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/guides/model-training-tips/).
Join the Ultralytics community where it suits you best. For real-time chat, head to [Discord](https://ultralytics.com/discord) 🎧. Prefer in-depth discussions? Check out [Discourse](https://community.ultralytics.com). Or dive into threads on our [Subreddit](https://reddit.com/r/ultralytics) to share knowledge with the community.
Join the Ultralytics community where it suits you best. For real-time chat, head to [Discord](https://discord.com/invite/ultralytics) 🎧. Prefer in-depth discussions? Check out [Discourse](https://community.ultralytics.com). Or dive into threads on our [Subreddit](https://reddit.com/r/Ultralytics) to share knowledge with the community.
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for examples.
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, e.g. `imgsz=640`. See the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for examples.
### Python
@ -117,11 +117,13 @@ See YOLO [Python Docs](https://docs.ultralytics.com/usage/python/) for more exam
## <divalign="center">Models</div>
YOLO11 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLO11 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models.
YOLO11 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLO11 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models. All [Models](https://docs.ultralytics.com/models/) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
@ -212,9 +214,9 @@ See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with
Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [W&B](https://docs.wandb.ai/guides/integrations/ultralytics/), [Comet](https://bit.ly/yolov8-readme-comet), [Roboflow](https://roboflow.com/?ref=ultralytics) and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.
@ -10,6 +10,17 @@ keywords: Model Evaluation, Machine Learning Model Evaluation, Fine Tuning Machi
Once you've [trained](./model-training-tips.md) your computer vision model, evaluating and refining it to perform optimally is essential. Just training your model isn't enough. You need to make sure that your model is accurate, efficient, and fulfills the [objective](./defining-project-goals.md) of your computer vision project. By evaluating and fine-tuning your model, you can identify weaknesses, improve its accuracy, and boost overall performance.
<strong>Watch:</strong> Insights into Model Evaluation and Fine-Tuning | Tips for Improving Mean Average Precision
</p>
In this guide, we'll share insights on model evaluation and fine-tuning that'll make this [step of a computer vision project](./steps-of-a-cv-project.md) more approachable. We'll discuss how to understand evaluation metrics and implement fine-tuning techniques, giving you the knowledge to elevate your model's capabilities.
- [GitHub Actions CI Tests](#github-actions-ci-tests)
3. [Reporting Bugs](#reporting-bugs)
4. [License](#license)
5. [Conclusion](#conclusion)
6. [FAQ](#faq)
## Code of Conduct
To ensure a welcoming and inclusive environment for everyone, all contributors must adhere to our [Code of Conduct](https://docs.ultralytics.com/help/code_of_conduct/). Respect, kindness, and professionalism are at the heart of our community.
@ -131,6 +119,118 @@ Ultralytics uses the [GNU Affero General Public License v3.0 (AGPL-3.0)](https:/
We encourage all contributors to familiarize themselves with the terms of the AGPL-3.0 license to contribute effectively and ethically to the Ultralytics open-source community.
## Open-Sourcing Your Projects with YOLO and AGPL-3.0 Compliance
If you're planning to develop and release your own project using YOLO models, the [GNU Affero General Public License v3.0 (AGPL-3.0)](https://www.gnu.org/licenses/agpl-3.0.html) ensures that all derivative works remain open and accessible. This section provides guidance, including steps, best practices, and requirements, to help you open-source your project while complying with AGPL-3.0.
### Options for Starting Your Project
You can kick-start your project using one of these approaches:
1. **Fork the Ultralytics YOLO Repository**
Fork the official Ultralytics YOLO repository directly from [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics).
- Use this option if you plan to build directly on the latest YOLO implementation.
- Modify the forked code as needed while ensuring compliance with AGPL-3.0.
2. **Start from the Ultralytics Template Repository**
Use the Ultralytics template repository available at [https://github.com/ultralytics/template](https://github.com/ultralytics/template).
- Ideal for starting a clean, modular project with pre-configured best practices.
- This option provides a lightweight starting point for projects that integrate or extend YOLO models.
### What You Need to Open-Source
To comply with AGPL-3.0, you must make the following components of your project openly available:
1. **Your Entire Project Source Code**:
- Include all code for the larger project containing your YOLO models, scripts, and utilities.
2. **Model Weights** (if modified):
- Share any fine-tuned or modified model weights as part of the open-source project.
3. **Configuration Files**:
- Provide configuration files such as `.yaml` or `.json` that define the training setup, hyperparameters, or deployment configurations.
4. **Training Data (if redistributable)**:
- If you include preprocessed or generated data that is redistributable, ensure it is part of the repository or clearly linked.
5. **Web Application Components**:
- Include all backend and frontend source code if your project is a web application, especially server-side components.
6. **Documentation**:
- Include clear documentation on how to use, build, and extend your project.
7. **Build and Deployment Scripts**:
- Share scripts for setting up the environment, building the application, and deploying it, such as `Dockerfiles`, `requirements.txt`, or `Makefiles`.
8. **Testing Framework**:
- Open-source your test cases, such as unit and integration tests, to ensure reproducibility and reliability.
9. **Third-Party Modifications**:
- Provide source code for any third-party libraries you've modified.
### Steps to Open-Source Your Project
1. **Choose Your Starting Point**:
- Fork the Ultralytics YOLO repository or start from the Ultralytics template repository.
