YOLO Dataset Tools - Comprehensive toolkit for YOLO format dataset processing
Project description
Features
- Auto-detects and handles both OBB (9 values:
class_id x1 y1 x2 y2 x3 y3 x4 y4) and BBox (5 values:class_id x_center y_center width height) formats - SAHI-powered smart slicing for large images with horizontal/grid modes and configurable overlap
- Resize (scale & crop) with custom interpolation (linear/lanczos4),image or yolo dataset
- Coordinate-based precision cropping
- Object cropping from model inference or dataset labels with padding and size filters
- Video frame extraction with parallel processing support
- Smart train/val split with class balancing
- Multi-dataset merging
- Dataset extraction by class IDs with optional label filtering and ID remapping
- Synthetic dataset generation with configurable objects per image, rotation ranges, and balanced class sampling
- YOLO auto-labeling with BBox/OBB format support
- Interactive dataset browser with keyboard controls (n/p/q)
Installation
pip install yolodt
Usage
ydt --help
usage: ydt [-h] [--version] [-v]
{slice,augment,video,crop-coords,resize,concat,split,merge,extract,synthesize,auto-label,analyze,visualize,viz-letterbox}
...
YOLO Dataset Tools - Process and manage YOLO format datasets
positional arguments:
{slice,augment,video,crop-coords,crop,resize,concat,split,merge,extract,synthesize,auto-label,analyze,visualize,viz-letterbox}
Available commands
slice Slice large images into tiles
augment Augment dataset with rotations
video Extract frames from videos
crop-coords Crop images by coordinates
crop Crop objects from images using model or dataset labels
resize Resize images or YOLO dataset
concat Concatenate two images
split Split dataset into train/val
merge Merge multiple datasets
extract Extract classes, images, or labels
synthesize Generate synthetic dataset
auto-label Auto-label images using YOLO model
analyze Analyze dataset statistics
visualize Visualize YOLO dataset interactively
viz-letterbox Visualize letterbox transformation
options:
-h, --help show this help message and exit
--version show program's version number and exit
-v, --verbose Verbose output
🙏 Acknowledgments
- Ultralytics - YOLO framework
- SAHI - Slicing aided hyper inference
- Albumentations - Image augmentation
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
yolodt-0.4.0.tar.gz
(82.8 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
yolodt-0.4.0-py3-none-any.whl
(83.2 kB
view details)
File details
Details for the file yolodt-0.4.0.tar.gz.
File metadata
- Download URL: yolodt-0.4.0.tar.gz
- Upload date:
- Size: 82.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
fdf22ea41c0ef88d87a91d16b2da3beb96ba75ff5996c24ca57665e1fcfe2f38
|
|
| MD5 |
24c7f9a364b3f0122c4ec86b02a49ddf
|
|
| BLAKE2b-256 |
9b910e5130d884ba58307c968008d1c5083a037f7e4cddeeed4932ebb0f31ed3
|
File details
Details for the file yolodt-0.4.0-py3-none-any.whl.
File metadata
- Download URL: yolodt-0.4.0-py3-none-any.whl
- Upload date:
- Size: 83.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
287af4d6768666969e4b0e25424105f515763c9f3cd092c1abfc4997303eea8b
|
|
| MD5 |
d2b7b892ec7fc1b249d42955898ebad7
|
|
| BLAKE2b-256 |
8783dfd2a003d54b8e9bcb8b6c07fb869d4a58a12a1240558fc7dd99f6f18e28
|