description:Please provide as much information as possible. Copy and paste console output and error messages. Use [Markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to format text, code and logs. If necessary, include screenshots for visual elements only. Providing detailed information will help us resolve the issue more efficiently.
description:Please provide as much information as possible. Copy and paste console output and error messages including the _full_ traceback. Use [Markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to format text, code and logs. If necessary, include screenshots for visual elements only. Providing detailed information will help us resolve the issue more efficiently.
placeholder:|
💡 ProTip! Include as much information as possible (logs, tracebacks, screenshots, etc.) to receive the most helpful response.
validations:
@ -52,7 +52,7 @@ body:
- type:textarea
attributes:
label:Environment
description:Many issues are often related to dependency versions and hardware. Please provide the output of `yolo checks` (CLI) or `ultralytics.utils.checks.collect_system_info()` (Python) command to help us diagnose the problem.
description:Try the latest version (`pip install -U ultralytics`) before reporting a bug. If it's still present, please provide the output of `yolo checks` (CLI) or `ultralytics.utils.checks.collect_system_info()` (Python) command to help us diagnose the problem.
placeholder:|
Paste output of `yolo checks` (CLI) or `ultralytics.utils.checks.collect_system_info()` (Python) command, i.e.:
<ahref="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-brain-tumor-detection-dataset.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Brain Tumor Dataset In Colab"></a>
A brain tumor detection dataset consists of medical images from MRI or CT scans, containing information about brain tumor presence, location, and characteristics. This dataset is essential for training [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) algorithms to automate brain tumor identification, aiding in early diagnosis and treatment planning.
@ -6,6 +6,8 @@ keywords: medical-pills dataset, pill detection, pharmaceutical imaging, AI in h
# Medical Pills Dataset
<ahref="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-medical-pills-dataset.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Medical Pills Dataset In Colab"></a>
The medical-pills detection dataset is a proof-of-concept (POC) dataset, carefully curated to demonstrate the potential of AI in pharmaceutical applications. It contains labeled images specifically designed to train [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) [models](https://docs.ultralytics.com/models/) for identifying medical-pills. This dataset serves as a foundational resource for automating essential [tasks](https://docs.ultralytics.com/tasks/) such as quality control, packaging automation, and efficient sorting in pharmaceutical workflows. By integrating this dataset into projects, researchers and developers can explore innovative [solutions](https://docs.ultralytics.com/solutions/) that enhance [accuracy](https://www.ultralytics.com/glossary/accuracy), streamline operations, and ultimately contribute to improved healthcare outcomes.
<ahref="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-carparts-segmentation-dataset.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Carparts Segmentation Dataset In Colab"></a>
The [Roboflow](https://roboflow.com/?ref=ultralytics) [Carparts Segmentation Dataset](https://universe.roboflow.com/gianmarco-russo-vt9xr/car-seg-un1pm?ref=ultralytics) is a curated collection of images and videos designed for [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications, specifically focusing on segmentation tasks related to car parts. This dataset provides a diverse set of visuals captured from multiple perspectives, offering valuable annotated examples for training and testing segmentation models.
Whether you're working on automotive research, developing AI solutions for vehicle maintenance, or exploring computer vision applications, the Carparts Segmentation Dataset serves as a valuable resource for enhancing accuracy and efficiency in your projects.
| `sam_model` | `str, optional` | Pre-trained SAM segmentation model. Defaults to `'sam_b.pt'`. | `'sam_b.pt'` |
| `device` | `str, optional` | Device to run the models on. Defaults to an empty string (CPU or GPU, if available). | `''` |
| `output_dir` | `str or None, optional` | Directory to save the annotated results. Defaults to a `'labels'` folder in the same directory as `'data'`. | `None` |
{% include "macros/sam-auto-annotate.md" %}
The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and [SAM segmentation models](../../models/sam.md), the device to run the models on, and the output directory for saving the annotated results.
The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection models i.e. [YOLO11](../../models/yolo11.md), [YOLOv8](../../models/yolov8.md) or other [models](../../models/index.md) and segmentation models i.e, [SAM](../../models/sam.md), [SAM2](../../models/sam-2.md) or [MobileSAM](../../models/mobile-sam.md), the device to run the models on, and the output directory for saving the annotated results.
