Computer · Data Science
Computer Vision: Real-Time Object Detection (YOLOv8)
Intermediate 6 weeks 40-60 hours
Train YOLOv8 on custom dataset. Deploy on edge device (Jetson Nano/RPi). Achieve real-time inference. Build web interface showing live detections.
Major
Computer
Focus area
Data Science
Total hours
40-60 hours
What you'll need
- Software only - Python, YOLOv8, Ultralytics, free GPU (Colab), OpenCV
Steps
- Collect and label a custom image dataset.
- Train YOLOv8 on free GPU time in Google Colab.
- Evaluate accuracy on a test set.
- Deploy the model to a Raspberry Pi or Jetson Nano.
- Build a web interface that shows live detections.
- Record a demo and document the results for your portfolio.
What to photograph for your portfolio
- Object detection video
- accuracy metrics (mAP)
- real-time FPS measurement
- confusion matrix
- edge device test
Resume bullet starters
Copy one, then swap in your own numbers.
Designed and built a computer vision: real-time object detection using Python, TensorFlow/PyTorch, computer vision
Applied edge deployment and dataset annotation to implement, test, and validate the system end-to-end
Quantified performance with [insert your result, such as accuracy, error reduction, response time, or load capacity] after iterative tuning
Skills you'll show off
PythonTensorFlow/PyTorchcomputer visionedge deploymentdataset annotationweb dev