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Project Overview
After conducting comprehensive model research, a real-time image processing and classification system was developed using the YOLO (You Only Look Once) algorithm. The project involved creating a specialized training set comprising images from various categories including elephants, airplanes, and dinosaurs.
Technologies & Tools
PythonPyTorchYOLOv8OpenCVNumPyPillowONNXTensorRTDockerFastAPILabelImg
Key Features
Real-time object detection and classification
92% detection accuracy across multiple object classes
Support for various object categories (elephants, airplanes, dinosaurs, and more)
Custom-trained YOLO model optimized for specific use cases
Bounding box visualization with class labels and confidence scores
Batch processing capability for multiple images
Video stream processing for real-time detection in video feeds
Configurable confidence threshold for precision/recall trade-off
Non-maximum suppression to eliminate duplicate detections
Model export in .pt format for easy deployment
GPU acceleration support for improved performance
REST API for easy integration with other applications
This YOLO-based object detection project demonstrates the practical application of state-of-the-art computer vision algorithms for real-time image analysis and classification. By leveraging the YOLO (You Only Look Once) architecture, renowned for its exceptional balance between speed and accuracy, this system can process images and identify objects in milliseconds while maintaining an impressive 92% accuracy rate. The project encompasses the complete machine learning pipeline from data collection and annotation to model training, optimization, and deployment. The custom training set, carefully curated with diverse examples of target object classes, ensures robust performance across various lighting conditions, angles, and backgrounds. Beyond simple object detection, the system implements advanced features like confidence thresholding, non-maximum suppression for handling overlapping detections, and multi-class classification. The final model, saved in PyTorch .pt format, is optimized for production deployment and can be easily integrated into various applications requiring real-time object detection capabilities.
Technical Deep Dive
YOLO Architecture
Implements YOLOv8, the latest iteration of the YOLO family, featuring improved backbone network with CSPDarknet53, enhanced neck with PANet for better feature fusion, and optimized detection head for multi-scale predictions. The architecture processes images in a single forward pass, dividing them into an SxS grid and predicting bounding boxes and class probabilities for each grid cell.
Training Dataset
Curated a custom dataset of 5,000+ annotated images across target object classes. Images collected from diverse sources ensure model generalization. Each image annotated with precise bounding boxes and class labels using LabelImg tool. Dataset split into 70% training, 20% validation, and 10% test sets. Applied data augmentation techniques including random crops, flips, rotation, and color jittering to improve model robustness.
Model Training Process
Training conducted using PyTorch framework with transfer learning from COCO pre-trained weights. Implemented custom loss function combining localization loss (CIoU), confidence loss (BCE), and classification loss (BCE). Used AdamW optimizer with cosine annealing learning rate schedule. Training for 300 epochs with early stopping based on validation mAP. Achieved 92% mAP@0.5 on test set.
Optimization & Deployment
Model quantization and pruning techniques applied to reduce model size by 40% while maintaining accuracy within 1% of original. Exported model to ONNX format for cross-platform compatibility. Implemented TensorRT acceleration for NVIDIA GPUs achieving 200+ FPS on RTX 3080. Docker containerization ensures consistent deployment across environments.
Inference Pipeline
Pre-processing includes image resizing to 640x640, normalization, and conversion to tensor format. Post-processing applies confidence thresholding (default 0.25), non-maximum suppression (IoU threshold 0.45), and class filtering. Results include bounding box coordinates, class labels, and confidence scores. Visualization module overlays detections on original images with colored boxes and labels.
Screenshots & Visuals
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