diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 3a65188e4..e0eff35cb 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -102,21 +102,19 @@ jobs:
python-version: ["3.11"]
model: [yolo11n]
steps:
+ - uses: astral-sh/setup-uv@v3
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- cache: "pip" # caching pip dependencies
- name: Install requirements
shell: bash # for Windows compatibility
run: |
- # Warnings: uv causes numpy errors during benchmarking
- python -m pip install --upgrade pip wheel
- pip install -e ".[export]" "coverage[toml]" --extra-index-url https://download.pytorch.org/whl/cpu
+ uv pip install --system -e ".[export]" "coverage[toml]" --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-first-match
- name: Check environment
run: |
yolo checks
- pip list
+ uv pip list
- name: Benchmark DetectionModel
shell: bash
run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.309
@@ -214,7 +212,8 @@ jobs:
- uses: actions/checkout@v4
- uses: astral-sh/setup-uv@v4
- name: Install requirements
- run: uv pip install --system . pytest-cov
+ shell: bash # for Windows compatibility
+ run: uv pip install --system -e . pytest-cov
- name: Check environment
run: |
yolo checks
diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml
index 28720abfb..84f39b7eb 100644
--- a/.github/workflows/format.yml
+++ b/.github/workflows/format.yml
@@ -35,7 +35,7 @@ jobs:
If this is a custom training โ 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) ๐ง. 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) ๐ง. 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.
## Upgrade
diff --git a/README.md b/README.md
index 61dd03d37..b03e95d03 100644
--- a/README.md
+++ b/README.md
@@ -80,7 +80,7 @@ YOLO may be used directly in the Command Line Interface (CLI) with a `yolo` comm
yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
```
-`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
##
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.
+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.
+
+
+
+
+ Detection (COCO)
@@ -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.
-
-
+
+
## Dataset Structure
diff --git a/docs/en/guides/instance-segmentation-and-tracking.md b/docs/en/guides/instance-segmentation-and-tracking.md
index a910a21d8..12cd7477a 100644
--- a/docs/en/guides/instance-segmentation-and-tracking.md
+++ b/docs/en/guides/instance-segmentation-and-tracking.md
@@ -82,15 +82,11 @@ There are two types of instance segmentation tracking available in the Ultralyti
=== "Instance Segmentation with Object Tracking"
```python
- from collections import defaultdict
-
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
- track_history = defaultdict(lambda: [])
-
model = YOLO("yolo11n-seg.pt") # segmentation model
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
@@ -205,15 +201,11 @@ To implement object tracking, use the `model.track` method and ensure that each
=== "Python"
```python
- from collections import defaultdict
-
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
- track_history = defaultdict(lambda: [])
-
model = YOLO("yolo11n-seg.pt") # segmentation model
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
diff --git a/docs/en/guides/model-evaluation-insights.md b/docs/en/guides/model-evaluation-insights.md
index 24514f4a4..5bd8bedea 100644
--- a/docs/en/guides/model-evaluation-insights.md
+++ b/docs/en/guides/model-evaluation-insights.md
@@ -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.
+
+
+
+
+ Watch: Insights into Model Evaluation and Fine-Tuning | Tips for Improving Mean Average Precision
+
+
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.
## Evaluating Model Performance Using Metrics
diff --git a/docs/en/guides/raspberry-pi.md b/docs/en/guides/raspberry-pi.md
index a834d8f07..ef41d0f8c 100644
--- a/docs/en/guides/raspberry-pi.md
+++ b/docs/en/guides/raspberry-pi.md
@@ -142,9 +142,10 @@ YOLO11 benchmarks were run by the Ultralytics team on nine different model forma
We have only included benchmarks for YOLO11n and YOLO11s models because other models sizes are too big to run on the Raspberry Pis and does not offer decent performance.
-
-
- Watch: Ultralytics YOLOv8 Object Counting in Multiple & Movable Regions
+ Watch: Object Counting in Different Regions using Ultralytics YOLO11 | Ultralytics Solutions ๐
## Advantages of Object Counting in Regions?
@@ -39,44 +39,45 @@ keywords: object counting, regions, YOLOv8, computer vision, Ultralytics, effici
=== "Python"
```python
- import cv2
- from ultralytics import solutions
-
- cap = cv2.VideoCapture("Path/to/video/file.mp4")
- assert cap.isOpened(), "Error reading video file"
- w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
-
- # Define region points
- # region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)] # Pass region as list
-
- # pass region as dictionary
- region_points = {
- "region-01": [(50, 50), (250, 50), (250, 250), (50, 250)],
- "region-02": [(640, 640), (780, 640), (780, 720), (640, 720)]
- }
-
- # Video writer
- video_writer = cv2.VideoWriter("region_counting.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
-
- # Init Object Counter
- region = solutions.RegionCounter(
- show=True,
- region=region_points,
- model="yolo11n.pt",
- )
-
- # Process video
- while cap.isOpened():
- success, im0 = cap.read()
- if not success:
- print("Video frame is empty or video processing has been successfully completed.")
- break
- im0 = region.count(im0)
- video_writer.write(im0)
-
- cap.release()
- video_writer.release()
- cv2.destroyAllWindows()
+ import cv2
+
+ from ultralytics import solutions
+
+ cap = cv2.VideoCapture("Path/to/video/file.mp4")
+ assert cap.isOpened(), "Error reading video file"
+ w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
+
+ # Define region points
+ # region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)] # Pass region as list
+
+ # pass region as dictionary
+ region_points = {
+ "region-01": [(50, 50), (250, 50), (250, 250), (50, 250)],
+ "region-02": [(640, 640), (780, 640), (780, 720), (640, 720)],
+ }
+
+ # Video writer
+ video_writer = cv2.VideoWriter("region_counting.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+
+ # Init Object Counter
+ region = solutions.RegionCounter(
+ show=True,
+ region=region_points,
+ model="yolo11n.pt",
+ )
+
+ # Process video
+ while cap.isOpened():
+ success, im0 = cap.read()
+ if not success:
+ print("Video frame is empty or video processing has been successfully completed.")
