yolo-scout 1.1.2

YOLO Dataset Quality Analysis Tool powered by FiftyOne

🔍 YoloScout — YOLO Dataset quality analysis tool

A comprehensive tool for analyzing and visualizing YOLO dataset quality using FiftyOne

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🚀 Quick Start

Installation

pip install yolo-scout

Basic Usage

# Option 1: Command-line only (no config file)
yolo-scout --dataset-path /path/to/dataset --dataset-task detect

# Option 2: Config file only (more details below)
yolo-scout --config my_config.yaml

# Option 3: Config file + overrides
yolo-scout --config default.yaml --batch-size 8

# Option 4: Force reload of an existing dataset
yolo-scout --dataset-path /path/to/dataset --dataset-task detect --reload

If you want to use the configuration file option, you can either create a config file (e.g., my_config.yaml) with the following structure:

dataset:
  path: "/path/to/your/dataset"
  name: "my_dataset"  # optional, auto-generated if not set
  task: "detect"  # detect, segment, classify, pose, obb
  reload: false

embeddings:
  skip: false
  model: "openai_clip"
  batch_size: 16
  mask_background: true  # Disable with --skip-mask-background if needed

thumbnails:
  dir: "./thumbnails"
  width: 800

quality:
  skip: false

port: 5151

Command-Line Arguments

Argument	Type	Default	Description
--config	str	None	Path to config YAML file. Overrides default settings.
--dataset-path	str	None	Path to your dataset. Required unless provided in config file and must follow the YOLO format.
--dataset-task	str	'detect'	Task type: classify, detect, segment, pose, obb. Required unless in config. More info on the tasks below.
--dataset-name	str	'default'	Name for the FiftyOne dataset. Auto-generated from path if not set.
--reload	bool	false	Force reload of the dataset even if it already exists. The current dataset will be deleted and recreated.
--skip-embeddings	bool	false	Skip CLIP embedding computation (useful for quick visualization).
--embeddings-model	str	'openai_clip'	Embeddings model to use. Possible values: openai_clip, metaclip_400m, metaclip_fullcc, siglip_base_224.
--batch-size	int	16	Batch size used during CLIP embedding computation.
--skip-mask-background	bool	false	Skip background masking for patch crops in segmentation/OBB tasks. Masking is enabled by default, replacing background with gray (114, 114, 114).
--thumbnail-width	int	800	Width (in pixels) of the generated image thumbnails in FiftyOne. The height is adjusted automatically to maintain aspect ratio. Set to -1 to disable thumbnail saving.
--thumbnail-dir	str	'./thumbnails'	Path to the directory where the thumbnails are saved.
--port	int	5151	Port to launch the FiftyOne app on.
--skip-quality	bool	false	Skip image quality metrics computation (blurriness, brightness, aspect_ratio, entropy).
--skip-launch	bool	false	Skip launching the FiftyOne app after processing.

📊 Supported tasks and image metadata

For each expected task format, the following metadata will be computed and available in FiftyOne for each annotation:

Task	Available parameters when using the UI
classify	cls_label.label
detect	area, width, height, iou_score
segment	area, num_keypoints, width, height, iou_score
obb	area, width, height, iou_score
pose	area, num_keypoints, width, height, iou_score

Also, for each image, the following metadata will be computed:

Image Metadata	Description
object_count	Number of objects in the image
metadata.size_bytes	Size of the image file in bytes
metadata.width	Width of the image in pixels
metadata.height	Height of the image in pixels
metadata.mime_type	MIME type of the image (e.g., image/jpeg)
metadata.num_channels	Number of color channels (e.g., 3 for RGB)

The following quality metrics are computed unless --skip-quality is passed. All metrics operate on grayscale pixel values and are available at both image and patch level.

