to improve gradient flow and reduce computational redundancy.
|
||||
1. **Backbone**: Responsible for [feature extraction](https://www.ultralytics.com/glossary/feature-extraction), the backbone in YOLOv10 uses an enhanced version of CSPNet (Cross Stage Partial Network) to improve gradient flow and reduce computational redundancy.
|
||||
2. **Neck**: The neck is designed to aggregate features from different scales and passes them to the head. It includes PAN (Path Aggregation Network) layers for effective multiscale feature fusion.
|
||||
3. **One-to-Many Head**: Generates multiple predictions per object during training to provide rich supervisory signals and improve learning accuracy.
|
||||
4. **One-to-One Head**: Generates a single best prediction per object during inference to eliminate the need for NMS, thereby reducing latency and improving efficiency.
|
||||
|
|
@ -72,7 +72,7 @@ Latency measured with TensorRT FP16 on T4 GPU.
|
|||
|
||||
YOLOv10 employs dual label assignments, combining one-to-many and one-to-one strategies during training to ensure rich supervision and efficient end-to-end deployment. The consistent matching metric aligns the supervision between both strategies, enhancing the quality of predictions during inference.
|
||||
|
||||
### Holistic Efficiency-Accuracy Driven Model Design
|
||||
### Holistic Efficiency-[Accuracy](https://www.ultralytics.com/glossary/accuracy) Driven Model Design
|
||||
|
||||
#### Efficiency Enhancements
|
||||
|
||||
|
|
@ -202,20 +202,20 @@ The YOLOv10 models series offers a range of models, each optimized for high-perf
|
|||
|
||||
Due to the new operations introduced with YOLOv10, not all export formats provided by Ultralytics are currently supported. The following table outlines which formats have been successfully converted using Ultralytics for YOLOv10. Feel free to open a pull request if you're able to [provide a contribution change](../help/contributing.md) for adding export support of additional formats for YOLOv10.
|
||||
|
||||
| Export Format | Export Support | Exported Model Inference | Notes |
|
||||
| ------------------------------------------------- | -------------- | ------------------------ | ------------------------------------------- |
|
||||
| [TorchScript](../integrations/torchscript.md) | ✅ | ✅ | Standard PyTorch model format. |
|
||||
| [ONNX](../integrations/onnx.md) | ✅ | ✅ | Widely supported for deployment. |
|
||||
| [OpenVINO](../integrations/openvino.md) | ✅ | ✅ | Optimized for Intel hardware. |
|
||||
| [TensorRT](../integrations/tensorrt.md) | ✅ | ✅ | Optimized for NVIDIA GPUs. |
|
||||
| [CoreML](../integrations/coreml.md) | ✅ | ✅ | Limited to Apple devices. |
|
||||
| [TF SavedModel](../integrations/tf-savedmodel.md) | ✅ | ✅ | TensorFlow's standard model format. |
|
||||
| [TF GraphDef](../integrations/tf-graphdef.md) | ✅ | ✅ | Legacy TensorFlow format. |
|
||||
| [TF Lite](../integrations/tflite.md) | ✅ | ✅ | Optimized for mobile and embedded. |
|
||||
| [TF Edge TPU](../integrations/edge-tpu.md) | ✅ | ✅ | Specific to Google's Edge TPU devices. |
|
||||
| [TF.js](../integrations/tfjs.md) | ✅ | ✅ | JavaScript environment for browser use. |
|
||||
| [PaddlePaddle](../integrations/paddlepaddle.md) | ❌ | ❌ | Popular in China; less global support. |
|
||||
| [NCNN](../integrations/ncnn.md) | ✅ | ❌ | Layer `torch.topk` not exists or registered |
|
||||
| Export Format | Export Support | Exported Model Inference | Notes |
|
||||
| ------------------------------------------------- | -------------- | ------------------------ | -------------------------------------------------------------------------------------- |
|
||||
| [TorchScript](../integrations/torchscript.md) | ✅ | ✅ | Standard [PyTorch](https://www.ultralytics.com/glossary/pytorch) model format. |
|
||||
| [ONNX](../integrations/onnx.md) | ✅ | ✅ | Widely supported for deployment. |
|
||||
| [OpenVINO](../integrations/openvino.md) | ✅ | ✅ | Optimized for Intel hardware. |
|
||||
| [TensorRT](../integrations/tensorrt.md) | ✅ | ✅ | Optimized for NVIDIA GPUs. |
|
||||
| [CoreML](../integrations/coreml.md) | ✅ | ✅ | Limited to Apple devices. |
|
||||
| [TF SavedModel](../integrations/tf-savedmodel.md) | ✅ | ✅ | [TensorFlow](https://www.ultralytics.com/glossary/tensorflow)'s standard model format. |
