If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/).
Join the vibrant [Ultralytics Discord](https://discord.gg/YVsATxj6wr) 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
Join the vibrant [Ultralytics Discord](https://ultralytics.com/discord) 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
@ -17,32 +17,51 @@ In this documentation, we provide information on four major models:
5. [YOLOv7](./yolov7.md): Updated YOLO models released in 2022 by the authors of YOLOv4.
6. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
7. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
7. [Mobile Segment Anything Model (MobileSAM)](./mobile-sam.md): MobileSAM for mobile applications by Kyung Hee University.
8. [Fast Segment Anything Model (FastSAM)](./fast-sam.md): FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
8. [Mobile Segment Anything Model (MobileSAM)](./mobile-sam.md): MobileSAM for mobile applications by Kyung Hee University.
9. [Fast Segment Anything Model (FastSAM)](./fast-sam.md): FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
You can use many of these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python:
## CLI Example
## Usage
Use the `model` argument to pass a model YAML such as `model=yolov8n.yaml` or a pretrained *.pt file such as `model=yolov8n.pt`
You can use RT-DETR for object detection tasks using the `ultralytics` pip package. The following is a sample code snippet showing how to use RT-DETR models for training and inference:
This example provides simple inference code for YOLO, SAM and RTDETR models. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using models with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
## Python Example
=== "Python"
PyTorch pretrained models as well as model YAML files can also be passed to the `YOLO()`, `SAM()`, `NAS()` and `RTDETR()` classes to create a model instance in python:
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:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt") # load a pretrained YOLOv8n model
# Load a COCO-pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
@ -15,7 +15,7 @@ Real-Time Detection Transformer (RT-DETR), developed by Baidu, is a cutting-edge
### Key Features
- **Efficient Hybrid Encoder:** Baidu's RT-DETR uses an efficient hybrid encoder that processes multi-scale 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.
- **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.
- **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.
- **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.
@ -28,15 +28,38 @@ The Ultralytics Python API provides pre-trained PaddlePaddle RT-DETR models with
## Usage
### Python API
You can use RT-DETR for object detection tasks using the `ultralytics` pip package. The following is a sample code snippet showing how to use RT-DETR models for training and inference:
!!! example ""
This example provides simple inference code for RT-DETR. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using RT-DETR with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
=== "Python"
```python
from ultralytics import RTDETR
model = RTDETR("rtdetr-l.pt")
model.info() # display model information
model.train(data="coco8.yaml") # train
model.predict("path/to/image.jpg") # predict
# Load a COCO-pretrained RT-DETR-l model
model = RTDETR('rtdetr-l.pt')
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
@ -36,35 +36,49 @@ Each model variant is designed to offer a balance between Mean Average Precision
## Usage
### Python API
Ultralytics has made YOLO-NAS models easy to integrate into your Python applications via our `ultralytics` python package. The package provides a user-friendly Python API to streamline the process.
The YOLO-NAS models are easy to integrate into your Python applications. Ultralytics provides a user-friendly Python API to streamline the process.
The following examples show how to use YOLO-NAS models with the `ultralytics` package for inference and validation:
#### Predict Usage
### Inference and Validation Examples
To perform object detection on an image, use the `predict` method as shown below:
In this example we validate YOLO-NAS-s on the COCO8 dataset.
This snippet demonstrates the simplicity of loading a pre-trained model and running a prediction on an image.
This example provides simple inference and validation code for YOLO-NAS. For handling inference results see [Predict](../modes/predict.md) mode. For using YOLO-NAS with additional modes see [Val](../modes/val.md) and [Export](../modes/export.md). YOLO-NAS on the `ultralytics` package does not support training.
#### Val Usage
=== "Python"
Validation of the model on a dataset can be done as follows:
PyTorch pretrained `*.pt` models files can be passed to the `NAS()` class to create a model instance in python:
```python
from ultralytics import NAS
model = NAS('yolo_nas_s')
results = model.val(data='coco8.yaml)
# Load a COCO-pretrained YOLO-NAS-s model
model = NAS('yolo_nas_s.pt')
# Display model information (optional)
model.info()
# Validate the model on the COCO8 example dataset
results model.val(data='coco8.yaml')
# Run inference with the YOLO-NAS-s model on the 'bus.jpg' image
results = model('path/to/bus.jpg')
```
In this example, the model is validated against the dataset specified in the 'coco8.yaml' file.
=== "CLI"
CLI commands are available to directly run the models:
```bash
# Load a COCO-pretrained YOLO-NAS-s model and validate it's performance on the COCO8 example dataset
yolo val model=yolo_nas_s.pt data=coco8.yaml
# Load a COCO-pretrained YOLO-NAS-s model and run inference on the 'bus.jpg' image
description: Get an overview of YOLOv3, YOLOv3-Ultralytics and YOLOv3u. Learn about their key features, usage, and supported tasks for object detection.
