While there has been impressive improvement in recent years in realistic NSFW models, the learning rate still heavily relies on different aspects of the training data quality, model architecture, and computational resources. This, in turn, suggests that training a high-end generative adversarial network with a dataset of 10,000+ images takes about 48 hours on high-performance GPUs such as Nvidia A100. In turn, the amount of information that it needs to acquire such fine details about, say, texture, lighting, and human anatomy for more realistic imagery is huge.
However, the learning process can go much faster in case this training set includes highly curated data. For example, having 500,000 high-quality images instead of 50,000 less-detailed ones may increase its generalization capability more powerfully. In one experiment, the results showed that a model trained with high-definition images, curated to 100,000 entries, resulted in a noticeable improvement in output realism in as little as three weeks. By contrast, training models with a more diverse and less refined set of images could extend the training duration, sometimes requiring 2 to 3 months for equivalent results.
Also, AI models have a tendency to converge faster along with advanced optimization algorithms, including Adam, capable of performing dynamic adjustments in the learning rate. With more powerful hardware, it can result in a saving of time in training, such as by 30-40%. For example, this took about 72 hours, compared to less than 48 hours using two Nvidia 3090 GPUs.
While it goes ahead to improve performance with time, the introduction of pre-trained models accelerates this learning for specific fine-tuning tasks. Depending on how complex these changes are, this could take around 3 to 5 days. Various researchers have found that starting with models pretrained on big, well-studied datasets-for example, ImageNet-14 million labeled images-resulted in considerably better performances after just a few days of training.
Therefore, nsfw ai models can really learn fast, but that again depends on the right mix of data quality, model architecture, and hardware. And how much faster learning would depend on how good this factor could be, with possibly more than 50% less time consumption if high-quality data is put into strong computing.
