AI Practitioner

Image Classification with ResNet-18: Advanced Training Strategies and Inference Pipeline This article is a follow-up to the previous guide, " Image Classification: Fine-tuning ResNet-18 on Kaggle Dataset (Pytorch + Lightning) ".  I recommend reviewing the previous post before proceeding. 1. Hyperparameter Configuration The performance of a deep learning model is highly influenced by the choice of hyperparameters. Below are some key hyperparameters that are commonly tuned: Learning Rate: Controls the step size during training. Commonly set between 1e-3 and 1e-5. Batch Size: Number of images processed in a single iteration. Adjust based on GPU memory. Epoch: Number of full passes through the entire training dataset. Optimizer: Algorithm used to update model parameters (e.g., Adam, SGD). Scheduler: Gradually adjusts the learning rate as training progresses. # Example hyperparameters BATCH_SIZE = 32 EPOCHS = 10 LEARNING_RATE = 0.0001 MODEL_PATH = ...