+ break
+ im0 = region.count(im0)
+ video_writer.write(im0)
+
+ cap.release()
+ video_writer.release()
+ cv2.destroyAllWindows()
```
!!! tip "Ultralytics Example Code"
diff --git a/docs/en/guides/trackzone.md b/docs/en/guides/trackzone.md
new file mode 100644
index 000000000..ec9822138
--- /dev/null
+++ b/docs/en/guides/trackzone.md
@@ -0,0 +1,160 @@
+---
+comments: true
+description: Discover how TrackZone leverages Ultralytics YOLO11 to precisely track objects within specific zones, enabling real-time insights for crowd analysis, surveillance, and targeted monitoring.
+keywords: TrackZone, object tracking, YOLO11, Ultralytics, real-time object detection, AI, deep learning, crowd analysis, surveillance, zone-based tracking, resource optimization
+---
+
+# TrackZone using Ultralytics YOLO11
+
+## What is TrackZone?
+
+TrackZone specializes in monitoring objects within designated areas of a frame instead of the whole frame. Built on [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/), it integrates object detection and tracking specifically within zones for videos and live camera feeds. YOLO11's advanced algorithms and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) technologies make it a perfect choice for real-time use cases, offering precise and efficient object tracking in applications like crowd monitoring and surveillance.
+
+## Advantages of Object Tracking in Zones (TrackZone)
+
+- **Targeted Analysis:** Tracking objects within specific zones allows for more focused insights, enabling precise monitoring and analysis of areas of interest, such as entry points or restricted zones.
+- **Improved Efficiency:** By narrowing the tracking scope to defined zones, TrackZone reduces computational overhead, ensuring faster processing and optimal performance.
+- **Enhanced Security:** Zonal tracking improves surveillance by monitoring critical areas, aiding in the early detection of unusual activity or security breaches.
+- **Scalable Solutions:** The ability to focus on specific zones makes TrackZone adaptable to various scenarios, from retail spaces to industrial settings, ensuring seamless integration and scalability.
+
+## Real World Applications
+
+| Agriculture | Transportation |
+| :-----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  |  |
+| Plants Tracking in Field Using Ultralytics YOLO11 | Vehicles Tracking on Road using Ultralytics YOLO11 |
+
+!!! example "TrackZone using YOLO11 Example"
+
+ === "CLI"
+
+ ```bash
+ # Run a trackzone example
+ yolo solutions trackzone show=True
+
+ # Pass a source video
+ yolo solutions trackzone show=True source="path/to/video/file.mp4"
+
+ # Pass region coordinates
+ yolo solutions trackzone show=True region=[(150, 150), (1130, 150), (1130, 570), (150, 570)]
+ ```
+
+ === "Python"
+
+ ```python
+ import cv2
+
+ from ultralytics import solutions
+
+ cap = cv2.VideoCapture("path/to/video/file.mp4")
+ assert cap.isOpened(), "Error reading video file"
+ w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
+
+ # Define region points
+ region_points = [(150, 150), (1130, 150), (1130, 570), (150, 570)]
+
+ # Video writer
+ video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+
+ # Init TrackZone (Object Tracking in Zones, not complete frame)
+ trackzone = solutions.TrackZone(
+ show=True, # Display the output
+ region=region_points, # Pass region points
+ model="yolo11n.pt", # You can use any model that Ultralytics support, i.e. YOLOv9, YOLOv10
+ # line_width=2, # Adjust the line width for bounding boxes and text display
+ # classes=[0, 2], # If you want to count specific classes i.e. person and car with COCO pretrained model.
+ )
+
+ # Process video
+ while cap.isOpened():
+ success, im0 = cap.read()
+ if not success:
+ print("Video frame is empty or video processing has been successfully completed.")
+ break
+ im0 = trackzone.trackzone(im0)
+ video_writer.write(im0)
+
+ cap.release()
+ video_writer.release()
+ cv2.destroyAllWindows()
+ ```
+
+### Argument `TrackZone`
+
+Here's a table with the `TrackZone` arguments:
+
+| Name | Type | Default | Description |
+| ------------ | ------ | ---------------------------------------------------- | ---------------------------------------------------- |
+| `model` | `str` | `None` | Path to Ultralytics YOLO Model File |
+| `region` | `list` | `[(150, 150), (1130, 150), (1130, 570), (150, 570)]` | List of points defining the object tracking region. |
+| `line_width` | `int` | `2` | Line thickness for bounding boxes. |
+| `show` | `bool` | `False` | Flag to control whether to display the video stream. |
+
+### Arguments `model.track`
+
+{% include "macros/track-args.md" %}
+
+## FAQ
+
+### How do I track objects in a specific area or zone of a video frame using Ultralytics YOLO11?
+
+Tracking objects in a defined area or zone of a video frame is straightforward with Ultralytics YOLO11. Simply use the command provided below to initiate tracking. This approach ensures efficient analysis and accurate results, making it ideal for applications like surveillance, crowd management, or any scenario requiring zonal tracking.
+
+```bash
+yolo solutions trackzone source="path/to/video/file.mp4" show=True
+```
+
+### How can I use TrackZone in Python with Ultralytics YOLO11?
+
+With just a few lines of code, you can set up object tracking in specific zones, making it easy to integrate into your projects.
+
+```python
+import cv2
+
+from ultralytics import solutions
+
+cap = cv2.VideoCapture("path/to/video/file.mp4")
+assert cap.isOpened(), "Error reading video file"
+w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
+
+# Define region points
+region_points = [(150, 150), (1130, 150), (1130, 570), (150, 570)]
+
+# Video writer
+video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+
+# Init TrackZone (Object Tracking in Zones, not complete frame)
+trackzone = solutions.TrackZone(
+ show=True, # Display the output
+ region=region_points, # Pass region points
+ model="yolo11n.pt",
+)
+
+# Process video
+while cap.isOpened():
+ success, im0 = cap.read()
+ if not success:
+ print("Video frame is empty or video processing has been successfully completed.")
+ break
+ im0 = trackzone.trackzone(im0)
+ video_writer.write(im0)
+
+cap.release()
+video_writer.release()
+cv2.destroyAllWindows()
+```
+
+### How do I configure the zone points for video processing using Ultralytics TrackZone?
+
+Configuring zone points for video processing with Ultralytics TrackZone is simple and customizable. You can directly define and adjust the zones through a Python script, allowing precise control over the areas you want to monitor.