|
||||
| [TF GraphDef](../integrations/tf-graphdef.md) | ✅ | ✅ | Legacy TensorFlow format. |
|
||||
| [TF Lite](../integrations/tflite.md) | ✅ | ✅ | Optimized for mobile and embedded. |
|
||||
| [TF Edge TPU](../integrations/edge-tpu.md) | ✅ | ✅ | Specific to Google's Edge TPU devices. |
|
||||
| [TF.js](../integrations/tfjs.md) | ✅ | ✅ | JavaScript environment for browser use. |
|
||||
| [PaddlePaddle](../integrations/paddlepaddle.md) | ❌ | ❌ | Popular in China; less global support. |
|
||||
| [NCNN](../integrations/ncnn.md) | ✅ | ❌ | Layer `torch.topk` not exists or registered |
|
||||
|
||||
## Conclusion
|
||||
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ This document presents an overview of three closely related object detection mod
|
|||
|
||||
## 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:** 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](https://www.ultralytics.com/glossary/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.
|
||||
|
||||
|
|
@ -30,7 +30,7 @@ This document presents an overview of three closely related object detection mod
|
|||
|
||||
The YOLOv3 series, including YOLOv3, YOLOv3-Ultralytics, and YOLOv3u, are designed specifically for object detection tasks. These models are renowned for their effectiveness in various real-world scenarios, balancing accuracy and speed. Each variant offers unique features and optimizations, making them suitable for a range of applications.
|
||||
|
||||
All three models support a comprehensive set of modes, ensuring versatility in various stages of model deployment and development. These modes include [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), providing users with a complete toolkit for effective object detection.
|
||||
All three models support a comprehensive set of modes, ensuring versatility in various stages of [model deployment](https://www.ultralytics.com/glossary/model-deployment) and development. These modes include [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), providing users with a complete toolkit for effective object detection.
|
||||
|
||||
| Model Type | Tasks Supported | Inference | Validation | Training | Export |
|
||||
| ------------------ | -------------------------------------- | --------- | ---------- | -------- | ------ |
|
||||
|
|
@ -48,7 +48,7 @@ This example provides simple YOLOv3 training and inference examples. For full do
|
|||
|
||||
=== "Python"
|
||||
|
||||
PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
|
||||
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
|
@ -101,7 +101,7 @@ Thank you to Joseph Redmon and Ali Farhadi for developing the original YOLOv3.
|
|||
|
||||
### What are the differences between YOLOv3, YOLOv3-Ultralytics, and YOLOv3u?
|
||||
|
||||
YOLOv3 is the third iteration of the YOLO (You Only Look Once) object detection algorithm developed by Joseph Redmon, known for its balance of accuracy and speed, utilizing three different scales (13x13, 26x26, and 52x52) for detections. YOLOv3-Ultralytics is Ultralytics' adaptation of YOLOv3 that adds support for more pre-trained models and facilitates easier model customization. YOLOv3u is an upgraded variant of YOLOv3-Ultralytics, integrating the anchor-free, objectness-free split head from YOLOv8, improving detection robustness and accuracy for various object sizes. For more details on the variants, refer to the [YOLOv3 series](https://github.com/ultralytics/yolov3).
|
||||
YOLOv3 is the third iteration of the YOLO (You Only Look Once) [object detection](https://www.ultralytics.com/glossary/object-detection) algorithm developed by Joseph Redmon, known for its balance of [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed, utilizing three different scales (13x13, 26x26, and 52x52) for detections. YOLOv3-Ultralytics is Ultralytics' adaptation of YOLOv3 that adds support for more pre-trained models and facilitates easier model customization. YOLOv3u is an upgraded variant of YOLOv3-Ultralytics, integrating the anchor-free, objectness-free split head from YOLOv8, improving detection robustness and accuracy for various object sizes. For more details on the variants, refer to the [YOLOv3 series](https://github.com/ultralytics/yolov3).