By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.
@ -195,7 +189,7 @@ Auto-annotation in Ultralytics YOLO allows you to generate segmentation annotati
```python
from ultralytics.data.annotator import auto_annotate
auto_annotate(data="path/to/images", det_model="yolo11x.pt", sam_model="sam_b.pt") # or sam_model="mobile_sam.pt"
```
This function automates the annotation process, making it faster and more efficient. For more details, explore the [Auto-Annotation](#auto-annotation) section.
This function automates the annotation process, making it faster and more efficient. For more details, explore the [Auto-Annotate Reference](https://docs.ultralytics.com/reference/data/annotator/#ultralytics.data.annotator.auto_annotate).
<ahref="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-train-ultralytics-yolo-on-package-segmentation-dataset.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Package Segmentation Dataset In Colab"></a>
The [Roboflow](https://roboflow.com/?ref=ultralytics) [Package Segmentation Dataset](https://universe.roboflow.com/factorypackage/factory_package?ref=ultralytics) is a curated collection of images specifically tailored for tasks related to package segmentation in the field of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv). This dataset is designed to assist researchers, developers, and enthusiasts working on projects related to package identification, sorting, and handling.
Containing a diverse set of images showcasing various packages in different contexts and environments, the dataset serves as a valuable resource for training and evaluating segmentation models. Whether you are engaged in logistics, warehouse automation, or any application requiring precise package analysis, the Package Segmentation Dataset provides a targeted and comprehensive set of images to enhance the performance of your computer vision algorithms.
@ -8,6 +8,8 @@ keywords: Ultralytics, YOLO11, heatmaps, data visualization, data analysis, comp
## Introduction to Heatmaps
<ahref="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-generate-heatmaps-using-ultralytics-yolo.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Heatmaps In Colab"></a>
A heatmap generated with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) transforms complex data into a vibrant, color-coded matrix. This visual tool employs a spectrum of colors to represent varying data values, where warmer hues indicate higher intensities and cooler tones signify lower values. Heatmaps excel in visualizing intricate data patterns, correlations, and anomalies, offering an accessible and engaging approach to data interpretation across diverse domains.
<ahref="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-count-the-objects-using-ultralytics-yolo.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Object Counting In Colab"></a>
Object counting with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLO11 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) capabilities.
# Ultralytics Docs: Using YOLO11 with SAHI for Sliced Inference
<ahref="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-use-ultralytics-yolo-with-sahi.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open SAHI for Sliced Inference In Colab"></a>
Welcome to the Ultralytics documentation on how to use YOLO11 with [SAHI](https://github.com/obss/sahi) (Slicing Aided Hyper Inference). This comprehensive guide aims to furnish you with all the essential knowledge you'll need to implement SAHI alongside YOLO11. We'll deep-dive into what SAHI is, why sliced inference is critical for large-scale applications, and how to integrate these functionalities with YOLO11 for enhanced [object detection](https://www.ultralytics.com/glossary/object-detection) performance.
<palign="center">
@ -117,7 +119,7 @@ from sahi.predict import get_prediction
result = get_prediction("demo_data/small-vehicles1.jpeg", detection_model)
# With a numpy image
result = get_prediction(read_image("demo_data/small-vehicles1.jpeg"), detection_model)
<ahref="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-track-the-objects-in-zone-using-ultralytics-yolo.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open TrackZone In Colab"></a>
## 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.
<ahref="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-monitor-workouts-using-ultralytics-yolo.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Workouts Monitoring In Colab"></a>
Monitoring workouts through pose estimation with [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics/) enhances exercise assessment by accurately tracking key body landmarks and joints in real-time. This technology provides instant feedback on exercise form, tracks workout routines, and measures performance metrics, optimizing training sessions for users and trainers alike.
@ -47,7 +47,7 @@ These are the notable functionalities offered by YOLO11's Val mode:
## Usage Examples
Validate trained YOLO11n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
Validate trained YOLO11n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes. See Arguments section below for a full list of validation arguments.
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