2. **Set Your License**:
- Add a `LICENSE` file containing the AGPL-3.0 text.
3. **Credit Upstream Contributions**:
- Include attribution to Ultralytics YOLO in your README. For example:
```
This project builds on [Ultralytics YOLO](https://github.com/ultralytics/ultralytics), licensed under AGPL-3.0.
```
4. **Make Your Code Public**:
- Push your entire project (including the components listed above) to a public GitHub repository.
5. **Document Your Project**:
- Write a clear `README.md` with instructions for setup, usage, and contributions.
6. **Enable Contributions**:
- Set up an issue tracker and contribution guidelines to foster collaboration.
By following these steps and ensuring you include all necessary components, you'll comply with AGPL-3.0 and contribute meaningfully to the open-source community. Let's continue fostering collaboration and innovation in computer vision together! 🚀
### Example Repository Structure
Below is an example structure for an AGPL-3.0 project. See [https://github.com/ultralytics/template](https://github.com/ultralytics/template) for details.
```
my-yolo-project/
│
├── LICENSE # AGPL-3.0 license text
├── README.md # Project overview and license information
By following this guide, you can ensure your project remains compliant with AGPL-3.0 while contributing to the open-source community. Your adherence strengthens the ethos of collaboration, transparency, and accessibility that drives the success of projects like YOLO.
## Conclusion
Thank you for your interest in contributing to [Ultralytics](https://www.ultralytics.com/) [open-source](https://github.com/ultralytics) YOLO projects. Your participation is essential in shaping the future of our software and building a vibrant community of innovation and collaboration. Whether you're enhancing code, reporting bugs, or suggesting new features, your contributions are invaluable.
@ -52,7 +52,7 @@ Now that we've covered what Albumentations is and what it can do, let's look at
### Installation
To use Albumentations with YOLOv11, start by making sure you have the necessary packages installed. If Albumentations isn't installed, the augmentations won't be applied during training. Once set up, you'll be ready to create an augmented dataset for training, with Albumentations integrated to enhance your model automatically.
To use Albumentations with YOLO11, start by making sure you have the necessary packages installed. If Albumentations isn't installed, the augmentations won't be applied during training. Once set up, you'll be ready to create an augmented dataset for training, with Albumentations integrated to enhance your model automatically.
!!! tip "Installation"
@ -67,7 +67,7 @@ For detailed instructions and best practices related to the installation process
### Usage
After installing the necessary packages, you're ready to start using Albumentations with YOLO11. When you train YOLOv11, a set of augmentations is automatically applied through its integration with Albumentations, making it easy to enhance your model's performance.
After installing the necessary packages, you're ready to start using Albumentations with YOLO11. When you train YOLO11, a set of augmentations is automatically applied through its integration with Albumentations, making it easy to enhance your model's performance.
| `seed` | `0` | Sets the random seed for training, ensuring reproducibility of results across runs with the same configurations. |
| `deterministic` | `True` | Forces deterministic algorithm use, ensuring reproducibility but may affect performance and speed due to the restriction on non-deterministic algorithms. |
| `single_cls` | `False` | Treats all classes in multi-class datasets as a single class during training. Useful for binary classification tasks or when focusing on object presence rather than classification. |
| `classes` | `None` | Specifies a list of class IDs to train on. Useful for filtering out and focusing only on certain classes during training. |
| `rect` | `False` | Enables rectangular training, optimizing batch composition for minimal padding. Can improve efficiency and speed but may affect model accuracy. |
| `cos_lr` | `False` | Utilizes a cosine [learning rate](https://www.ultralytics.com/glossary/learning-rate) scheduler, adjusting the learning rate following a cosine curve over epochs. Helps in managing learning rate for better convergence. |
| `close_mosaic` | `10` | Disables mosaic [data augmentation](https://www.ultralytics.com/glossary/data-augmentation) in the last N epochs to stabilize training before completion. Setting to 0 disables this feature. |
YOLO11 is an AI framework that supports multiple [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) **tasks**. The framework can be used to perform [detection](detect.md), [segmentation](segment.md), [obb](obb.md), [classification](classify.md), and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
@ -13,28 +13,16 @@ Pose estimation is a task that involves identifying the location of specific poi
The output of a pose estimation model is a set of points that represent the keypoints on an object in the image, usually along with the confidence scores for each point. Pose estimation is a good choice when you need to identify specific parts of an object in a scene, and their location in relation to each other.
@ -374,6 +374,91 @@ See docstring for each function or visit the `ultralytics.utils.ops` [reference
Ultralytics includes an Annotator class that can be used to annotate any kind of data. It's easiest to use with [object detection bounding boxes](../modes/predict.md#boxes), [pose key points](../modes/predict.md#keypoints), and [oriented bounding boxes](../modes/predict.md#obb).
#### Ultralytics Sweep Annotation
!!! example "Python Examples using YOLO11 🚀"
=== "Python"
```python
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
# User defined video path and model file
cap = cv2.VideoCapture("Path/to/video/file.mp4")
model = YOLO(model="yolo11s-seg.pt") # Model file i.e. yolo11s.pt or yolo11m-seg.pt
if not cap.isOpened():
print("Error: Could not open video.")
exit()
# Initialize the video writer object.
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
Burhan
3 days ago
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