|
||||
|
||||
### How can I train a YOLOv3 model using Ultralytics?
|
||||
|
||||
|
|
|
|||
|
|
@ -12,11 +12,11 @@ Welcome to the Ultralytics documentation page for YOLOv4, a state-of-the-art, re
|
|||
|
||||
## Introduction
|
||||
|
||||
YOLOv4 stands for You Only Look Once version 4. It is a real-time object detection model developed to address the limitations of previous YOLO versions like [YOLOv3](yolov3.md) and other object detection models. Unlike other convolutional neural network (CNN) based object detectors, YOLOv4 is not only applicable for recommendation systems but also for standalone process management and human input reduction. Its operation on conventional graphics processing units (GPUs) allows for mass usage at an affordable price, and it is designed to work in real-time on a conventional GPU while requiring only one such GPU for training.
|
||||
YOLOv4 stands for You Only Look Once version 4. It is a real-time object detection model developed to address the limitations of previous YOLO versions like [YOLOv3](yolov3.md) and other object detection models. Unlike other [convolutional neural network](https://www.ultralytics.com/glossary/convolutional-neural-network-cnn) (CNN) based object detectors, YOLOv4 is not only applicable for recommendation systems but also for standalone process management and human input reduction. Its operation on conventional graphics processing units (GPUs) allows for mass usage at an affordable price, and it is designed to work in real-time on a conventional GPU while requiring only one such GPU for training.
|
||||
|
||||
## Architecture
|
||||
|
||||
YOLOv4 makes use of several innovative features that work together to optimize its performance. These include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT), Mish-activation, Mosaic data augmentation, DropBlock regularization, and CIoU loss. These features are combined to achieve state-of-the-art results.
|
||||
YOLOv4 makes use of several innovative features that work together to optimize its performance. These include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT), Mish-activation, Mosaic [data augmentation](https://www.ultralytics.com/glossary/data-augmentation), DropBlock [regularization](https://www.ultralytics.com/glossary/regularization), and CIoU loss. These features are combined to achieve state-of-the-art results.
|
||||
|
||||
A typical object detector is composed of several parts including the input, the backbone, the neck, and the head. The backbone of YOLOv4 is pre-trained on ImageNet and is used to predict classes and bounding boxes of objects. The backbone could be from several models including VGG, ResNet, ResNeXt, or DenseNet. The neck part of the detector is used to collect feature maps from different stages and usually includes several bottom-up paths and several top-down paths. The head part is what is used to make the final object detections and classifications.
|
||||
|
||||
|
|
@ -71,9 +71,9 @@ The original YOLOv4 paper can be found on [arXiv](https://arxiv.org/abs/2004.109
|
|||
|
||||
## FAQ
|
||||
|
||||
### What is YOLOv4 and why should I use it for object detection?
|
||||
### What is YOLOv4 and why should I use it for [object detection](https://www.ultralytics.com/glossary/object-detection)?
|
||||
|
||||
YOLOv4, which stands for "You Only Look Once version 4," is a state-of-the-art real-time object detection model developed by Alexey Bochkovskiy in 2020. It achieves an optimal balance between speed and accuracy, making it highly suitable for real-time applications. YOLOv4's architecture incorporates several innovative features like Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), and Self-adversarial-training (SAT), among others, to achieve state-of-the-art results. If you're looking for a high-performance model that operates efficiently on conventional GPUs, YOLOv4 is an excellent choice.
|
||||
YOLOv4, which stands for "You Only Look Once version 4," is a state-of-the-art real-time object detection model developed by Alexey Bochkovskiy in 2020. It achieves an optimal balance between speed and [accuracy](https://www.ultralytics.com/glossary/accuracy), making it highly suitable for real-time applications. YOLOv4's architecture incorporates several innovative features like Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), and Self-adversarial-training (SAT), among others, to achieve state-of-the-art results. If you're looking for a high-performance model that operates efficiently on conventional GPUs, YOLOv4 is an excellent choice.
|
||||
|
||||
### How does the architecture of YOLOv4 enhance its performance?
|
||||
|
||||
|
|
@ -85,7 +85,7 @@ The architecture of YOLOv4 includes several key components: the backbone, the ne
|
|||
|
||||
### Why is YOLOv4 considered suitable for real-time object detection on conventional GPUs?
|
||||
|
||||
YOLOv4 is designed to optimize both speed and accuracy, making it ideal for real-time object detection tasks that require quick and reliable performance. It operates efficiently on conventional GPUs, needing only one for both training and inference. This makes it accessible and practical for various applications ranging from recommendation systems to standalone process management, thereby reducing the need for extensive hardware setups and making it a cost-effective solution for real-time object detection.
