yolo-object-detection

YOLO Object Detection & Tracking

End-to-end production pipelines for custom training, TensorRT quantization, multi-object tracking (ByteTrack), and real-time inference serving with YOLO.

---

1. Pipeline Architecture

+---------------------+      +------------------------------+      +---------------------------+
| Custom Dataset      | ---> | YOLO Model Training          | ---> | Export to TensorRT        |
| (Roboflow / COCO)   |      | (PyTorch / Ultralytics)      |      | (FP16 / INT8 Calibration) |
+---------------------+      +------------------------------+      +---------------------------+
                                                                                 |
                                                                                 v
+---------------------+      +------------------------------+      +---------------------------+
| Stream Output       | <--- | Real-Time Multi-Object       | <--- | TensorRT Engine           |
| (Bounding Boxes/IDs)|      | Tracking (ByteTrack / BoT)   |      | High-Throughput Inference |
+---------------------+      +------------------------------+      +---------------------------+

---

2. Custom Dataset Definition & Model Training (train_yolo.py)

Dataset YAML Config (dataset.yaml)

path: /data/datasets/manufacturing_defects
train: images/train
val: images/val
test: images/test

names:
  0: scratch
  1: dent
  2: crack

PyTorch Training Pipeline (train_yolo.py)

from ultralytics import YOLO
import torch
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def train_custom_yolo():
    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    logger.info(f"Using device: {device}")

    # Load baseline pre-trained YOLO model (e.g. YOLOv8x or YOLO11x)
    model = YOLO("yolov8x.pt")

    # Execute Distributed Training
    results = model.train(
        data="dataset.yaml",
        epochs=100,
        imgsz=640,
        batch=32,
        device=device,
        workers=8,
        optimizer="AdamW",
        lr0=0.001,
        weight_decay=0.0005,
        val=True,
        save=True,
        project="yolo_defects_project",
        name="experiment_v1"
    )

    # Validate trained model
    metrics = model.val()
    logger.info(f"mAP50-95: {metrics.box.map}")
    logger.info(f"mAP50: {metrics.box.map50}")

if __name__ == "__main__":
    train_custom_yolo()