Train Object Detection(YOLO) Models

The Object Detection Training Window trains YOLO-based object detection models using your annotated dataset.

Training is best with an Nvidia GPU and properly installed CUDA / cuDNN.

If the training window is disabled or shows missing runtime/dependency errors, open the Module Downloader Window and install the required AI tools.

Getting Started

1. Launch AugeLab Studio.

2. Open AI Tools → Object Detection Training Window

3. Prepare:

   • a dataset folder and class names file prepared with AugeLab Studio’s Annotation Tool

   • or a dataset folder containing images and YOLO label files (.txt) in the same folder

   • a class names file in .names format (one class name per line)

The training window scans your dataset and shows Dataset Analytics (total images, annotated/unannotated counts, and the class list) to help you catch mistakes early.

1) Configuration

The current training window uses a simple Configuration panel instead of a menu-only workflow.

Select Dataset Folder

Choose the folder that contains your training images.

Notes:

• Only files in this folder are used (keep your training images in one folder)

• Supported image extensions include: .jpg, .jpeg, .png, .bmp, .tiff, .tif

Select Class Names File (.names)

Choose your class list file.

If the .names file is empty, the training window will treat it as an error. Make sure it contains one class name per line.

Model Type

Choose a model variant from Model Type.

In general:

• Robust Ones variants are slower but can reach higher accuracy, YOLOv4-Scaled is a good default

• Fast variants train faster and are easier on low-spec PCs

• Micro / Nano are designed for very small / edge-device style models

Optional: Custom Weights

You can start from custom/pretrained weights (Darknet .weights / backbone .conv.*).

Good use cases:

• Continuing a previous run

• Faster convergence on similar datasets

If you change Model Type, it’s safest to clear and re-select weights that match the chosen model.

2) Advanced Settings

Advanced Settings allows you to tune training behavior (memory use, speed, and accuracy).

The most important settings:

• Dataset Split Ratio (Train/Val): how much data is used for validation (affects mAP reporting)

• Network Input Size (Width/Height): bigger can help small objects, but uses more VRAM and slows training

• Batch Size / Subdivisions: main knobs for GPU memory errors

  • If you see “Out of Memory”, increase subdivisions or decrease batch size

• Recalculate Anchors: can improve results on custom datasets (recommended for new datasets)

• Calculate Optimal Network Size: optional auto-selection helper

• GPUs to Use: for multi-GPU systems (e.g., 0 or 0,1)

• mAP During Training: shows accuracy progress but can slow training a bit

• Clear Previous Training: start fresh vs. resume

• Live Augmentation Options: applies on-the-fly variations during training (does not create extra files)

3) Start / Stop Training

Once Dataset + Classes are valid, the main button becomes active.

1. Click Start Training

2. Monitor:

   • the Log area (console output)

   • the Training Chart window (loss / mAP)

3. Click Stop Training to terminate the process

Training Logging

Closing the training window while training is running will terminate the training process.

After Training

When training finishes (or you stop it), check the output directory referenced in the log/status messages.

Next steps:

• Load your trained model into your workflow (inference)

• Validate results on a holdout set or real camera footage