Running TensorFlow SSD MobileNet v2 with OpenCV DNN: Setup, Speed‑up, and Gotchas
Learn how to import a TensorFlow SSD MobileNet v2 model into OpenCV’s DNN module, run inference on CPU or CUDA GPU, and verify performance and compatibility.
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tensorflowCPU vs GPU Image Resizing in OpenCV: A Practical Decision Guide
Decide when to use OpenCV’s CPU or GPU resize: compare CPU, CUDA, and OpenCL options, weigh speed vs. portability, and see a ready‑to‑run C++ example with validation checks.
OpenCV DNN strict backend selection without silent CPU fallback
OpenCV DNN strict backend selection without silent CPU fallback Goal: configure cv::dnn::Net so that requesting DNN_BACKEND_CUDA with DNN_TARGET_CUDA fails explicitly when CUDA support is absent or no CUDA device is available, instead of silently falling back to the default OpenCV CPU backend. Constraint: setPreferableBackend and setPreferableTarget currentl
Fork or Spawn: Which Start Method Should a CUDA DataLoader Pipeline Standardize On?
GPU training with DataLoader(num_workers>0) carries a start-method decision PyTorch leaves to the user. Python's long-standing Linux default, fork , starts workers almost instantly and lets them inherit parent memory, including CUDA state. spawn isolates each worker in a fresh interpreter, re-importing the module and re-initializing CUDA per worker. That
pytorchOpenCV DNN CUDA Backend Selection Without Compile-Time Backend Support
OpenCV DNN CUDA Backend Selection Without Compile-Time Backend Support The goal is to confirm that cv::dnn::Net uses a CUDA backend and target in deployment after setPreferableBackend and setPreferableTarget are called. Inference may fail or run slower than expected without revealing whether GPU acceleration was active. OpenCV honors a CUDA request only when
OpenCV CUDA Backend Detection and Runtime Device Availability
Problem Statement OpenCV's CUDA acceleration capabilities depend on both the build configuration and runtime hardware detection. The library provides cv::cuda::getCudaEnabledDeviceCount() to check GPU availability, but this function may return zero even when compatible CUDA hardware exists in the system. Constraints and Uncertainties The detection mechanism
Limits of PyTorch DataLoader multiprocessing start‑method for CUDA workloads
Goal: assess whether the forkserver start method can be employed with PyTorch DataLoader workers to avoid per‑worker CUDA context re‑initialization while maintaining safe GPU usage in distributed training. Constraints: the official documentation warns that fork‑ing a process holding CUDA resources is unsafe, and there is no stable API for forkserver in PyTor
Google Colab runtime and PyTorch CUDA memory: should cleanup be automatic after cell execution?
Context I'm designing a training workflow that runs on a Google Colab GPU runtime (Tesla T4 class) using PyTorch. The two components involved are the Colab hosted runtime, which owns the GPU session lifecycle, and PyTorch's CUDA caching allocator, which manages device memory within the process. The interoperability question is where responsibility for releas
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