This is the latest revision of this page; it has no approved revision.Jump to navigation Jump to search
This page gives you an overview of the terminology and abbreviations you might see in relation with ESRGAN.
General AI Terms
- GAN: Generative Adversarial Network
- An AI network that works by having a generator that tries to fool the discriminator. During training, the generator tries to generate realistic fakes of the training data, while the discriminator tries to tell which images are real and which are fake. Both of these two elements keep improving each other during training.
- Dataset: The collection of data you train your AI model on. For super resolution, it's a pair of low-resolution and high-resolution images.
- Iterations: The amount of times your AI model updates itself with new data. Multiply with your batch size to get the total amount of images that have been processed.
- For example: If your model is at 10,000 iterations with batch size 4, it has seen 40,000 training images.
- Epochs: How often your entire dataset was processed. Important: One epoch does not mean your model has finished training! It can still improve, even after seeing the same data many times.
- Fine-Tuning: Training a model using another model as a "template" instead of training from scratch. You usually do this with ESRGAN models.
- Pretrain Model: The model used for fine-tuning. Should be similar to what you want your fine-tuned model to be, or very neutral.
- Checkpoint: A model that was saved during training.
Hardware and Software
- GPU: Synonymous with "Graphics Card", though this technically only refers to the actual processor chip on your graphics card.
- VRAM: Video RAM, the amount of memory your GPU has. Most AI related applications need as much VRAM as they can get.
- CUDA: Nvidia's software stack that allows all kinds of software to run on your GPU.
- Python: The language most AI applications are written in. The runtime needs to be installed before you can run any python scripts.
ESRGAN Specific Terms
- ESRGAN: Enhanced Super Resolution Generative Adversarial Network (or just Enhanced SRGAN, as SRGAN has already existed before it)
- BasicSR: The training toolkit for ESRGAN, basically just a re-brand of ESRGAN.
- Tile Size: Most ESRGAN implementations split images into tiles to avoid running out of VRAM. The tile size defines how large these tiles are.
- Larger tiles are not automatically better, but they can sometimes avoid seams and slightly speed things up. However, smaller tiles usually work just as well.
ESRGAN Training (BasicSR) Specific Terms
- LR: Low Resolution - The part of your training data that resembles the type of images you want to use your model on.
- HR: High Resolution - The part of your training data that resembles what you want your model to output.
- Augmentation: The process of making your LR images intentionally "worse" in order to make the AI learn to improve them.
- Examples: JPEG compression, dithering, blur, noise
- Batch Size: The amount of images process per training iteration. Higher number means slower training and higher memory usage, but usually better results.
- AMP: Automatic Mixed Precision - Speeds up training on modern GPUs (cards with Tensor Cores like the Tesla V100 or RTX 2080 Ti)
- HR Size: The size of the automatically cropped tiles that get used as training data. As the name implies, it's based on the HR image, so HR size 256 on a 4x scale model means the LR tiles would be 64px.
- The LR size is what impacts the VRAM consumption, so make sure to change this if you change your model's scale. A 64px 1x model will use as much VRAM as a 512px 8x model.
- Validation: The process of "benchmarking" your model during training and calculating certain metrics to help you see how the model performs. This is purely optional and has no impact on the model.
- Discriminator: The part of the AI that tries to tell whether the generator's image is real or fake. BasicSR offers VGG, PatchGAN, and Multiscale PatchGAN.
- Loss Function: Calculates metrics that tell the generator how well it's performing. Used in combination with the GAN (Adversarial) loss, but can also be used by itself, with GAN disabled.
- Learning Rate: How fast the model trains. By default, it slowly decreases in order to make the model more stable with time.