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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
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    • Framework
      • Tensorflow
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      • TFLite
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      • 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
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  • 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
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  • 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
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  • How to Create a Dataset Version
  • Adjusting Train/Validation/Test Splits

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  1. Datasets

Create a Dataset Version

Creating a dataset version allows you to prepare your data for training a model.

PreviousManage BatchesNextPreprocessing Images

Last updated 8 months ago

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A dataset version is a point-in-time snapshot of your dataset. By keeping track of the exact images, preprocessing steps, and augmentations used in each model iteration, you maintain the ability to reproduce results. This ensures that you can scientifically test various models and frameworks while confidently attributing results to model changes, not to bugs or changes in the data pipeline.

Once a version is created, it is frozen in time. This means that any subsequent changes to the project (such as adding or removing images, annotations, or other data) will not affect previously created versions.

How to Create a Dataset Version

To create a dataset version:

I. Click "Versions" in the sidebar of your project.

II. Click "Build New Version".

From this page, you can:

· Set the train/test/validation split.

· Specify preprocessing steps.

· Define augmentations for your new dataset version.

Adjusting Train/Validation/Test Splits

During the version creation process, you can readjust the balance of your training, validation, and test sets. To do this:

· Go to "Step 4: Train/Test Split".

· Click on the option to adjust the split settings.

After specifying the preprocessing steps and augmentations you want to apply, click "Build Train-Ready Version". This will generate a new dataset version. You can then use this dataset version to train a model in or export it for manual model training.

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