yolo-vision-tools

# Ultralytics YOLO Vision Tools Ultralytics YOLO is a state-of-the-art computer vision framework supporting multiple tasks including object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection. This skill provides comprehensive guidance for using YOLO effectively. **Latest Model**: YOLO26 (released January 2026) features end-to-end NMS-free inference and optimized edge deployment. For stable production workloads, both YOLO26 and YOLO11 are recommended. ## Quick Start ### 1. Installation & Environment Check ```bash # Install/update Ultralytics pip install -U ultralytics # Verify installation and check environment yolo checks ``` The `yolo checks` command validates Python version, PyTorch, CUDA, GPU availability, and all dependencies. For detailed environment troubleshooting, see [Environment Check](./references/environment_check.md) or use the provided environment check script: `python scripts/check_environment.py`. ### 2. Basic Usage Examples #### Python Interface ```python from ultralytics import YOLO # Load a model (YOLO automatically infers task from model) model = YOLO("yolo26n.pt") # or your custom model path # Predict on various sources # By default, outputs are saved to workspace/yolo-vision folder results = model("image.jpg") # image file → saved to yolo-vision/outputs/images/ results = model("video.mp4", stream=True) # video with streaming → saved to yolo-vision/outputs/videos/ results = model("https://example.com/image.jpg") # URL → saved to yolo-vision/outputs/images/ results = model(0, show=True) # webcam with display → saved to yolo-vision/outputs/videos/ # Custom output directory (optional) results = model("image.jpg", project="/custom/path") # save to custom directory ``` #### CLI Interface ```bash # Basic syntax: yolo TASK MODE ARGS # By default, outputs are saved to workspace/yolo-vision folder yolo predict model=yolo26n.pt source="image.jpg" # → saved to yolo-vision/runs/detect/predict/ # Task-specific examples yolo detect predict model=yolo26n.pt source="video.mp4" # → saved to yolo-vision/runs/detect/predict/ yolo segment predict model=yolo26n-seg.pt source="image.jpg" # → saved to yolo-vision/runs/segment/predict/ yolo pose predict model=yolo26n-pose.pt source="image.jpg" # → saved to yolo-vision/runs/pose/predict/ # Custom output directory (optional) yolo predict model=yolo26n.pt source="image.jpg" project="/custom/path" # save to custom directory ``` ### 3. Model Selection For quick start, use these default models: - **Detection**: `yolo26n.pt` (nano), `yolo26s.pt` (small), `yolo26m.pt` (medium) - **Segmentation**: `yolo26n-seg.pt`, `yolo26s-seg.pt`, `yolo26m-seg.pt` - **Classification**: `yolo26n-cls.pt`, `yolo26s-cls.pt`, `yolo26m-cls.pt` - **Pose Estimation**: `yolo26n-pose.pt`, `yolo26s-pose.pt`, `yolo26m-pose.pt` - **Oriented Detection**: `yolo26n-obb.pt`, `yolo26s-obb.pt`, `yolo26m-obb.pt` For complete model list and selection guidance: [Model Names](./references/model_names.md) | [Model Selection](./references/model_selection.md) ## Core Workflow ### Step 1: Understand YOLO Tasks YOLO supports five main computer vision tasks. Choose the right task for your application: - **Detection**: Identify and localize objects with bounding boxes - **Segmentation**: Generate pixel-level masks for objects - **Classification**: Categorize entire images - **Pose Estimation**: Detect keypoints for pose analysis - **Oriented Detection**: Detect rotated objects with angle parameter Detailed comparison: [Task Types](./references/task_types.md) ### Step 2: Select Appropriate Model Consider these factors when selecting a model: - **Speed vs. Accuracy**: Nano (fastest) → X (most accurate) - **Hardware Constraints**: GPU memory, CPU performance - **Application Requirements**: Real-time vs. batch processing Guidance: [Model Selection](./references/model_selection.md) ### Step 3: Configure Parameters Common configuration parameters: - `conf`: Confidence threshold (default: 0.25) - `iou`: IoU threshold for NMS (default: 0.7) - `imgsz`: Input image size (default: 640) - `device`: Device ID (`0` for first GPU, `cpu` for CPU) - `save`: Save results to disk - `show`: Display results in real-time Complete examples: [Configuration Samples](./references/configuration_samples.md) ### Step 4: Process Results YOLO returns `Results` objects containing: - `boxes`: Bounding boxes, confidence scores, class labels - `masks`: Segmentation masks (for segmentation tasks) - `keypoints`: Pose keypoints (for pose estimation) - `probs`: Classification probabilities (for classification) - `obb`: Oriented bounding boxes (for OBB tasks) ## Advanced Topics ### Training Custom Models ```python from ultralytics import YOLO # Load a model model = YOLO("yolo26n.pt") # Train on custom dataset results = model.train(data="dataset.yaml", epochs=100, imgsz=640) ``` Training guide: [Training Basics](./references/training_basics.md) | [Dataset Preparation](./references/dataset_preparation.md) ### Installation Options Multiple installation methods available: - **pip**: `pip install -U ultralytics` - **Conda**: `conda install -c conda-forge ultralytics` - **Docker**: Pre-built images for GPU/CPU environments - **From Source**: For development and customization Detailed instructions: [Installation Guide](./references/installation_guide.md) ### Performance Optimization - **Streaming Mode**: Use `stream=True` for videos/long sequences to reduce memory - **Batch Processing**: