Update docs metadata (#3781)
This commit is contained in:
Glenn Jocher
GitHub
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore the Fast Segment Anything Model (FastSAM), a real-time solution for the segment anything task that leverages a Convolutional Neural Network (CNN) for segmenting any object within an image, guided by user interaction prompts.
|
||||
keywords: FastSAM, Segment Anything Model, SAM, Convolutional Neural Network, CNN, image segmentation, real-time image processing
|
||||
description: Explore FastSAM, a CNN-based solution for real-time object segmentation in images. Enhanced user interaction, computational efficiency and adaptable across vision tasks.
|
||||
keywords: FastSAM, machine learning, CNN-based solution, object segmentation, real-time solution, Ultralytics, vision tasks, image processing, industrial applications, user interaction
|
||||
---
|
||||
|
||||
# Fast Segment Anything Model (FastSAM)
|
||||
@ -166,4 +166,4 @@ We would like to acknowledge the FastSAM authors for their significant contribut
|
||||
}
|
||||
```
|
||||
|
||||
The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12156). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/CASIA-IVA-Lab/FastSAM). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
|
||||
The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12156). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/CASIA-IVA-Lab/FastSAM). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: Learn about the supported models and architectures, such as YOLOv3, YOLOv5, and YOLOv8, and how to contribute your own model to Ultralytics.
|
||||
keywords: Ultralytics YOLO, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, SAM, YOLO-NAS, RT-DETR, object detection, instance segmentation, detection transformers, real-time detection, computer vision, CLI, Python
|
||||
description: Learn about the YOLO family, SAM, MobileSAM, FastSAM, YOLO-NAS, and RT-DETR models supported by Ultralytics, with examples on how to use them via CLI and Python.
|
||||
keywords: Ultralytics, documentation, YOLO, SAM, MobileSAM, FastSAM, YOLO-NAS, RT-DETR, models, architectures, Python, CLI
|
||||
---
|
||||
|
||||
# Models
|
||||
@ -45,4 +45,4 @@ model.info() # display model information
|
||||
model.train(data="coco128.yaml", epochs=100) # train the model
|
||||
```
|
||||
|
||||
For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.
|
||||
For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: MobileSAM is a lightweight adaptation of the Segment Anything Model (SAM) designed for mobile applications. It maintains the full functionality of the original SAM while significantly improving speed, making it suitable for CPU-only edge devices, such as mobile phones.
|
||||
keywords: MobileSAM, Faster Segment Anything, Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI
|
||||
description: Learn more about MobileSAM, its implementation, comparison with the original SAM, and how to download and test it in the Ultralytics framework. Improve your mobile applications today.
|
||||
keywords: MobileSAM, Ultralytics, SAM, mobile applications, Arxiv, GPU, API, image encoder, mask decoder, model download, testing method
|
||||
---
|
||||
|
||||
|
||||
@ -96,4 +96,4 @@ If you find MobileSAM useful in your research or development work, please consid
|
||||
journal={arXiv preprint arXiv:2306.14289},
|
||||
year={2023}
|
||||
}
|
||||
```
|
||||
```
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: Dive into Baidu's RT-DETR, a revolutionary real-time object detection model built on the foundation of Vision Transformers (ViT). Learn how to use pre-trained PaddlePaddle RT-DETR models with the Ultralytics Python API for various tasks.
|
||||
keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API, object detector
|
||||
description: Discover the features and benefits of RT-DETR, Baidu’s efficient and adaptable real-time object detector powered by Vision Transformers, including pre-trained models.
|
||||
keywords: RT-DETR, Baidu, Vision Transformers, object detection, real-time performance, CUDA, TensorRT, IoU-aware query selection, Ultralytics, Python API, PaddlePaddle
|
||||
---
|
||||
|
||||
# Baidu's RT-DETR: A Vision Transformer-Based Real-Time Object Detector
|
||||
@ -71,4 +71,4 @@ If you use Baidu's RT-DETR in your research or development work, please cite the
|
||||
|
||||
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.
|
||||
|
||||
*Keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API*
|
||||
*Keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API*
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover the Segment Anything Model (SAM), a revolutionary promptable image segmentation model, and delve into the details of its advanced architecture and the large-scale SA-1B dataset.
|
||||
keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI
|
||||
description: Explore the cutting-edge Segment Anything Model (SAM) from Ultralytics that allows real-time image segmentation. Learn about its promptable segmentation, zero-shot performance, and how to use it.
|
||||
keywords: Ultralytics, image segmentation, Segment Anything Model, SAM, SA-1B dataset, real-time performance, zero-shot transfer, object detection, image analysis, machine learning
|
||||
---
|
||||
|
||||
# Segment Anything Model (SAM)
|
||||
@ -218,4 +218,4 @@ If you find SAM useful in your research or development work, please consider cit
|
||||
|
||||
We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the computer vision community.
|
||||
|
||||
*keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI.*
|
||||
*keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI.*
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: Dive into YOLO-NAS, Deci's next-generation object detection model, offering breakthroughs in speed and accuracy. Learn how to utilize pre-trained models using the Ultralytics Python API for various tasks.