+
+```python
+# Define region points
+region_points = [(150, 150), (1130, 150), (1130, 570), (150, 570)]
+
+# Init TrackZone (Object Tracking in Zones, not complete frame)
+trackzone = solutions.TrackZone(
+ show=True, # Display the output
+ region=region_points, # Pass region points
+)
+```
diff --git a/docs/en/help/contributing.md b/docs/en/help/contributing.md
index 1dad4f531..c637c5936 100644
--- a/docs/en/help/contributing.md
+++ b/docs/en/help/contributing.md
@@ -11,18 +11,6 @@ Welcome! We're thrilled that you're considering contributing to our [Ultralytics
-## Table of Contents
-
-1. [Code of Conduct](#code-of-conduct)
-2. [Contributing via Pull Requests](#contributing-via-pull-requests)
- - [CLA Signing](#cla-signing)
- - [Google-Style Docstrings](#google-style-docstrings)
- - [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! ๐
+
+### 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/
+โ
+โโโ LICENSE # AGPL-3.0 license text
+โโโ README.md # Project overview and license information
+โโโ src/ # Source code for the project
+โ โโโ model.py # YOLO-based model implementation
+โ โโโ utils.py # Utility scripts
+โ โโโ ...
+โโโ pyproject.toml # Python dependencies
+โโโ tests/ # Unit and integration tests
+โโโ .github/ # GitHub Actions for CI
+โ โโโ workflows/
+โ โโโ ci.yml # Continuous integration configuration
+โโโ docs/ # Project documentation
+ โโโ index.md
+```
+
+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.
diff --git a/docs/en/integrations/albumentations.md b/docs/en/integrations/albumentations.md
index fe7b081c2..3fa4040ba 100644
--- a/docs/en/integrations/albumentations.md
+++ b/docs/en/integrations/albumentations.md
@@ -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.
!!! example "Usage"
diff --git a/docs/en/integrations/sony-imx500.md b/docs/en/integrations/sony-imx500.md
index 88338ebb6..5dccbc558 100644
--- a/docs/en/integrations/sony-imx500.md
+++ b/docs/en/integrations/sony-imx500.md
@@ -162,7 +162,7 @@ cd examples/imx500
Step 3: Run YOLOv8 object detection, using the labels.txt file that has been generated during the IMX500 export.
```bash
-python imx500_object_detection_demo.py --model --fps 25 --bbox-normalization --ignore-dash-labels --bbox-order xy โlabels
+python imx500_object_detection_demo.py --model --fps 25 --bbox-normalization --ignore-dash-labels --bbox-order xy --labels
```
Then you will be able to see live inference output as follows
diff --git a/docs/en/macros/solutions-args.md b/docs/en/macros/solutions-args.md
new file mode 100644
index 000000000..0ce5d52a0
--- /dev/null
+++ b/docs/en/macros/solutions-args.md
@@ -0,0 +1,11 @@
+| Argument | Type | Default | Description |
+| ---------------- | -------------- | -------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `region` | `list` | `[(20, 400), (1080, 400), (1080, 360), (20, 360)]` | Defines the region points for object counting, queue monitoring, trackzone or speed estimation. The points are defined as coordinates forming a polygonal area for analysis. |
+| `show_in` | `bool` | `True` | Indicates whether to display objects that are counted as entering the defined region. Essential for real-world analytics, such as monitoring ingress trends. |
+| `show_out` | `bool` | `True` | Indicates whether to display objects that are counted as exiting the defined region. Useful for applications requiring egress tracking and analytics. |
+| `colormap` | `int or tuple` | `COLORMAP_PARULA` | Specifies the OpenCV-supported colormap for heatmap visualization. Default is `COLORMAP_PARULA`, but other colormaps can be used for different visualization preferences. |
+| `up_angle` | `float` | `145.0` | Angle threshold for detecting the "up" position in workouts monitoring. Can be adjusted based on the position of keypoints for different exercises. |
+| `down_angle` | `float` | `90.0` | Angle threshold for detecting the "down" position in workouts monitoring. Adjust this based on keypoint positions for specific exercises. |
+| `kpts` | `list` | `[6, 8, 10]` | List of keypoints used for monitoring workouts. These keypoints correspond to body joints or parts, such as shoulders, elbows, and wrists, for exercises like push-ups, pull-ups, squats, ab-workouts. |
+| `analytics_type` | `str` | `line` | Specifies the type of analytics visualization to generate. Options include `"line"`, `"pie"`, `"bar"`, or `"area"`. The default is `"line"` for trend visualization. |
+| `json_file` | `str` | `None` | Path to the JSON file defining regions for parking systems or similar applications. Enables flexible configuration of analysis areas. |
diff --git a/docs/en/macros/train-args.md b/docs/en/macros/train-args.md
index 924bd3134..e9e026e64 100644
--- a/docs/en/macros/train-args.md
+++ b/docs/en/macros/train-args.md
@@ -20,6 +20,7 @@
| `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. |
diff --git a/docs/en/models/sam-2.md b/docs/en/models/sam-2.md
index 983d8cdcd..8d39b5eae 100644
--- a/docs/en/models/sam-2.md
+++ b/docs/en/models/sam-2.md
@@ -271,6 +271,17 @@ Auto-annotation is a powerful feature of SAM 2, enabling users to generate segme
### How to Auto-Annotate with SAM 2
+
+
+
+
+ Watch: Auto Annotation with Meta's Segment Anything 2 Model using Ultralytics | Data Labeling
+
+
To auto-annotate your dataset using SAM 2, follow this example:
!!! example "Auto-Annotation Example"
diff --git a/docs/en/modes/benchmark.md b/docs/en/modes/benchmark.md
index 587462dfe..149ad5a27 100644
--- a/docs/en/modes/benchmark.md
+++ b/docs/en/modes/benchmark.md
@@ -47,13 +47,13 @@ Once your model is trained and validated, the next logical step is to evaluate i
-
- Watch: Ultralytics Modes Tutorial: Benchmark
+ Watch: Benchmark Ultralytics YOLO11 Models | How to Compare Model Performance on Different Hardware?
## Why Is Benchmarking Crucial?
diff --git a/docs/en/reference/solutions/trackzone.md b/docs/en/reference/solutions/trackzone.md
new file mode 100644
index 000000000..546d61dcb
--- /dev/null
+++ b/docs/en/reference/solutions/trackzone.md
@@ -0,0 +1,16 @@
+---
+description: Discover Ultralytics' TrackZone solution for real-time object tracking within defined zones. Gain insights into initializing regions, tracking objects exclusively within specific areas, and optimizing video stream processing for region-based object detection.
