Understanding num_workers
Deep dive into PyTorch DataLoader num_workers parameter: how parallel workers prefetch data, optimal configuration, and common pitfalls.
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ML Concepts - Page 26
Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
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Concepts - Page 26
Pinned Memory and DMA Transfers
Understanding PyTorch pin_memory for faster CPU to GPU data transfers using DMA (Direct Memory Access) and page-locked memory.
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ASFF: Adaptive Spatial Feature Fusion
Learning where to fuse multi-scale features with per-pixel, per-level fusion weights. ASFF challenges FPN's uniform fusion assumption.
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RoI Pooling, RoI Align & Deformable RoI Pooling
Understanding region-based feature extraction for object detection, from quantized pooling to sub-pixel alignment and adaptive sampling
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Anchor-Based vs Anchor-Free Object Detection
Compare anchor-based vs anchor-free object detection: Faster R-CNN and RetinaNet anchors vs FCOS and CenterNet point-based methods.
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NAS-FPN: Learning to Design Feature Pyramid Networks
Understanding how neural architecture search discovers optimal feature pyramid architectures that outperform hand-designed alternatives
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Showing 151-156 of 174 articles