|
||||
keywords: YOLO-NAS, Deci AI, Ultralytics, object detection, deep learning, neural architecture search, Python API, pre-trained models, quantization
|
||||
description: Explore detailed documentation of YOLO-NAS, a superior object detection model. Learn about its features, pre-trained models, usage with Ultralytics Python API, and more.
|
||||
keywords: YOLO-NAS, Deci AI, object detection, deep learning, neural architecture search, Ultralytics Python API, YOLO model, pre-trained models, quantization, optimization, COCO, Objects365, Roboflow 100
|
||||
---
|
||||
|
||||
# YOLO-NAS
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: YOLOv3, YOLOv3-Ultralytics and YOLOv3u by Ultralytics explained. Learn the evolution of these models and their specifications.
|
||||
keywords: YOLOv3, Ultralytics YOLOv3, YOLO v3, YOLOv3 models, object detection, models, machine learning, AI, image recognition, object recognition
|
||||
description: Get an overview of YOLOv3, YOLOv3-Ultralytics and YOLOv3u. Learn about their key features, usage, and supported tasks for object detection.
|
||||
keywords: YOLOv3, YOLOv3-Ultralytics, YOLOv3u, Object Detection, Inferencing, Training, Ultralytics
|
||||
---
|
||||
|
||||
# YOLOv3, YOLOv3-Ultralytics, and YOLOv3u
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: Explore YOLOv4, a state-of-the-art, real-time object detector. Learn about its architecture, features, and performance.
|
||||
keywords: YOLOv4, object detection, real-time, CNN, GPU, Ultralytics, documentation, YOLOv4 architecture, YOLOv4 features, YOLOv4 performance
|
||||
description: Explore our detailed guide on YOLOv4, a state-of-the-art real-time object detector. Understand its architectural highlights, innovative features, and application examples.
|
||||
keywords: ultralytics, YOLOv4, object detection, neural network, real-time detection, object detector, machine learning
|
||||
---
|
||||
|
||||
# YOLOv4: High-Speed and Precise Object Detection
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: YOLOv5 by Ultralytics explained. Discover the evolution of this model and its key specifications. Experience faster and more accurate object detection.
|
||||
keywords: YOLOv5, Ultralytics YOLOv5, YOLO v5, YOLOv5 models, YOLO, object detection, model, neural network, accuracy, speed, pre-trained weights, inference, validation, training
|
||||
description: Discover YOLOv5u, a boosted version of the YOLOv5 model featuring an improved accuracy-speed tradeoff and numerous pre-trained models for various object detection tasks.
|
||||
keywords: YOLOv5u, object detection, pre-trained models, Ultralytics, Inference, Validation, YOLOv5, YOLOv8, anchor-free, objectness-free, real-time applications, machine learning
|
||||
---
|
||||
|
||||
# YOLOv5
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover Meituan YOLOv6, a robust real-time object detector. Learn how to utilize pre-trained models with Ultralytics Python API for a variety of tasks.
|
||||
keywords: Meituan, YOLOv6, object detection, Bi-directional Concatenation (BiC), anchor-aided training (AAT), pre-trained models, high-resolution input, real-time, ultra-fast computations
|
||||
description: Explore Meituan YOLOv6, a state-of-the-art object detection model striking a balance between speed and accuracy. Dive into features, pre-trained models, and Python usage.
|
||||
keywords: Meituan YOLOv6, object detection, Ultralytics, YOLOv6 docs, Bi-directional Concatenation, Anchor-Aided Training, pretrained models, real-time applications
|
||||
---
|
||||
|
||||
# Meituan YOLOv6
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: Discover YOLOv7, a cutting-edge real-time object detector that surpasses competitors in speed and accuracy. Explore its unique trainable bag-of-freebies.
|
||||
keywords: object detection, real-time object detector, YOLOv7, MS COCO, computer vision, neural networks, AI, deep learning, deep neural networks, real-time, GPU, GitHub, arXiv
|
||||
description: Explore the YOLOv7, a real-time object detector. Understand its superior speed, impressive accuracy, and unique trainable bag-of-freebies optimization focus.
|
||||
keywords: YOLOv7, real-time object detector, state-of-the-art, Ultralytics, MS COCO dataset, model re-parameterization, dynamic label assignment, extended scaling, compound scaling
|
||||
---
|
||||
|
||||
# YOLOv7: Trainable Bag-of-Freebies
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
---
|
||||
comments: true
|
||||
description: Learn about YOLOv8's pre-trained weights supporting detection, instance segmentation, pose, and classification tasks. Get performance details.
|
||||
keywords: YOLOv8, real-time object detection, object detection, deep learning, machine learning
|
||||
description: Explore the thrilling features of YOLOv8, the latest version of our real-time object detector! Learn how advanced architectures, pre-trained models and optimal balance between accuracy & speed make YOLOv8 the perfect choice for your object detection tasks.
|
||||
keywords: YOLOv8, Ultralytics, real-time object detector, pre-trained models, documentation, object detection, YOLO series, advanced architectures, accuracy, speed
|
||||
---
|
||||
|
||||
# YOLOv8
|
||||
|
||||
Reference in New Issue
Block a user