Metric	Description
blurriness	Inverse of the Laplacian variance. A score close to 1 indicates a blurry image, while a score close to 0 indicates a sharp one
brightness	Mean pixel intensity normalized between 0 and 1. A score of 0 is fully dark and a score of 1 is fully bright
aspect_ratio	Width-to-height ratio of the image or patch crop. Values greater than 1 are wider than tall, values less than 1 are taller than wide
entropy	Shannon entropy of the pixel intensity histogram. A low score indicates a flat or visually repetitive image

⭐️ Supported Models

All models use 224x224 input resolution. This is a constraint imposed by FiftyOne's OpenCLIP integration (higher resolution variants (384, 512) cause preprocessing errors when computing embeddings). The 224x224 resolution provides excellent quality for most computer vision tasks while maintaining compatibility with FiftyOne's model zoo.

Model	Description	Training Dataset
openai_clip	Original OpenAI CLIP model with ViT-B/32 architecture. Hosted on GitHub releases for offline usage. This is the default model and works without internet connection after first download.	OpenAI CLIP
metaclip_400m	MetaCLIP model trained on curated 400M image-text pairs. Offers improved data quality and better embeddings compared to OpenAI CLIP while maintaining the same speed and architecture.	MetaCLIP
metaclip_fullcc	MetaCLIP model trained on the full CommonCrawl dataset. Provides the highest quality embeddings among MetaCLIP variants with more diverse training data.	MetaCLIP
siglip_base_224	SigLIP (Sigmoid Loss for Language-Image Pre-training) base model. Uses improved sigmoid loss function for better performance with smaller batch sizes and more efficient training.	SigLIP

Model Selection Guide

• Use openai_clip if you want to use the most common embeddings model
• Use metaclip_400m for better quality embeddings (recommended default)
• Use metaclip_fullcc when you need the highest quality embeddings
• Use siglip_base_224 as an alternative to CLIP-based models

All models have similar inference speed and produce 512-dimensional embeddings with full support for FiftyOne visualization and analysis features.

🧩 Additional Installed Plugins

This tool ships with a custom-built FiftyOne plugin that is automatically installed at startup. No manual setup required.

Plugin	Description	Icon	How to use?
@ultralytics/image-adjuster	Custom plugin to adjust image brightness, contrast, and label overlay opacity.		Open a sample, then click the slider icon in the bottom-left corner.

⚒️ Dataset Structure

This tool supports two common YOLO dataset directory structures:

Format 1: Type-First Structure

dataset/
├── images/
│   ├── train/
│   │   ├── image1.jpg
│   │   ├── image2.jpg
│   │   └── ...
│   ├── val/
│   │   ├── image1.jpg
│   │   └── ...
│   └── test/
│       └── ...
└── labels/
    ├── train/
    │   ├── image1.txt
    │   ├── image2.txt
    │   └── ...
    ├── val/
    │   ├── image1.txt
    │   └── ...
    └── test/
        └── ...

In this format, images and labels are organized by type first, then by split.

Format 2: Split-First Structure

dataset/
├── train/
│   ├── images/
│   │   ├── image1.jpg
│   │   ├── image2.jpg
│   │   └── ...
│   └── labels/
│       ├── image1.txt
│       ├── image2.txt
│       └── ...
├── val/
│   ├── images/
│   │   └── ...
│   └── labels/
│       └── ...
└── test/
    ├── images/
    │   └── ...
    └── labels/
        └── ...

In this format, the dataset is organized by split first, then by type (images/labels).

⌨️ FiftyOne commands

If you have used this tool at least one time to visualize a dataset, you can then use the following commands bellow to interact with the FiftyOne datasets and application:

# List all the datasets
fiftyone datasets list

# Delete a specific dataset using its name
fiftyone datasets delete <dataset_name>

# Delete all datasets
python -c "import fiftyone as fo; [fo.delete_dataset(name) for name in fo.list_datasets()]"

# Launch the FiftyOne app
fiftyone app launch

# Launch the FiftyOne app and pre-select a dataset using its name
fiftyone app launch <dataset_name>

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with FiftyOne by Voxel51
• Inspired by Ultralytics YOLO ecosystem
• CLIP models from OpenAI

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Made with ❤️ for the YOLO community