You can use these models for object detection tasks using the Ultralytics YOLOv3 repository. The following is a sample code snippet showing how to use the YOLOv3u model for inference:
You can use YOLOv3 for object detection tasks using the Ultralytics repository. The following is a sample code snippet showing how to use YOLOv3 model for inference:
!!! example ""
This example provides simple inference code for YOLOv3. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using YOLOv3 with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
=== "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:
```python
from ultralytics import YOLO
# Load the model
model = YOLO('yolov3.pt') # load a pretrained model
# Load a COCO-pretrained YOLOv3n model
model = YOLO('yolov3n.pt')
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
YOLOv5u is an enhanced version of the [YOLOv5](https://github.com/ultralytics/yolov5) object detection model from Ultralytics. This iteration incorporates the anchor-free, objectness-free split head that is featured in the [YOLOv8](./yolov8.md) models. Although it maintains the same backbone and neck architecture as YOLOv5, YOLOv5u provides an improved accuracy-speed tradeoff for object detection tasks, making it a robust choice for numerous applications.
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.
- **Anchor-free Split Ultralytics Head:**YOLOv5u replaces the conventional anchor-based detection head with an anchor-free split Ultralytics head, boosting performance in object detection tasks.
- **Anchor-free Split Ultralytics Head:**Traditional object detection models rely on predefined anchor boxes to predict object locations. However, YOLOv5u modernizes this approach. By adopting an anchor-free split Ultralytics head, it ensures a more flexible and adaptive detection mechanism, consequently enhancing the performance in diverse scenarios.
- **Optimized Accuracy-Speed Tradeoff:**By delivering a better balance between accuracy and speed, YOLOv5u is suitable for a diverse range of real-time applications, from autonomous driving to video surveillance.
- **Optimized Accuracy-Speed Tradeoff:**Speed and accuracy often pull in opposite directions. But YOLOv5u challenges this tradeoff. It offers a calibrated balance, ensuring real-time detections without compromising on accuracy. This feature is particularly invaluable for applications that demand swift responses, such as autonomous vehicles, robotics, and real-time video analytics.
- **Variety of Pre-trained Models:**YOLOv5u includes numerous pre-trained models for tasks like Inference, Validation, and Training, providing the flexibility to tackle various object detection challenges.
- **Variety of Pre-trained Models:**Understanding that different tasks require different toolsets, YOLOv5u provides a plethora of pre-trained models. Whether you're focusing on Inference, Validation, or Training, there's a tailor-made model awaiting you. This variety ensures you're not just using a one-size-fits-all solution, but a model specifically fine-tuned for your unique challenge.
## Supported Tasks
@ -38,41 +38,67 @@ YOLOv5u is an enhanced version of the [YOLOv5](https://github.com/ultralytics/yo
You can use YOLOv5u for object detection tasks using the Ultralytics repository. The following is a sample code snippet showing how to use YOLOv5u model for inference:
!!! example ""
This example provides simple inference code for YOLOv5. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using YOLOv5 with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
=== "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:
```python
from ultralytics import YOLO
# Load the model
model = YOLO('yolov5n.pt') # load a pretrained model
# Load a COCO-pretrained YOLOv5n model
model = YOLO('yolov5n.pt')
# Display model information (optional)
model.info()
# Perform inference
results = model('image.jpg')
# Train the model on the COCO8 example dataset for 100 epochs
@ -17,7 +17,7 @@ structure of a BiC module. (c) A SimCSPSPPF block. ([source](https://arxiv.org/p
### Key Features
- **Bi-directional Concatenation (BiC) Module:** YOLOv6 introduces a BiC module in the neck of the detector, enhancing localization signals and delivering performance gains with negligible speed degradation.
- **Bidirectional Concatenation (BiC) Module:** YOLOv6 introduces a BiC module in the neck of the detector, enhancing localization signals and delivering performance gains with negligible speed degradation.
- **Anchor-Aided Training (AAT) Strategy:** This model proposes AAT to enjoy the benefits of both anchor-based and anchor-free paradigms without compromising inference efficiency.
- **Enhanced Backbone and Neck Design:** By deepening YOLOv6 to include another stage in the backbone and neck, this model achieves state-of-the-art performance on the COCO dataset at high-resolution input.
- **Self-Distillation Strategy:** A new self-distillation strategy is implemented to boost the performance of smaller models of YOLOv6, enhancing the auxiliary regression branch during training and removing it at inference to avoid a marked speed decline.
@ -36,14 +36,42 @@ YOLOv6 also provides quantized models for different precisions and models optimi
## Usage
### Python API
You can use YOLOv6 for object detection tasks using the Ultralytics pip package. The following is a sample code snippet showing how to use YOLOv6 models for training:
!!! example ""
This example provides simple training code for YOLOv6. For more options including training settings see [Train](../modes/train.md) mode. For using YOLOv6 with additional modes see [Predict](../modes/predict.md), [Val](../modes/val.md) and [Export](../modes/export.md).
=== "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:
```python
from ultralytics import YOLO
model = YOLO("yolov6n.yaml") # build new model from scratch
model.info() # display model information
model.predict("path/to/image.jpg") # predict
# Build a YOLOv6n model from scratch
model = YOLO('yolov6n.yaml')
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
@ -83,23 +83,50 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
You can use YOLOv8 for object detection tasks using the Ultralytics pip package. The following is a sample code snippet showing how to use YOLOv8 models for inference:
!!! example ""
This example provides simple inference code for YOLOv8. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using YOLOv8 with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
=== "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:
```python
from ultralytics import YOLO
# Load the model
model = YOLO('yolov8n.pt') # load a pretrained model
# Load a COCO-pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
# Display model information (optional)
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
Glenn Jocher
1 year ago
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