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 = ...

Image Classification: Fine-Tuning ResNet-18 on Kaggle's Lions vs Cheetahs Dataset Image classification is a fundamental task in computer vision where the goal is to assign a label or class to an input image. It is widely used in various domains such as medical imaging, autonomous driving, wildlife monitoring, and security. A typical image classification pipeline involves feeding an image into a neural network model, which processes the input and outputs class probabilities corresponding to predefined categories. What is the ImageNet Dataset? ImageNet is one of the most influential datasets in the history of computer vision. It contains over 14 million labeled images across more than 20,000 categories, with a popular subset of 1,000 categories used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Models trained on ImageNet learn powerful visual features that generalize well to many downstream tasks, making them a popular choice for transfer learning an...

Introduction to Semi-Supervised Learning Semi-Supervised Learning is a deep learning technique that utilizes a small amount of labeled data and a large amount of unlabeled data. Traditional Supervised Learning uses only labeled data for training, but acquiring labeled data is often difficult and time-consuming. In contrast, Semi-Supervised Learning improves model performance by utilizing unlabeled data, achieving better results with less labeling effort in real-world scenarios. This approach is particularly advantageous in computer vision tasks such as image classification, object detection, and video analysis. When there is a lack of labeled data in large-scale image datasets, Semi-Supervised Learning can effectively enhance model performance using unlabeled data. Technical Background: The core techniques of Semi-Supervised Learning are  Consistency Regularization  and  Pseudo-labeling . Consistency Regularization encourages the model to make consistent predictions on au...