|
||||
YOLOv4 is designed to optimize both speed and accuracy, making it ideal for real-time object detection tasks that require quick and reliable performance. It operates efficiently on conventional GPUs, needing only one for both training and inference. This makes it accessible and practical for various applications ranging from [recommendation systems](https://www.ultralytics.com/glossary/recommendation-system) to standalone process management, thereby reducing the need for extensive hardware setups and making it a cost-effective solution for real-time object detection.
|
||||
|
||||
### How can I get started with YOLOv4 if Ultralytics does not currently support it?
|
||||
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ keywords: YOLOv5, YOLOv5u, object detection, Ultralytics, anchor-free, pre-train
|
|||
|
||||
## Overview
|
||||
|
||||
YOLOv5u represents an advancement in object detection methodologies. Originating from the foundational architecture of the [YOLOv5](https://github.com/ultralytics/yolov5) model developed by Ultralytics, YOLOv5u integrates the anchor-free, objectness-free split head, a feature previously introduced in the [YOLOv8](yolov8.md) models. This adaptation refines the model's architecture, leading to an improved accuracy-speed tradeoff in object detection tasks. Given the empirical results and its derived features, YOLOv5u provides an efficient alternative for those seeking robust solutions in both research and practical applications.
|
||||
YOLOv5u represents an advancement in [object detection](https://www.ultralytics.com/glossary/object-detection) methodologies. Originating from the foundational architecture of the [YOLOv5](https://github.com/ultralytics/yolov5) model developed by Ultralytics, YOLOv5u integrates the anchor-free, objectness-free split head, a feature previously introduced in the [YOLOv8](yolov8.md) models. This adaptation refines the model's architecture, leading to an improved accuracy-speed tradeoff in object detection tasks. Given the empirical results and its derived features, YOLOv5u provides an efficient alternative for those seeking robust solutions in both research and practical applications.
|
||||
|
||||
|
||||
|
||||
|
|
@ -60,7 +60,7 @@ This example provides simple YOLOv5 training and inference examples. For full do
|
|||
|
||||
=== "Python"
|
||||
|
||||
PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
|
||||
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
|
@ -117,7 +117,7 @@ Please note that YOLOv5 models are provided under [AGPL-3.0](https://github.com/
|
|||
|
||||
### What is Ultralytics YOLOv5u and how does it differ from YOLOv5?
|
||||
|
||||
Ultralytics YOLOv5u is an advanced version of YOLOv5, integrating the anchor-free, objectness-free split head that enhances the accuracy-speed tradeoff for real-time object detection tasks. Unlike the traditional YOLOv5, YOLOv5u adopts an anchor-free detection mechanism, making it more flexible and adaptive in diverse scenarios. For more detailed information on its features, you can refer to the [YOLOv5 Overview](#overview).
|
||||
Ultralytics YOLOv5u is an advanced version of YOLOv5, integrating the anchor-free, objectness-free split head that enhances the [accuracy](https://www.ultralytics.com/glossary/accuracy)-speed tradeoff for real-time object detection tasks. Unlike the traditional YOLOv5, YOLOv5u adopts an anchor-free detection mechanism, making it more flexible and adaptive in diverse scenarios. For more detailed information on its features, you can refer to the [YOLOv5 Overview](#overview).
|
||||
|
||||
### How does the anchor-free Ultralytics head improve object detection performance in YOLOv5u?
|
||||
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ YOLOv6 provides various pre-trained models with different scales:
|
|||
- YOLOv6-L: 52.8% AP at 116 FPS.
|
||||
- YOLOv6-L6: State-of-the-art accuracy in real-time.
|
||||
|
||||
YOLOv6 also provides quantized models for different precisions and models optimized for mobile platforms.
|
||||
YOLOv6 also provides quantized models for different [precisions](https://www.ultralytics.com/glossary/precision) and models optimized for mobile platforms.
|
||||
|
||||
## Usage Examples
|
||||
|
||||
|
|
@ -40,7 +40,7 @@ This example provides simple YOLOv6 training and inference examples. For full do
|
|||
|
||||
=== "Python"
|
||||
|
||||
PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
|
||||
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
|
@ -72,7 +72,7 @@ This example provides simple YOLOv6 training and inference examples. For full do
|
|||
|
||||
## Supported Tasks and Modes
|
||||
|
||||
The YOLOv6 series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications.
