AI Practitioner

D-FINE is a cutting-edge algorithm developed to overcome the limitations of existing Transformer-based object detection models (DETR series), particularly in bounding box regression and slow convergence. This article focuses on D-FINE’s core mechanisms— Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD) —and provides a detailed analysis of its architecture, technical contributions, performance benchmarks, and a comparison with YOLOv12. 1. Background and Motivation DETR (Detection Transformer) was revolutionary for eliminating anchors and non-maximum suppression (NMS) from object detection pipelines. However, it introduced several challenges in real-world applications: Extremely slow convergence Inefficient direct regression of bounding box coordinates Limited real-time applicability without high-end hardware D-FINE retains the Transformer backbone but enhances the bounding b...

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 = ...

Why Image Augmentation is Essential in Deep Learning In computer vision, image augmentation plays a critical role in improving the generalization of deep neural networks. By artificially expanding the diversity of the training dataset through transformations that preserve the label, image augmentation helps reduce overfitting and increases model robustness. Especially for convolutional neural networks (CNNs) and vision transformers (ViTs), which learn hierarchical and spatial features, input variability introduced by augmentation forces the model to learn more invariant and meaningful representations. This is analogous to improving the mutual information between relevant features and output predictions while discarding noise. Common Image Augmentation Techniques and Parameter Descriptions 1. RandomHorizontalFlip Purpose:  Introduces horizontal symmetry by flipping the image left-to-right with a certain probability. from torchvision.transforms import v2 as transforms transform ...

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...