📋
docs.binaexperts.com
  • Introduction
  • Get Started
  • Organization
    • Create an Organization
    • Add Team Members
    • Role-Based Access Control
  • Datasets
    • Creating a Project
    • Uploading Data
      • Uploading Video
    • Manage Batches
    • Create a Dataset Version
    • Preprocessing Images
    • Creating Augmented Images
    • Add Tags to Images
    • Manage Categories
    • Export Versions
    • Health Check
    • Merge Projects and Datasets
    • Delete an Image
    • Delete a Project
  • annotate
    • Annotation Tools
    • Use BinaExperts Annotate
  • Train
    • Train
    • Framework
      • Tensorflow
      • PyTorch
      • NVIDIA TAO
      • TFLite
    • Models
      • YOLO
      • CenterNet
      • EfficientNet
      • Faster R-CNN
      • Single Shot Multibox Detector (SSD)
      • DETR
      • DETECTRON2 FASTER RCNN
      • RETINANET
    • dataset healthcheck
      • Distribution of annotations based on their size relative
      • Distribution of annotations based on their size relative
    • TensorBoard
    • Hyperparameters
    • Advanced Hyperparameter
      • YAML
      • Image Size
      • Validation input image size
      • Patience
      • Rectangular training
      • Autoanchor
      • Weighted image
      • multi scale
      • learning rate
      • Momentum
  • Deployment
    • Deployment
      • Legacy
      • Deployment model (Triton)
    • Introducing the BinaExperts SDK
  • ابزارهای نشانه گذاری
  • استفاده از نشانه گذاری بینااکسپرتز
  • 🎓آموزش مدل
  • آموزش
  • چارچوب ها
    • تنسورفلو
    • پایتورچ
    • انویدیا تاو
    • تنسورفلو لایت
  • مدل
    • یولو
    • سنترنت
    • افیشنت نت
    • R-CNN سریعتر
    • SSD
    • DETR
    • DETECTRON2 FASTER RCNN
  • تست سلامت دیتاست
    • توزیع اندازه نسبی
    • رسم نمودار توزیع
  • تنسوربرد
  • ابرمقادیر
  • ابرمقادیر پیشرفته
    • YAML (یامل)
    • اندازه تصویر
    • اعتبار سنجی تصاویر ورودی
    • انتظار
    • آموزش مستطیلی
  • مستندات فارسی
    • معرفی بینااکسپرتز
    • آغاز به کار پلتفرم بینااکسپرتز
  • سازماندهی
    • ایجاد سازمان
    • اضافه کردن عضو
    • کنترل دسترسی مبتنی بر نقش
  • مجموعه داده ها
    • ایجاد یک پروژه
    • بارگذاری داده‌ها
      • بارگذاری ویدیو
    • مدیریت دسته ها
    • ایجاد یک نسخه از مجموعه داده
    • پیش‌پردازش تصاویر
    • ایجاد تصاویر افزایش یافته
    • افزودن تگ به تصاویر
    • مدیریت کلاس‌ها
  • برچسب گذاری
    • Page 3
  • آموزش
    • Page 4
  • استقرار
    • Page 5
Powered by GitBook
On this page

Was this helpful?

  1. Datasets

Health Check

Assess and improve the quality of your dataset.

PreviousExport VersionsNextMerge Projects and Datasets

Last updated 1 year ago

Was this helpful?

Prefer to learn with video? We have a video tutorial that shows .

Health Check shows a range of statistics about the dataset associated with a project. You can see the following pieces of information:

  • Number of images in your dataset;

  • Number of annotations;

  • Average image size;

  • Median image ratio;

  • Number of missing annotations;

  • Number of null annotations;

  • Image dimensions across your dataset;

  • Object count histogram, and;

  • A heatmap of annotation locations.

Using health check, you can derive a range of insights about your dataset. For example, if you have no null annotations, you may want to consider adding a few depending on the project on which you are working; if there are images with missing annotations, you can dig deeper to add the requisite annotations.

See more on the difference between null and missing annotations.

Class Balance

The health check feature also shows class balance across your annotations. Class Balance shows how many of each object there are and easily visualizes class balance/imbalance. Imbalanced data can yield unfavorable results, especially when measuring models with accuracy.

Here is an example of the class balance feature in use:

how to use the dataset health check to improve model quality