|
||||
The YOLOv6 series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and [accuracy](https://www.ultralytics.com/glossary/accuracy) requirements, making them versatile for a wide array of applications.
|
||||
|
||||
| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
|
||||
| ---------- | ------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
|
||||
|
|
@ -82,7 +82,7 @@ The YOLOv6 series offers a range of models, each optimized for high-performance
|
|||
| YOLOv6-L | `yolov6-l.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
|
||||
| YOLOv6-L6 | `yolov6-l6.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
|
||||
|
||||
This table provides a detailed overview of the YOLOv6 model variants, highlighting their capabilities in object detection tasks and their compatibility with various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv6 models in a broad range of object detection scenarios.
|
||||
This table provides a detailed overview of the YOLOv6 model variants, highlighting their capabilities in [object detection](https://www.ultralytics.com/glossary/object-detection) tasks and their compatibility with various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv6 models in a broad range of object detection scenarios.
|
||||
|
||||
## Citations and Acknowledgements
|
||||
|
||||
|
|
@ -155,7 +155,7 @@ These models are evaluated on the COCO dataset using an NVIDIA Tesla T4 GPU. For
|
|||
|
||||
### How does the Anchor-Aided Training (AAT) strategy benefit YOLOv6?
|
||||
|
||||
Anchor-Aided Training (AAT) in YOLOv6 combines elements of anchor-based and anchor-free approaches, enhancing the model's detection capabilities without compromising inference efficiency. This strategy leverages anchors during training to improve bounding box predictions, making YOLOv6 effective in diverse object detection tasks.
|
||||
Anchor-Aided Training (AAT) in YOLOv6 combines elements of anchor-based and anchor-free approaches, enhancing the model's detection capabilities without compromising inference efficiency. This strategy leverages anchors during training to improve [bounding box](https://www.ultralytics.com/glossary/bounding-box) predictions, making YOLOv6 effective in diverse object detection tasks.
|
||||
|
||||
### Which operational modes are supported by YOLOv6 models in Ultralytics?
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ keywords: YOLOv7, real-time object detection, Ultralytics, AI, computer vision,
|
|||
|
||||
# YOLOv7: Trainable Bag-of-Freebies
|
||||
|
||||
YOLOv7 is a state-of-the-art real-time object detector that surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS. It has the highest accuracy (56.8% AP) among all known real-time object detectors with 30 FPS or higher on GPU V100. Moreover, YOLOv7 outperforms other object detectors such as YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, and many others in speed and accuracy. The model is trained on the MS COCO dataset from scratch without using any other datasets or pre-trained weights. Source code for YOLOv7 is available on GitHub.
|
||||
YOLOv7 is a state-of-the-art real-time object detector that surpasses all known object detectors in both speed and [accuracy](https://www.ultralytics.com/glossary/accuracy) in the range from 5 FPS to 160 FPS. It has the highest accuracy (56.8% AP) among all known real-time object detectors with 30 FPS or higher on GPU V100. Moreover, YOLOv7 outperforms other object detectors such as YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, and many others in speed and accuracy. The model is trained on the MS COCO dataset from scratch without using any other datasets or pre-trained weights. Source code for YOLOv7 is available on GitHub.
|
||||
|
||||
|
||||
|
||||
|
|
@ -62,7 +62,7 @@ From the results in the YOLO comparison table we know that the proposed method h
|
|||
|
||||
## Overview
|
||||
|
||||
Real-time object detection is an important component in many computer vision systems, including multi-object tracking, autonomous driving, robotics, and medical image analysis. In recent years, real-time object detection development has focused on designing efficient architectures and improving the inference speed of various CPUs, GPUs, and neural processing units (NPUs). YOLOv7 supports both mobile GPU and GPU devices, from the edge to the cloud.
|
||||
Real-time object detection is an important component in many [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) systems, including multi-object tracking, autonomous driving, robotics, and medical image analysis. In recent years, real-time object detection development has focused on designing efficient architectures and improving the inference speed of various CPUs, GPUs, and neural processing units (NPUs). YOLOv7 supports both mobile GPU and GPU devices, from the edge to the cloud.
|
||||
|
||||
Unlike traditional real-time object detectors that focus on architecture optimization, YOLOv7 introduces a focus on the optimization of the training process. This includes modules and optimization methods designed to improve the accuracy of object detection without increasing the inference cost, a concept known as the "trainable bag-of-freebies".