+keywords: Ultralytics, TrackZone, Object Tracking, Zone Tracking, Region Tracking, Python, Real-time Object Tracking, Video Stream Processing, Region-based Detection
+---
+
+# Reference for `ultralytics/solutions/trackzone.py`
+
+!!! note
+
+ This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/trackzone.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/trackzone.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/solutions/trackzone.py) ๐ ๏ธ. Thank you ๐!
+
+
+
+## ::: ultralytics.solutions.trackzone.TrackZone
+
+
diff --git a/docs/en/solutions/index.md b/docs/en/solutions/index.md
index 4046975de..243fbf275 100644
--- a/docs/en/solutions/index.md
+++ b/docs/en/solutions/index.md
@@ -27,8 +27,10 @@ Here's our curated list of Ultralytics solutions that can be used to create awes
- [Distance Calculation](../guides/distance-calculation.md) ๐: Calculate distances between objects using [bounding box](https://www.ultralytics.com/glossary/bounding-box) centroids in YOLO11, essential for spatial analysis.
- [Queue Management](../guides/queue-management.md) ๐: Implement efficient queue management systems to minimize wait times and improve productivity using YOLO11.
- [Parking Management](../guides/parking-management.md) ๐: Organize and direct vehicle flow in parking areas with YOLO11, optimizing space utilization and user experience.
-- [Analytics](../guides/analytics.md) ๐ NEW: Conduct comprehensive data analysis to discover patterns and make informed decisions, leveraging YOLO11 for descriptive, predictive, and prescriptive analytics.
+- [Analytics](../guides/analytics.md) ๐: Conduct comprehensive data analysis to discover patterns and make informed decisions, leveraging YOLO11 for descriptive, predictive, and prescriptive analytics.
- [Live Inference with Streamlit](../guides/streamlit-live-inference.md) ๐: Leverage the power of YOLO11 for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) directly through your web browser with a user-friendly Streamlit interface.
+- [Live Inference with Streamlit](../guides/streamlit-live-inference.md) ๐: Leverage the power of YOLO11 for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) directly through your web browser with a user-friendly Streamlit interface.
+- [Track Objects in Zone](../guides/trackzone.md) ๐ฏ NEW: Learn how to track objects within specific zones of video frames using YOLO11 for precise and efficient monitoring.
## Solutions Usage
diff --git a/docs/en/tasks/index.md b/docs/en/tasks/index.md
index b31b96c44..d71825fa8 100644
--- a/docs/en/tasks/index.md
+++ b/docs/en/tasks/index.md
@@ -6,7 +6,6 @@ keywords: Ultralytics YOLO11, detection, segmentation, classification, oriented
# Ultralytics YOLO11 Tasks
-
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.
diff --git a/docs/en/tasks/pose.md b/docs/en/tasks/pose.md
index 0523239fc..7efe7414f 100644
--- a/docs/en/tasks/pose.md
+++ b/docs/en/tasks/pose.md
@@ -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.
-
-
-
-
-
- Watch: Pose Estimation with Ultralytics YOLO.
-
-
-
-
- Watch: Pose Estimation with Ultralytics HUB.
-
-
-
+
+
+
+
+ Watch: Ultralytics YOLO11 Pose Estimation Tutorial | Real-Time Object Tracking and Human Pose Detection
+
!!! tip
diff --git a/docs/en/usage/cfg.md b/docs/en/usage/cfg.md
index 95dc8b46f..c51863c5e 100644
--- a/docs/en/usage/cfg.md
+++ b/docs/en/usage/cfg.md
@@ -130,6 +130,14 @@ It is crucial to thoughtfully configure these settings to ensure the exported mo
[Export Guide](../modes/export.md){ .md-button }
+## Solutions Settings
+
+The configuration settings for Ultralytics Solutions offer a flexible way to customize the model for various tasks like object counting, heatmap creation, workout tracking, data analysis, zone tracking, queue management, and region-based counting. These options make it easy to adjust the setup for accurate and useful results tailored to specific needs.
+
+{% include "macros/solutions-args.md" %}
+
+[Solutions Guide](../solutions/index.md){ .md-button }
+
## Augmentation Settings
Augmentation techniques are essential for improving the robustness and performance of YOLO models by introducing variability into the [training data](https://www.ultralytics.com/glossary/training-data), helping the model generalize better to unseen data. The following table outlines the purpose and effect of each augmentation argument:
diff --git a/docs/en/usage/simple-utilities.md b/docs/en/usage/simple-utilities.md
index 2026e5a21..d4bc8b08a 100644
--- a/docs/en/usage/simple-utilities.md
+++ b/docs/en/usage/simple-utilities.md
@@ -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 ๐"
+
+ === "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))
+ video_writer = cv2.VideoWriter("ultralytics.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+
+ masks = None # Initialize variable to store masks data
+ f = 0 # Initialize frame count variable for enabling mouse event.
+ line_x = w # Store width of line.
+ dragging = False # Initialize bool variable for line dragging.
+ classes = model.names # Store model classes names for plotting.
+ window_name = "Ultralytics Sweep Annotator"
+
+
+ def drag_line(event, x, y, flags, param): # Mouse callback for dragging line.
+ global line_x, dragging
+ if event == cv2.EVENT_LBUTTONDOWN or (flags & cv2.EVENT_FLAG_LBUTTON):
+ line_x = max(0, min(x, w))
+ dragging = True
+
+
+ while cap.isOpened(): # Loop over the video capture object.
+ ret, im0 = cap.read()
+ if not ret:
+ break
+ f = f + 1 # Increment frame count.
+ count = 0 # Re-initialize count variable on every frame for precise counts.
+ annotator = Annotator(im0)
+ results = model.track(im0, persist=True) # Track objects using track method.
+ if f == 1:
+ cv2.namedWindow(window_name)
+ cv2.setMouseCallback(window_name, drag_line)
+
+ if results[0].boxes.id is not None:
+ if results[0].masks is not None:
+ masks = results[0].masks.xy
+ track_ids = results[0].boxes.id.int().cpu().tolist()
+ clss = results[0].boxes.cls.cpu().tolist()
+ boxes = results[0].boxes.xyxy.cpu()
+
+ for mask, box, cls, t_id in zip(masks or [None] * len(boxes), boxes, clss, track_ids):
+ color = colors(t_id, True) # Assign different color to each tracked object.
