The article provides a comprehensive walkthrough of YOLOv1, the pioneering real-time object detection model introduced in 2015 by Joseph Redmon and colleagues in the paper "You Only Look Once: Unified, Real-Time Object Detection." Unlike earlier methods such as R-CNN, which relied on a multi-stage process involving region proposals, feature extraction, and classification, YOLOv1 treats object detection as a single regression problem. This unified approach enables significantly faster inference while maintaining competitive accuracy. At its core, YOLOv1 divides an input image into a grid of S×S cells—typically 7×7—where each cell is responsible for predicting bounding boxes and class probabilities if an object's center falls within that cell. For each cell, the model predicts two bounding boxes, each defined by five values: the x and y coordinates of the box’s center (relative to the cell), the width and height (also relative to the cell), and a confidence score indicating how likely the box contains an object. Additionally, the model predicts class probabilities for each cell, resulting in a total of 30 values per cell (C classes, 2 boxes × 5 values each, plus confidence). The target vector for each cell is structured to match this output. It includes one-hot encoded class labels (20 for PASCAL VOC), confidence, and the five parameters for each of the two predicted boxes. The full ground truth for an image is formed by concatenating these vectors across all 49 cells into a 30×7×7 tensor. The network architecture is built around a CNN backbone with 24 convolutional layers, followed by two fully connected layers. The backbone uses a series of ConvBlock modules, each consisting of a convolutional layer, a leaky ReLU activation (with a negative slope of 0.1), and optionally a max pooling layer for downsampling. The model starts with a 7×7 convolution and progressively reduces spatial dimensions through strategic strides and pooling, ultimately producing a 1024×7×7 feature map. This feature map is flattened and passed through two fully connected layers: the first maps to 4096 neurons with a leaky ReLU activation and dropout (rate 0.5), and the second outputs 1470 values (30×7×7), which are reshaped during post-processing into the final prediction tensor. The implementation in PyTorch mirrors the original design, with modular components for the backbone and fully connected layers. Testing with a dummy input confirms the expected tensor dimensions at each stage, validating the architecture's correctness. The article also notes that while the full YOLOv1 is computationally heavy, a lighter version called Fast YOLO was proposed, though details are sparse. The author encourages readers to experiment with the model, including using alternative backbones like ResNet or ViT, and to consider simplifying the network for training feasibility due to the original model's long training time. The code for the implementation is available on GitHub, and the article concludes with a call for feedback and a note on the model’s historical significance. YOLOv1’s real-time performance and unified detection framework laid the foundation for subsequent versions, making it a landmark in computer vision.