|
||||
|
||||
|
|
@ -115,7 +115,7 @@ The original YOLOv7 paper can be found on [arXiv](https://arxiv.org/pdf/2207.026
|
|||
|
||||
## FAQ
|
||||
|
||||
### What is YOLOv7 and why is it considered a breakthrough in real-time object detection?
|
||||
### What is YOLOv7 and why is it considered a breakthrough in real-time [object detection](https://www.ultralytics.com/glossary/object-detection)?
|
||||
|
||||
YOLOv7 is a cutting-edge real-time object detection model that achieves unparalleled speed and accuracy. It surpasses other models, such as YOLOX, YOLOv5, and PPYOLOE, in both parameters usage and inference speed. YOLOv7's distinguishing features include its model re-parameterization and dynamic label assignment, which optimize its performance without increasing inference costs. For more technical details about its architecture and comparison metrics with other state-of-the-art object detectors, refer to the [YOLOv7 paper](https://arxiv.org/pdf/2207.02696.pdf).
|
||||
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ keywords: YOLOv8, real-time object detection, YOLO series, Ultralytics, computer
|
|||
|
||||
## Overview
|
||||
|
||||
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.
|
||||
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](https://www.ultralytics.com/glossary/object-detection) tasks in a wide range of applications.
|
||||
|
||||
|
||||
|
||||
|
|
@ -25,14 +25,14 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
|
|||
|
||||
## Key Features
|
||||
|
||||
- **Advanced Backbone and Neck Architectures:** YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved feature extraction and object detection performance.
|
||||
- **Advanced Backbone and Neck Architectures:** YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) and object detection performance.
|
||||
- **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.
|
||||
- **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.
|
||||
- **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.
|
||||
|
||||
## Supported Tasks and Modes
|
||||
|
||||
The YOLOv8 series offers a diverse range of models, each specialized for specific tasks in computer vision. These models are designed to cater to various requirements, from object detection to more complex tasks like instance segmentation, pose/keypoints detection, oriented object detection, and classification.
|
||||
The YOLOv8 series offers a diverse range of models, each specialized for specific tasks in computer vision. These models are designed to cater to various requirements, from object detection to more complex tasks like [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose/keypoints detection, oriented object detection, and classification.
|
||||
|
||||
Each variant of the YOLOv8 series is optimized for its respective task, ensuring high performance and accuracy. Additionally, these models are compatible with various operational modes including [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), facilitating their use in different stages of deployment and development.
|
||||
|
||||
|
|
@ -44,7 +44,7 @@ Each variant of the YOLOv8 series is optimized for its respective task, ensuring
|
|||
| YOLOv8-obb | `yolov8n-obb.pt` `yolov8s-obb.pt` `yolov8m-obb.pt` `yolov8l-obb.pt` `yolov8x-obb.pt` | [Oriented Detection](../tasks/obb.md) | ✅ | ✅ | ✅ | ✅ |
|
||||
| YOLOv8-cls | `yolov8n-cls.pt` `yolov8s-cls.pt` `yolov8m-cls.pt` `yolov8l-cls.pt` `yolov8x-cls.pt` | [Classification](../tasks/classify.md) | ✅ | ✅ | ✅ | ✅ |
|
||||
|
||||
This table provides an overview of the YOLOv8 model variants, highlighting their applicability in specific tasks and their compatibility with various operational modes such as Inference, Validation, Training, and Export. It showcases the versatility and robustness of the YOLOv8 series, making them suitable for a variety of applications in computer vision.
|
||||
This table provides an overview of the YOLOv8 model variants, highlighting their applicability in specific tasks and their compatibility with various operational modes such as Inference, Validation, Training, and Export. It showcases the versatility and robustness of the YOLOv8 series, making them suitable for a variety of applications in [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
|
||||
|
||||
## Performance Metrics
|
||||
|
||||
|
|
@ -133,7 +133,7 @@ Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for obj
|
|||
|
||||
=== "Python"
|
||||
|
||||
PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
|
||||
[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
|
||||
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
|
@ -189,7 +189,7 @@ Please note that the DOI is pending and will be added to the citation once it is
|
|||
|
||||
### What is YOLOv8 and how does it differ from previous YOLO versions?
|
||||
|
||||
YOLOv8 is the latest iteration in the Ultralytics YOLO series, designed to improve real-time object detection performance with advanced features. Unlike earlier versions, YOLOv8 incorporates an **anchor-free split Ultralytics head**, state-of-the-art backbone and neck architectures, and offers optimized accuracy-speed tradeoff, making it ideal for diverse applications. For more details, check the [Overview](#overview) and [Key Features](#key-features) sections.