+ if mask is not None and mask.size > 0:
+ # If you want to overlay the masks
+ # mask[:, 0] = np.clip(mask[:, 0], line_x, w)
+ # mask_img = cv2.fillPoly(im0.copy(), [mask.astype(int)], color)
+ # cv2.addWeighted(mask_img, 0.5, im0, 0.5, 0, im0)
+
+ if box[0] > line_x:
+ count += 1
+ annotator.seg_bbox(mask=mask, mask_color=color, label=str(classes[cls]))
+ else:
+ if box[0] > line_x:
+ count += 1
+ annotator.box_label(box=box, color=color, label=str(classes[cls]))
+
+ annotator.sweep_annotator(line_x=line_x, line_y=h, label=f"COUNT:{count}") # Display the sweep
+ cv2.imshow(window_name, im0)
+ video_writer.write(im0)
+ if cv2.waitKey(1) & 0xFF == ord("q"):
+ break
+
+ cap.release() # Release the video capture.
+ video_writer.release() # Release the video writer.
+ cv2.destroyAllWindows() # Destroy all opened windows.
+ ```
+
#### Horizontal Bounding Boxes
```{ .py .annotate }
diff --git a/docs/overrides/assets/favicon.ico b/docs/overrides/assets/favicon.ico
deleted file mode 100644
index 7aa506618..000000000
Binary files a/docs/overrides/assets/favicon.ico and /dev/null differ
diff --git a/examples/YOLO-Series-ONNXRuntime-Rust/README.md b/examples/YOLO-Series-ONNXRuntime-Rust/README.md
index ca05fbb18..0b6fabe20 100644
--- a/examples/YOLO-Series-ONNXRuntime-Rust/README.md
+++ b/examples/YOLO-Series-ONNXRuntime-Rust/README.md
@@ -62,7 +62,7 @@ cargo run -r -- --task detect --ver v8 --nc 6 --model xxx.onnx # YOLOv8
# Classify
cargo run -r -- --task classify --ver v5 --scale s --width 224 --height 224 --nc 1000 # YOLOv5
cargo run -r -- --task classify --ver v8 --scale n --width 224 --height 224 --nc 1000 # YOLOv8
-cargo run -r -- --task classify --ver v11 --scale n --width 224 --height 224 --nc 1000 # YOLOv11
+cargo run -r -- --task classify --ver v11 --scale n --width 224 --height 224 --nc 1000 # YOLO11
# Detect
cargo run -r -- --task detect --ver v5 --scale n # YOLOv5
@@ -71,12 +71,12 @@ cargo run -r -- --task detect --ver v7 --scale t # YOLOv7
cargo run -r -- --task detect --ver v8 --scale n # YOLOv8
cargo run -r -- --task detect --ver v9 --scale t # YOLOv9
cargo run -r -- --task detect --ver v10 --scale n # YOLOv10
-cargo run -r -- --task detect --ver v11 --scale n # YOLOv11
+cargo run -r -- --task detect --ver v11 --scale n # YOLO11
cargo run -r -- --task detect --ver rtdetr --scale l # RTDETR
# Pose
cargo run -r -- --task pose --ver v8 --scale n # YOLOv8-Pose
-cargo run -r -- --task pose --ver v11 --scale n # YOLOv11-Pose
+cargo run -r -- --task pose --ver v11 --scale n # YOLO11-Pose
# Segment
cargo run -r -- --task segment --ver v5 --scale n # YOLOv5-Segment
@@ -86,7 +86,7 @@ cargo run -r -- --task segment --ver v8 --model yolo/FastSAM-s-dyn-f16.onnx # F
# OBB
cargo run -r -- --ver v8 --task obb --scale n --width 1024 --height 1024 --source images/dota.png # YOLOv8-Obb
-cargo run -r -- --ver v11 --task obb --scale n --width 1024 --height 1024 --source images/dota.png # YOLOv11-Obb
+cargo run -r -- --ver v11 --task obb --scale n --width 1024 --height 1024 --source images/dota.png # YOLO11-Obb
```
**`cargo run -- --help` for more options**
diff --git a/examples/YOLOv8-LibTorch-CPP-Inference/main.cc b/examples/YOLOv8-LibTorch-CPP-Inference/main.cc
index b68b7f7e4..0937b5682 100644
--- a/examples/YOLOv8-LibTorch-CPP-Inference/main.cc
+++ b/examples/YOLOv8-LibTorch-CPP-Inference/main.cc
@@ -226,6 +226,7 @@ int main() {
cv::Mat image = cv::imread("/path/to/bus.jpg");
cv::Mat input_image;
letterbox(image, input_image, {640, 640});
+ cv::cvtColor(input_image, input_image, cv::COLOR_BGR2RGB);
torch::Tensor image_tensor = torch::from_blob(input_image.data, {input_image.rows, input_image.cols, 3}, torch::kByte).to(device);
image_tensor = image_tensor.toType(torch::kFloat32).div(255);
diff --git a/mkdocs.yml b/mkdocs.yml
index 7046ba43f..283d52f3d 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -22,7 +22,7 @@ theme:
language: en
custom_dir: docs/overrides/
logo: https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Reverse.svg
- favicon: assets/favicon.ico
+ favicon: https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/logo/favicon-yolo.png
icon:
repo: fontawesome/brands/github
# font: # disabled for faster page load times
@@ -326,7 +326,8 @@ nav:
- Distance Calculation: guides/distance-calculation.md
- Queue Management: guides/queue-management.md
- Parking Management: guides/parking-management.md
- - Live Inference ๐ NEW: guides/streamlit-live-inference.md
+ - Live Inference: guides/streamlit-live-inference.md
+ - Track Objects in Zone ๐ NEW: guides/trackzone.md
- Guides:
- guides/index.md
- YOLO Common Issues: guides/yolo-common-issues.md
@@ -573,6 +574,7 @@ nav:
- speed_estimation: reference/solutions/speed_estimation.md
- streamlit_inference: reference/solutions/streamlit_inference.md
- region_counter: reference/solutions/region_counter.md
+ - trackzone: reference/solutions/trackzone.md
- trackers:
- basetrack: reference/trackers/basetrack.md
- bot_sort: reference/trackers/bot_sort.md
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index fe22ab07a..601d1bb36 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
# Ultralytics YOLO ๐, AGPL-3.0 license
-__version__ = "8.3.38"
+__version__ = "8.3.40"
import os
diff --git a/ultralytics/cfg/__init__.py b/ultralytics/cfg/__init__.py
index de9ef96a1..e4c239f3d 100644
--- a/ultralytics/cfg/__init__.py
+++ b/ultralytics/cfg/__init__.py
@@ -11,7 +11,6 @@ import cv2
from ultralytics.utils import (
ASSETS,
- ASSETS_URL,
DEFAULT_CFG,
DEFAULT_CFG_DICT,
DEFAULT_CFG_PATH,
@@ -42,6 +41,7 @@ SOLUTION_MAP = {
"speed": ("SpeedEstimator", "estimate_speed"),
"workout": ("AIGym", "monitor"),
"analytics": ("Analytics", "process_data"),
+ "trackzone": ("TrackZone", "trackzone"),
"help": None,
}
@@ -75,13 +75,12 @@ ARGV = sys.argv or ["", ""] # sometimes sys.argv = []
SOLUTIONS_HELP_MSG = f"""
Arguments received: {str(['yolo'] + ARGV[1:])}. Ultralytics 'yolo solutions' usage overview:
- yolo SOLUTIONS SOLUTION ARGS
-
- Where SOLUTIONS (required) is a keyword
- SOLUTION (optional) is one of {list(SOLUTION_MAP.keys())}
- ARGS (optional) are any number of custom 'arg=value' pairs like 'show_in=True' that override defaults.
