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Dataset Label Audit for Image Datasets

Dataset Label Audit for Image Datasets

by: JG
Find the data problems that silently hurt your model before you train: suspected mislabels, duplicate and near-duplicate images, train/val/test leakage, conflicting labels, class imbalance, corrupt images, missing label files and broken bounding boxes. Supports classification folders, YOLO and COCO. Runs locally and read-only, so your data never leaves your machine. Get a prioritized fix list in plain language.
#Ingeniería#Análisis#Investigación
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Why

Bad labels, duplicates and data leakage quietly cap your model's accuracy, and a leaked test set makes your results look better than they are. This Agent finds those problems before you spend GPU hours training.

What it checks

Priority Problem
Critical Train/val/test leakage (same or near-identical image in two splits)
Critical Conflicting labels (same image saved under two classes) and suspected mislabels
Critical Invalid class IDs, malformed label lines
High Corrupt images, missing label files, zero-size or out-of-image boxes, non-normalized YOLO coordinates
High Empty or tiny classes, class imbalance
Medium Duplicate boxes, overlapping boxes with different classes, orphan or empty label files
Low Same-split duplicates, tiny boxes, extreme aspect ratios

Supported formats

  • Classification folders: root/<class>/*.jpg, optionally under train/ val/ test/
  • YOLO: images/ + labels/, class names from data.yaml, classes.txt or obj.names
  • COCO: one COCO JSON plus images

Suspected mislabels

Give it a CSV of out-of-sample model probabilities (path,class1,class2,..., e.g. from k-fold cross-validation) and it ranks the images most likely to be mislabeled, confident-learning style. No predictions yet? It can write a k-fold script for PyTorch, Ultralytics or Keras.

Private by design

  • Runs on your own machine. Your images are never uploaded.
  • Read-only: it never moves or deletes your files. Fixes are suggestions, or a list you approve.
  • Only needs Python 3.8+, numpy and Pillow. 6,000 images audited in about 30 seconds in our test.

What you get

A plain-language report in your language (English, Traditional/Simplified Chinese, Japanese, Korean, Vietnamese, Indonesian): a one-line verdict, problems ranked by severity with example files and concrete fixes, a per-split class table, and next steps. Optional fix_list.csv for large datasets.


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