|
||||
YOLOv8 is the latest iteration in the Ultralytics YOLO series, designed to improve real-time object detection performance with advanced features. Unlike earlier versions, YOLOv8 incorporates an **anchor-free split Ultralytics head**, state-of-the-art backbone and neck architectures, and offers optimized [accuracy](https://www.ultralytics.com/glossary/accuracy)-speed tradeoff, making it ideal for diverse applications. For more details, check the [Overview](#overview) and [Key Features](#key-features) sections.
|
||||
|
||||
### How can I use YOLOv8 for different computer vision tasks?
|
||||
|
||||
|
|
@ -201,7 +201,7 @@ YOLOv8 models achieve state-of-the-art performance across various benchmarking d
|
|||
|
||||
### How do I train a YOLOv8 model?
|
||||
|
||||
Training a YOLOv8 model can be done using either Python or CLI. Below are examples for training a model using a COCO-pretrained YOLOv8 model on the COCO8 dataset for 100 epochs:
|
||||
Training a YOLOv8 model can be done using either Python or CLI. Below are examples for training a model using a COCO-pretrained YOLOv8 model on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch):
|
||||
|
||||
!!! example
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ description: Explore YOLOv9, the latest leap in real-time object detection, feat
|
|||
keywords: YOLOv9, object detection, real-time, PGI, GELAN, deep learning, MS COCO, AI, neural networks, model efficiency, accuracy, Ultralytics
|
||||
---
|
||||
|
||||
# YOLOv9: A Leap Forward in Object Detection Technology
|
||||
# YOLOv9: A Leap Forward in [Object Detection](https://www.ultralytics.com/glossary/object-detection) Technology
|
||||
|
||||
YOLOv9 marks a significant advancement in real-time object detection, introducing groundbreaking techniques such as Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN). This model demonstrates remarkable improvements in efficiency, accuracy, and adaptability, setting new benchmarks on the MS COCO dataset. The YOLOv9 project, while developed by a separate open-source team, builds upon the robust codebase provided by [Ultralytics](https://www.ultralytics.com/) [YOLOv5](yolov5.md), showcasing the collaborative spirit of the AI research community.
|
||||
|
||||
|
|
@ -23,7 +23,7 @@ YOLOv9 marks a significant advancement in real-time object detection, introducin
|
|||
|
||||
## Introduction to YOLOv9
|
||||
|
||||
In the quest for optimal real-time object detection, YOLOv9 stands out with its innovative approach to overcoming information loss challenges inherent in deep neural networks. By integrating PGI and the versatile GELAN architecture, YOLOv9 not only enhances the model's learning capacity but also ensures the retention of crucial information throughout the detection process, thereby achieving exceptional accuracy and performance.
|
||||
In the quest for optimal real-time object detection, YOLOv9 stands out with its innovative approach to overcoming information loss challenges inherent in deep [neural networks](https://www.ultralytics.com/glossary/neural-network-nn). By integrating PGI and the versatile GELAN architecture, YOLOv9 not only enhances the model's learning capacity but also ensures the retention of crucial information throughout the detection process, thereby achieving exceptional accuracy and performance.
|
||||
|
||||
## Core Innovations of YOLOv9
|
||||
|
||||
|
|
@ -47,7 +47,7 @@ The concept of Reversible Functions is another cornerstone of YOLOv9's design. A
|
|||
X = v_zeta(r_psi(X))
|
||||
```
|
||||
|
||||
with `psi` and `zeta` as parameters for the reversible and its inverse function, respectively. This property is crucial for deep learning architectures, as it allows the network to retain a complete information flow, thereby enabling more accurate updates to the model's parameters. YOLOv9 incorporates reversible functions within its architecture to mitigate the risk of information degradation, especially in deeper layers, ensuring the preservation of critical data for object detection tasks.
|
||||
with `psi` and `zeta` as parameters for the reversible and its inverse function, respectively. This property is crucial for [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) architectures, as it allows the network to retain a complete information flow, thereby enabling more accurate updates to the model's parameters. YOLOv9 incorporates reversible functions within its architecture to mitigate the risk of information degradation, especially in deeper layers, ensuring the preservation of critical data for object detection tasks.
