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Train Custom AI Models with Training Window
You will need to have a computer with Nvidia GPU, should install CUDA, CUDNN, and use Module Downloader Window.
The AugeLab Studio Detection Training window provides a user-friendly interface to train object detection models using the YOLO (You Only Look Once) framework. With this feature, you can train your models on custom datasets and customize various training configurations.
- 1.Open AugeLab Studio: Launch AugeLab Studio on your computer.
- 2.Access Detection Training: Draw and drop Object Detection Train block and click on "Open Train Window".
- 3.Dataset and Class File: Before you start training, you need to load the dataset folder and the class file that contains the classes for the objects you want to detect.
You can integrate pretrained YOLO weights with class names as well.
- 1.Load Dataset: Click on this action to select the dataset folder containing images for training.
- 2.Load Class File: Use this action to load the class file (
.namesformat) that contains the list of classes for training.
- 3.Detection Model Type: Choose between "Fast Detection" and "Robust Detection" models. The "Fast Detection" model is suitable for low-spec PCs and offers lower accuracy. The "Robust Detection" model provides higher accuracy but requires high-spec PCs.
- 4.Define Batch Size: Select the batch size for training, which affects GPU memory usage. You can choose from batch sizes of 16, 32, or 64.
- 5.Load Custom Training Checkpoint: If you have a pre-trained weight file (
.weightsor.pwformat), you can load it to use as a starting point for training.
The "Train Procedure" action under the "Help" menu provides detailed instructions on how to train the models effectively.
- 1.Detection Model Type: Choose between "Fast Detection" and "Robust Detection" models.
- 2.Batch Size: Select the batch size (16, 32, or 64) based on your GPU memory usage and hardware specifications.
- 1.Start Train: After loading the dataset and class file, click on "START TRAIN" to initiate the training process.
- 2.Train Log: The log area will display the progress and status of the training process.
- 3.Stop Train: You can stop the training process by clicking on the "STOP TRAIN" button.
You can follow training process from two screens:
- 1.Training Logging Window
Training Logging
- 2.Training Chart (Loss and mAP)
If you close the Detection Training window while the training process is running, the process will be terminated.
You can now use the Detection Training window in AugeLab Studio to train object detection models on custom datasets. Experiment with different model types, batch sizes, and custom checkpoints to achieve optimal results! Enjoy training your object detection models with AugeLab Studio!