# Paperspace/DataAugmentationForObjectDetection

Data Augmentation For Object Detection

Repository: https://github.com/Paperspace/DataAugmentationForObjectDetection
Canonical: https://ross.abutalabs.com/products/dataaugmentationforobjectdetection
Homepage: https://blog.paperspace.com/data-augmentation-for-bounding-boxes/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: data-augmentation, imagine-augmentation, object-detection, bounding-box, deep-learning, opencv
Last push: 2020-04-14T21:45:40+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2914, "days_push": 2332, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1156, forks 313 (observed 2026-08-28T04:03:48.062297+00:00)

## What it is
A Python library implementing data augmentation transforms (flipping, scaling, rotation, shearing, translation, resizing) that adjust both images and bounding boxes for object detection training. It accompanies a Paperspace tutorial series and is written as Jupyter Notebook code with OpenCV and NumPy.

## Use cases
- augment images with bounding boxes for object detection training
- flip images and adjust bounding box coordinates
- rotate or shear images while keeping boxes valid
- expand a small object detection dataset
- learn how image augmentation works for detection tasks

## When to avoid
- you need a production-grade, actively maintained augmentation pipeline
- you need segmentation masks, keypoints, or GPU-accelerated augmentation
- you want broad transform coverage like albumentations

## Facets
- artifact type: library
- maturity: maintenance
- function: image-processing, machine-learning, data-science
- domain: computer-vision, deep-learning, machine-learning
- platform: python
- tags: data-augmentation, object-detection, bounding-boxes, opencv, tutorial-code

## Member repositories
- Paperspace/DataAugmentationForObjectDetection (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:48.062297+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:32:06.333970+00:00, confidence not recorded.
  - readme: https://github.com/Paperspace/DataAugmentationForObjectDetection (fetched 2026-08-28T04:03:48.062297+00:00, sha ede9bec787d3)
  - homepage: https://blog.paperspace.com/data-augmentation-for-bounding-boxes/ (fetched 2026-08-29T12:37:12.415126+00:00, sha 21a17df104d7)
  - site_page: https://docs.paperspace.com/ (fetched 2026-08-29T12:37:12.424800+00:00, sha a5b08c40101f)
  - site_page: https://www.paperspace.com/about (fetched 2026-08-29T12:37:12.430873+00:00, sha a494c98640cf)
  - site_page: https://www.paperspace.com/pricing (fetched 2026-08-29T12:37:12.426806+00:00, sha c3cd7dfd10cc)
  - site_page: https://updates.paperspace.com/ (fetched 2026-08-29T12:37:12.428948+00:00, sha 239e2109c803)
- Data as of 2026-08-30T08:39:29.467469+00:00.