- See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
+ yolo solutions SOLUTION ARGS
+ Where SOLUTION (optional) is one of {list(SOLUTION_MAP.keys())}
+ ARGS (optional) are any number of custom 'arg=value' pairs like 'show_in=True' that override defaults
+ at https://docs.ultralytics.com/usage/cfg
+
1. Call object counting solution
yolo solutions count source="path/to/video/file.mp4" region=[(20, 400), (1080, 400), (1080, 360), (20, 360)]
@@ -96,6 +95,9 @@ SOLUTIONS_HELP_MSG = f"""
5. Generate analytical graphs
yolo solutions analytics analytics_type="pie"
+
+ 6. Track Objects Within Specific Zones
+ yolo solutions trackzone source="path/to/video/file.mp4" region=[(150, 150), (1130, 150), (1130, 570), (150, 570)]
"""
CLI_HELP_MSG = f"""
Arguments received: {str(['yolo'] + ARGV[1:])}. Ultralytics 'yolo' commands use the following syntax:
diff --git a/ultralytics/engine/model.py b/ultralytics/engine/model.py
index 667b54eba..874613d2f 100644
--- a/ultralytics/engine/model.py
+++ b/ultralytics/engine/model.py
@@ -144,6 +144,9 @@ class Model(nn.Module):
else:
self._load(model, task=task)
+ # Delete super().training for accessing self.model.training
+ del self.training
+
def __call__(
self,
source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
@@ -1143,3 +1146,29 @@ class Model(nn.Module):
"""
self.model.eval()
return self
+
+ def __getattr__(self, name):
+ """
+ Enables accessing model attributes directly through the Model class.
+
+ This method provides a way to access attributes of the underlying model directly through the Model class
+ instance. It first checks if the requested attribute is 'model', in which case it returns the model from
+ the module dictionary. Otherwise, it delegates the attribute lookup to the underlying model.
+
+ Args:
+ name (str): The name of the attribute to retrieve.
+
+ Returns:
+ (Any): The requested attribute value.
+
+ Raises:
+ AttributeError: If the requested attribute does not exist in the model.
+
+ Examples:
+ >>> model = YOLO("yolo11n.pt")
+ >>> print(model.stride)
+ >>> print(model.task)
+ """
+ if name == "model":
+ return self._modules["model"]
+ return getattr(self.model, name)
diff --git a/ultralytics/models/yolo/classify/predict.py b/ultralytics/models/yolo/classify/predict.py
index 596931a17..b75a19498 100644
--- a/ultralytics/models/yolo/classify/predict.py
+++ b/ultralytics/models/yolo/classify/predict.py
@@ -54,6 +54,6 @@ class ClassificationPredictor(BasePredictor):
orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
return [
- Results(orig_img, path=img_path, names=self.model.names, probs=pred)
+ Results(orig_img, path=img_path, names=self.model.names, probs=pred.softmax(0))
for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
]
diff --git a/ultralytics/models/yolo/detect/train.py b/ultralytics/models/yolo/detect/train.py
index 606b9fb92..4a32acec1 100644
--- a/ultralytics/models/yolo/detect/train.py
+++ b/ultralytics/models/yolo/detect/train.py
@@ -146,5 +146,5 @@ class DetectionTrainer(BaseTrainer):
"""Get batch size by calculating memory occupation of model."""
