FAQ#
Glossary#
Object Detection FAQ#
General Object Detection#
Q: What is object detection?
A: computer vision task that identifies and locates objects in images using bounding boxes.
Q: What are common challenges?
A: Small/occluded objects, class imbalance, limited data, and computational cost for training/real-time detection.
Q: How do I select a model?
A: Start with YOLO for speed and ease of use. Pretrained models help with small datasets and startup.
Q: How do I measure performance?
A: Use mAP (mean Average Precision) and IoU (Intersection over Union). Higher mAP = better detection.
Q: What hardware is needed?
A: GPUs (NVIDIA, 8GB+ VRAM) speed up training. CPUs work but are slower. Use cloud if needed.
AI4ME Cookbook-Specific FAQs#
Q: How do I set things up?
A: Clone the repo, install dependencies (pip install -r requirements.txt), and follow the notebooks.
Q: Common errors and fixes?
A: Missing modules (pip install needed), incorrect file paths, or insufficient GPU memory.
Q: Can I use my own dataset?
A: Yes, format it properly (e.g., COCO/YOLO format) and update the training scripts accordingly.
Python for R Users FAQs#
Q: How does Python compare to R for ML?
A: Python is better for deep learning and production, while R excels at statistics and visualization.
Q: What Python libraries are used for detection?
A: TensorFlow, PyTorch, Ultralytics YOLO, OpenCV for preprocessing, and several others.
Q: How do pandas and dplyr compare?
A: Pandas is Python’s equivalent of dplyr, using methods like .groupby(), .apply(), and .filter().
Q: How do I debug Python if I’m used to R?
A: Read error tracebacks, use print(), check 0-based indexing, and verify library installations.