Below is labeled screenshot of the Deep Learning Trainer which reveals the layout of and purpose behind each user interface element.
Grid spacing to split each image into tiles prior to training. This reduces memory load on the GPU and provides more data to the training algorithm, which tends to produce better results. (Example: Tiles = 3×2 splits each image into 6 sub-images)
2. Size Factor
Resize factor for training images (0-1). This reduces GPU memory load and significantly speeds up training times. A factor down to 0.5 can often be used and still provide adequate precision.
Number of epochs to run training for. The below table offers some recommendations based on your amount of training data:
|Number of Training Sub-images||Epochs|
- GPU: Train model on single GPU (strongly recommended — see Deep Learning System Requirements for more information). Active GPU is set in “GPU Computation” panel in File > Preferences.
- CPU: Train model on the CPU
- Multi-GPU: Train model on multiple GPUs on your local system (only available when more than 1 GPU is detected)
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