Process multiple images together for efficiency - **Hardware Acceleration**: Configure CUDA, TensorRT, or OpenVINO for optimal performance ## Reference Documentation | Document | Description | |----------|-------------| | [Environment Check](./references/environment_check.md) | Comprehensive environment validation and troubleshooting | | [Installation Guide](./references/installation_guide.md) | All installation methods (pip, Conda, Docker, source) | | [Task Types](./references/task_types.md) | Detailed comparison of YOLO tasks and use cases | | [Model Names](./references/model_names.md) | Complete YOLO26 model list with specifications | | [Model Selection](./references/model_selection.md) | Strategy for choosing models based on requirements | | [Configuration Samples](./references/configuration_samples.md) | Parameter configuration examples for various scenarios | | [Dataset Preparation](./references/dataset_preparation.md) | Guide for preparing custom datasets for training | | [Training Basics](./references/training_basics.md) | Fundamentals of training YOLO models on custom data | | [Parameter Reference](./references/parameter_reference.md) | Complete reference for all YOLO configuration parameters | ## Utility Scripts To save token usage and provide ready-to-use tools, the following Python scripts are available in the `scripts/` directory: | Script | Description | Usage Example | |--------|-------------|---------------| | **check_environment.py** | Comprehensive environment diagnostics | `python scripts/check_environment.py` | | **config_templates.py** | Ready-to-use configuration templates | `from scripts.config_templates import get_production_config` | | **dataset_tools.py** | Dataset preparation and conversion tools | `from scripts.dataset_tools import coco_to_yolo` | | **training_helpers.py** | Training, evaluation, and model management | `from scripts.training_helpers import evaluate_model` | | **quick_tests.py** | Quick functionality tests | `python scripts/quick_tests.py --test environment` | | **model_utils.py** | Model selection and validation utilities | `from scripts.model_utils import select_model` | **Benefits of using scripts:** - **Save tokens**: Large code blocks are extracted from documentation - **Ready-to-use**: No need to copy-paste code from documentation - **Modular**: Import only what you need - **Maintainable**: Scripts can be updated independently ## Troubleshooting ### Common Issues **Q: `yolo` command not found after installation?** A: Try `python -m ultralytics yolo` or check Python environment PATH. **Q: How to use specific GPU?** A: Set `device=0` (first GPU) or `device=cpu` for CPU-only mode. **Q: Model downloads slowly?** A: Set `ULTRALYTICS_HOME` environment variable to control cache location. **Q: How to filter specific classes?** A: Use `classes` parameter: `classes=[0, 2, 5]` (class indices). **Q: Memory issues with long videos?** A: Use `stream=True` to process videos as generators. **Q: Real-time webcam support?** A: Yes, use `source=0` (default camera) with `show=True` for live display. ### Getting Help - Run `yolo checks` to diagnose environment issues - Check official documentation: https://docs.ultralytics.com - Review configuration reference: https://docs.ultralytics.com/usage/cfg/ ## Output Directory Convention ### Default Output Location When processing images or videos with YOLO, if the user does not specify an output directory, all generated files will be saved to the workspace's `yolo-vision` folder. ### File Organization The `yolo-vision` folder will be organized as follows: ``` yolo-vision/ ├── inputs/ # Original input files (copied for reference) ├── outputs/ # Processed files with detection results │ ├── images/ # Detected images │ ├── videos/ # Detected videos │ └── previews/ # Preview images ├── reports/ # Analysis reports and statistics │ ├── json/ # JSON format reports │ ├── markdown/ # Markdown format reports │ └── csv/ # CSV format data ├── models/ # Downloaded YOLO models │ ├── yolo26/ # YOLO26 models │ ├── yolo11/ # YOLO11 models │ └── custom/ # Custom trained models └── logs/ # Processing logs and debug information ``` ### Automatic Folder Creation The skill will automatically: 1. Create the `yolo-vision` folder if it doesn't exist 2. Create all subdirectories as needed 3. Organize files by date and task type 4. Generate timestamp-based filenames for easy tracking ### Example Usage ```python # Without specifying output directory - uses default yolo-vision folder results = model("image.jpg") # Output saved to yolo-vision/outputs/images/ # With custom output directory results = model("image.jpg", save_dir="/custom/path") # Uses specified path ``` ### Benefits 1. **Consistency**: All YOLO outputs in one predictable location 2. **Organization**: Files automatically categorized by type 3. **Backup**: Input files are preserved for reference 4. **Reproducibility**: Easy to find and compare previous analyses 5. **Clean Workspace**: Prevents clutter in the main workspace directory ### User Override Users can still specify custom output directories when needed: - By providing a `save_dir` parameter in Python code - By using the `--project` flag in CLI commands - By setting the `ULTRALYTICS_PROJECT` environment variable --- **License Note**: Ultralytics YOLO is available under AGPL-3.0 for open source use and Enterprise License for commercial applications. Review licensing at https://ultralytics.com/license.