YOLO Object Detection

A streamlined implementation of YOLOv2 (You Only Look Once v2) object detection using MLX, optimized for Apple Silicon.

Features

• YOLOv2 architecture with anchor boxes
• Training on PASCAL VOC dataset
• Memory-efficient training
• Real-time object detection capabilities
• Optimized for Apple Silicon using MLX

Model Architecture

• Input resolution: 448x448 pixels
• Backbone: darknet-19
• Output: 7x7 grid with 5 anchor boxes per cell
• Anchor box predictions: (x, y, w, h, confidence)
• Classes: 20 PASCAL VOC classes
• Loss: Multi-part loss function (classification, localization, confidence)

Components

• model.py: YOLO model architecture
• loss.py: Loss function implementation
• simple_train.py: Training script
• simple_inference.py: Inference script
• compute_anchors.py: Utility for computing anchor boxes
• data/: Dataset handling utilities

Training Modes

1. Development Mode:

python simple_train.py --mode dev

• Uses small subset of data (10 images)
• Smaller batch size
• Faster iteration for testing changes

2. Full Training:

python simple_train.py --mode full

• Uses complete dataset
• Larger batch size
• Regular validation and checkpointing

Dataset

The model is trained on PASCAL VOC 2012 with 20 object classes:

• Vehicles: aeroplane, bicycle, boat, bus, car, motorbike, train
• Animals: bird, cat, cow, dog, horse, sheep
• Indoor Objects: bottle, chair, diningtable, pottedplant, sofa, tvmonitor
• People: person