Question 1

How do I install Detectron2 on Windows with CUDA support?

Accepted Answer

Installation on Windows can be tricky; follow the official instructions using conda or pip, but ensure you have compatible PyTorch and CUDA versions. Using Docker is often recommended to avoid dependency issues, as the setup is primarily tested on Linux.

Question 2

Detectron2 vs YOLO for real-time object detection?

Accepted Answer

Detectron2 offers more advanced algorithms like Cascade R-CNN for high accuracy, but YOLO variants are often faster and lighter for real-time edge deployment. Choose Detectron2 for research or production needing state-of-the-art segmentation, and YOLO for speed-critical applications.

Question 3

Can I use Detectron2 for semantic segmentation only?

Accepted Answer

Yes, through integrations like DeepLab, but it's optimized for detection and instance segmentation. For pure semantic segmentation, other libraries might be more streamlined, though Detectron2's modularity allows customization.

Question 4

How to export a Detectron2 model to ONNX format?

Accepted Answer

Detectron2 primarily supports TorchScript and Caffe2 exports; for ONNX, you may need additional conversion steps using PyTorch's built-in tools. Check community resources or extensions, as direct support isn't highlighted in the core features.

Question 5

Is Detectron2 suitable for small datasets or few-shot learning?

Accepted Answer

It can handle small datasets with fine-tuning, but it's designed for large-scale training with pre-trained models. For few-shot learning, you might need to implement custom augmentations or use external modules, as it's not a built-in focus.

Question 6

What are the system requirements for training models in Detectron2?

Accepted Answer

You'll need a modern GPU with at least 8GB VRAM, CUDA support, and sufficient RAM for data loading. Training times vary by model size, but expect high computational costs, especially for transformer-based architectures like ViTDet.

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