train_dataset = self.build_dataset(self.trainset, mode="train", batch=16)
# 4 for mosaic augmentation
- max_num_obj = max(len(l["cls"]) for l in train_dataset.labels) * 4
+ max_num_obj = max(len(label["cls"]) for label in train_dataset.labels) * 4
return super().auto_batch(max_num_obj)
diff --git a/ultralytics/nn/modules/head.py b/ultralytics/nn/modules/head.py
index 29a1953e4..25964ac2e 100644
--- a/ultralytics/nn/modules/head.py
+++ b/ultralytics/nn/modules/head.py
@@ -296,7 +296,7 @@ class Classify(nn.Module):
if isinstance(x, list):
x = torch.cat(x, 1)
x = self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
- return x if self.training else x.softmax(1)
+ return x
class WorldDetect(Detect):
diff --git a/ultralytics/solutions/__init__.py b/ultralytics/solutions/__init__.py
index 9de61edce..a2333129b 100644
--- a/ultralytics/solutions/__init__.py
+++ b/ultralytics/solutions/__init__.py
@@ -10,6 +10,7 @@ from .queue_management import QueueManager
from .region_counter import RegionCounter
from .speed_estimation import SpeedEstimator
from .streamlit_inference import inference
+from .trackzone import TrackZone
__all__ = (
"AIGym",
@@ -23,4 +24,5 @@ __all__ = (
"Analytics",
"inference",
"RegionCounter",
+ "TrackZone",
)
diff --git a/ultralytics/solutions/ai_gym.py b/ultralytics/solutions/ai_gym.py
index 68e369762..359ed9dd4 100644
--- a/ultralytics/solutions/ai_gym.py
+++ b/ultralytics/solutions/ai_gym.py
@@ -71,7 +71,7 @@ class AIGym(BaseSolution):
>>> processed_image = gym.monitor(image)
"""
# Extract tracks
- tracks = self.model.track(source=im0, persist=True, classes=self.CFG["classes"])[0]
+ tracks = self.model.track(source=im0, persist=True, classes=self.CFG["classes"], **self.track_add_args)[0]
if tracks.boxes.id is not None:
# Extract and check keypoints
diff --git a/ultralytics/solutions/solutions.py b/ultralytics/solutions/solutions.py
index fc05d42d6..f47223b57 100644
--- a/ultralytics/solutions/solutions.py
+++ b/ultralytics/solutions/solutions.py
@@ -74,6 +74,10 @@ class BaseSolution:
self.model = YOLO(self.CFG["model"])
self.names = self.model.names
+ self.track_add_args = { # Tracker additional arguments for advance configuration
+ k: self.CFG[k] for k in ["verbose", "iou", "conf", "device", "max_det", "half", "tracker"]
+ }
+
if IS_CLI and self.CFG["source"] is None:
d_s = "solutions_ci_demo.mp4" if "-pose" not in self.CFG["model"] else "solution_ci_pose_demo.mp4"
LOGGER.warning(f"โ ๏ธ WARNING: source not provided. using default source {ASSETS_URL}/{d_s}")
@@ -98,7 +102,7 @@ class BaseSolution:
>>> frame = cv2.imread("path/to/image.jpg")
>>> solution.extract_tracks(frame)
"""
- self.tracks = self.model.track(source=im0, persist=True, classes=self.CFG["classes"])
+ self.tracks = self.model.track(source=im0, persist=True, classes=self.CFG["classes"], **self.track_add_args)
# Extract tracks for OBB or object detection
self.track_data = self.tracks[0].obb or self.tracks[0].boxes
diff --git a/ultralytics/solutions/trackzone.py b/ultralytics/solutions/trackzone.py
new file mode 100644
index 000000000..0492c0a7e
--- /dev/null
+++ b/ultralytics/solutions/trackzone.py
@@ -0,0 +1,68 @@
+# Ultralytics YOLO ๐, AGPL-3.0 license
+
+import cv2
+import numpy as np
+
+from ultralytics.solutions.solutions import BaseSolution
+from ultralytics.utils.plotting import Annotator, colors
+
+
+class TrackZone(BaseSolution):
+ """
+ A class to manage region-based object tracking in a video stream.
+
+ This class extends the BaseSolution class and provides functionality for tracking objects within a specific region
+ defined by a polygonal area. Objects outside the region are excluded from tracking. It supports dynamic initialization
+ of the region, allowing either a default region or a user-specified polygon.
+
+ Attributes:
+ region (ndarray): The polygonal region for tracking, represented as a convex hull.
+
+ Methods:
+ trackzone: Processes each frame of the video, applying region-based tracking.
+
+ Examples:
+ >>> tracker = TrackZone()
+ >>> frame = cv2.imread("frame.jpg")
+ >>> processed_frame = tracker.trackzone(frame)
+ >>> cv2.imshow("Tracked Frame", processed_frame)
+ """
+
+ def __init__(self, **kwargs):
+ """Initializes the TrackZone class for tracking objects within a defined region in video streams."""
+ super().__init__(**kwargs)
+ default_region = [(150, 150), (1130, 150), (1130, 570), (150, 570)]
+ self.region = cv2.convexHull(np.array(self.region or default_region, dtype=np.int32))
+
+ def trackzone(self, im0):
+ """
+ Processes the input frame to track objects within a defined region.
+
+ This method initializes the annotator, creates a mask for the specified region, extracts tracks
+ only from the masked area, and updates tracking information. Objects outside the region are ignored.
+
+ Args:
+ im0 (numpy.ndarray): The input image or frame to be processed.
+
+ Returns:
+ (numpy.ndarray): The processed image with tracking id and bounding boxes annotations.
+
+ Examples:
+ >>> tracker = TrackZone()
+ >>> frame = cv2.imread("path/to/image.jpg")
+ >>> tracker.trackzone(frame)
+ """
+ self.annotator = Annotator(im0, line_width=self.line_width) # Initialize annotator
+ # Create a mask for the region and extract tracks from the masked image
+ masked_frame = cv2.bitwise_and(im0, im0, mask=cv2.fillPoly(np.zeros_like(im0[:, :, 0]), [self.region], 255))
+ self.extract_tracks(masked_frame)
+
+ cv2.polylines(im0, [self.region], isClosed=True, color=(255, 255, 255), thickness=self.line_width * 2)
+
+ # Iterate over boxes, track ids, classes indexes list and draw bounding boxes
+ for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
+ self.annotator.box_label(box, label=f"{self.names[cls]}:{track_id}", color=colors(track_id, True))
+
+ self.display_output(im0) # display output with base class function
+
+ return im0 # return output image for more usage
diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py
index e51855d17..81882363f 100644
--- a/ultralytics/utils/ops.py
+++ b/ultralytics/utils/ops.py
@@ -75,9 +75,8 @@ def segment2box(segment, width=640, height=640):
(np.ndarray): the minimum and maximum x and y values of the segment.
"""
x, y = segment.T # segment xy
- inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
- x = x[inside]
- y = y[inside]
+ x = x.clip(0, width)
+ y = y.clip(0, height)
return (
np.array([x.min(), y.min(), x.max(), y.max()], dtype=segment.dtype)
if any(x)
diff --git a/ultralytics/utils/plotting.py b/ultralytics/utils/plotting.py
index f4514247c..3943a87e8 100644
--- a/ultralytics/utils/plotting.py
+++ b/ultralytics/utils/plotting.py
@@ -238,7 +238,16 @@ class Annotator:
}
def get_txt_color(self, color=(128, 128, 128), txt_color=(255, 255, 255)):
- """Assign text color based on background color."""
+ """
+ Assign text color based on background color.
+
+ Args:
+ color (tuple, optional): The background color of the rectangle for text (B, G, R).
+ txt_color (tuple, optional): The color of the text (R, G, B).
+
+ Returns:
+ txt_color (tuple): Text color for label
+ """
if color in self.dark_colors:
return 104, 31, 17
elif color in self.light_colors:
@@ -544,7 +553,9 @@ class Annotator:
bbox (tuple): Bounding box coordinates in the format (x_min, y_min, x_max, y_max).