|
||||
|
||||
### Impact on Lightweight Models
|
||||
|
||||
|
|
@ -86,7 +86,7 @@ By benchmarking, you can ensure that your model not only performs well in contro
|
|||
|
||||
## Performance on MS COCO Dataset
|
||||
|
||||
The performance of YOLOv9 on the [COCO dataset](../datasets/detect/coco.md) exemplifies its significant advancements in real-time object detection, setting new benchmarks across various model sizes. Table 1 presents a comprehensive comparison of state-of-the-art real-time object detectors, illustrating YOLOv9's superior efficiency and accuracy.
|
||||
The performance of YOLOv9 on the [COCO dataset](../datasets/detect/coco.md) exemplifies its significant advancements in real-time object detection, setting new benchmarks across various model sizes. Table 1 presents a comprehensive comparison of state-of-the-art real-time object detectors, illustrating YOLOv9's superior efficiency and [accuracy](https://www.ultralytics.com/glossary/accuracy).
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**Table 1. Comparison of State-of-the-Art Real-Time Object Detectors**
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@ -109,7 +109,7 @@ The performance of YOLOv9 on the [COCO dataset](../datasets/detect/coco.md) exem
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| [YOLOv9c-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9c-seg.pt) | 640 | 52.4 | 42.2 | 27.9 | 159.4 |
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| [YOLOv9e-seg](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov9e-seg.pt) | 640 | 55.1 | 44.3 | 60.5 | 248.4 |
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YOLOv9's iterations, ranging from the tiny `t` variant to the extensive `e` model, demonstrate improvements not only in accuracy (mAP metrics) but also in efficiency with a reduced number of parameters and computational needs (FLOPs). This table underscores YOLOv9's ability to deliver high precision while maintaining or reducing the computational overhead compared to prior versions and competing models.
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YOLOv9's iterations, ranging from the tiny `t` variant to the extensive `e` model, demonstrate improvements not only in accuracy (mAP metrics) but also in efficiency with a reduced number of parameters and computational needs (FLOPs). This table underscores YOLOv9's ability to deliver high [precision](https://www.ultralytics.com/glossary/precision) while maintaining or reducing the computational overhead compared to prior versions and competing models.
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Comparatively, YOLOv9 exhibits remarkable gains:
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@ -118,7 +118,7 @@ Comparatively, YOLOv9 exhibits remarkable gains:
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The YOLOv9c model, in particular, highlights the effectiveness of the architecture's optimizations. It operates with 42% fewer parameters and 21% less computational demand than YOLOv7 AF, yet it achieves comparable accuracy, demonstrating YOLOv9's significant efficiency improvements. Furthermore, the YOLOv9e model sets a new standard for large models, with 15% fewer parameters and 25% less computational need than [YOLOv8x](yolov8.md), alongside an incremental 1.7% improvement in AP.
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These results showcase YOLOv9's strategic advancements in model design, emphasizing its enhanced efficiency without compromising on the precision essential for real-time object detection tasks. The model not only pushes the boundaries of performance metrics but also emphasizes the importance of computational efficiency, making it a pivotal development in the field of computer vision.
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These results showcase YOLOv9's strategic advancements in model design, emphasizing its enhanced efficiency without compromising on the precision essential for real-time object detection tasks. The model not only pushes the boundaries of performance metrics but also emphasizes the importance of computational efficiency, making it a pivotal development in the field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv).
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## Conclusion
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@ -132,7 +132,7 @@ This example provides simple YOLOv9 training and inference examples. For full do
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=== "Python"
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PyTorch pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
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[PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python:
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```python
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from ultralytics import YOLO
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@ -235,4 +235,4 @@ YOLOv9 is designed to mitigate information loss, which is particularly important
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### What tasks and modes does YOLOv9 support?
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YOLOv9 supports various tasks including object detection and instance segmentation. It is compatible with multiple operational modes such as inference, validation, training, and export. This versatility makes YOLOv9 adaptable to diverse real-time computer vision applications. Refer to the [supported tasks and modes](#supported-tasks-and-modes) section for more information.
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YOLOv9 supports various tasks including object detection and [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation). It is compatible with multiple operational modes such as inference, validation, training, and export. This versatility makes YOLOv9 adaptable to diverse real-time computer vision applications. Refer to the [supported tasks and modes](#supported-tasks-and-modes) section for more information.
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