Returns:
- angle (degree): Degree value of angle between three points
+ width (float): Width of the bounding box.
+ height (float): Height of the bounding box.
+ area (float): Area enclosed by the bounding box.
"""
x_min, y_min, x_max, y_max = bbox
width = x_max - x_min
@@ -791,19 +802,52 @@ class Annotator:
cv2.polylines(self.im, [np.int32([mask])], isClosed=True, color=mask_color, thickness=2)
text_size, _ = cv2.getTextSize(label, 0, self.sf, self.tf)
- cv2.rectangle(
- self.im,
- (int(mask[0][0]) - text_size[0] // 2 - 10, int(mask[0][1]) - text_size[1] - 10),
- (int(mask[0][0]) + text_size[0] // 2 + 10, int(mask[0][1] + 10)),
- mask_color,
- -1,
- )
-
if label:
+ cv2.rectangle(
+ self.im,
+ (int(mask[0][0]) - text_size[0] // 2 - 10, int(mask[0][1]) - text_size[1] - 10),
+ (int(mask[0][0]) + text_size[0] // 2 + 10, int(mask[0][1] + 10)),
+ mask_color,
+ -1,
+ )
cv2.putText(
self.im, label, (int(mask[0][0]) - text_size[0] // 2, int(mask[0][1])), 0, self.sf, txt_color, self.tf
)
+ def sweep_annotator(self, line_x=0, line_y=0, label=None, color=(221, 0, 186), txt_color=(255, 255, 255)):
+ """
+ Function for drawing a sweep annotation line and an optional label.
+
+ Args:
+ line_x (int): The x-coordinate of the sweep line.
+ line_y (int): The y-coordinate limit of the sweep line.
+ label (str, optional): Text label to be drawn in center of sweep line. If None, no label is drawn.
+ color (tuple): RGB color for the line and label background.
+ txt_color (tuple): RGB color for the label text.
+ """
+ # Draw the sweep line
+ cv2.line(self.im, (line_x, 0), (line_x, line_y), color, self.tf * 2)
+
+ # Draw label, if provided
+ if label:
+ (text_width, text_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, self.sf, self.tf)
+ cv2.rectangle(
+ self.im,
+ (line_x - text_width // 2 - 10, line_y // 2 - text_height // 2 - 10),
+ (line_x + text_width // 2 + 10, line_y // 2 + text_height // 2 + 10),
+ color,
+ -1,
+ )
+ cv2.putText(
+ self.im,
+ label,
+ (line_x - text_width // 2, line_y // 2 + text_height // 2),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ self.sf,
+ txt_color,
+ self.tf,
+ )
+
def plot_distance_and_line(
self, pixels_distance, centroids, line_color=(104, 31, 17), centroid_color=(255, 0, 255)
):
diff --git a/ultralytics/utils/torch_utils.py b/ultralytics/utils/torch_utils.py
index b413297be..a6e744762 100644
--- a/ultralytics/utils/torch_utils.py
+++ b/ultralytics/utils/torch_utils.py
@@ -301,28 +301,22 @@ def fuse_deconv_and_bn(deconv, bn):
def model_info(model, detailed=False, verbose=True, imgsz=640):
- """
- Model information.
-
- imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320].
- """
+ """Print and return detailed model information layer by layer."""
if not verbose:
return
n_p = get_num_params(model) # number of parameters
n_g = get_num_gradients(model) # number of gradients
n_l = len(list(model.modules())) # number of layers
if detailed:
- LOGGER.info(
- f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}"
- )
+ LOGGER.info(f"{'layer':>5}{'name':>40}{'gradient':>10}{'parameters':>12}{'shape':>20}{'mu':>10}{'sigma':>10}")
for i, (name, p) in enumerate(model.named_parameters()):
name = name.replace("module_list.", "")
LOGGER.info(
- "%5g %40s %9s %12g %20s %10.3g %10.3g %10s"
- % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std(), p.dtype)
+ f"{i:>5g}{name:>40s}{p.requires_grad!r:>10}{p.numel():>12g}{str(list(p.shape)):>20s}"
+ f"{p.mean():>10.3g}{p.std():>10.3g}{str(p.dtype):>15s}"
)
- flops = get_flops(model, imgsz)
+ flops = get_flops(model, imgsz) # imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]
fused = " (fused)" if getattr(model, "is_fused", lambda: False)() else ""
fs = f", {flops:.1f} GFLOPs" if flops else ""
yaml_file = getattr(model, "yaml_file", "") or getattr(model, "yaml", {}).get("yaml_file", "")
+
-
-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.
+
+ 
+ 
-
+ 
-## Table of Contents
-
-1. [Code of Conduct](#code-of-conduct)
-2. [Contributing via Pull Requests](#contributing-via-pull-requests)
- - [CLA Signing](#cla-signing)
- - [Google-Style Docstrings](#google-style-docstrings)
- - [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! ๐
+
+### 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/
+โ
+โโโ LICENSE # AGPL-3.0 license text
+โโโ README.md # Project overview and license information
+โโโ src/ # Source code for the project
+โ โโโ model.py # YOLO-based model implementation
+โ โโโ utils.py # Utility scripts
+โ โโโ ...
+โโโ pyproject.toml # Python dependencies
+โโโ tests/ # Unit and integration tests
+โโโ .github/ # GitHub Actions for CI
+โ โโโ workflows/
+โ โโโ ci.yml # Continuous integration configuration
+โโโ docs/ # Project documentation
+ โโโ index.md
+```
+
+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.
diff --git a/docs/en/integrations/albumentations.md b/docs/en/integrations/albumentations.md
index fe7b081c2..3fa4040ba 100644
--- a/docs/en/integrations/albumentations.md
+++ b/docs/en/integrations/albumentations.md
@@ -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.
!!! example "Usage"
diff --git a/docs/en/integrations/sony-imx500.md b/docs/en/integrations/sony-imx500.md
index 88338ebb6..5dccbc558 100644
--- a/docs/en/integrations/sony-imx500.md
+++ b/docs/en/integrations/sony-imx500.md
@@ -162,7 +162,7 @@ cd examples/imx500
Step 3: Run YOLOv8 object detection, using the labels.txt file that has been generated during the IMX500 export.
```bash
-python imx500_object_detection_demo.py --model