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+**1. Algorithm Choice**  
+- **Selected:** YOLOv8n (lightweight version)  
+- **Why:**  
+  - Fast detection (0.5s/image on CPU)  
+  - Works well with small datasets (40 images)  
+  - Accurate for motherboard components  
+
+**2. Hardware Impact**  
+- **Training:**  
+  - GPU recommended (4x faster training)  
+  - CPU works but slower  
+- **Deployment:**  
+  - CPU sufficient for basic use  
+  - GPU better for high volume  
+
+**3. Video Handling**  
+- **Approach:** Process each frame individually  
+- **Changes Needed:**  
+  - Add frame-by-frame processing  
+  - Include tracking to follow memory modules  
+  - Optimize for speed (lower resolution helps)  
+
+**Key Facts:**  
+- Same model works for images/video  
+- CPU processing is practical  
+- No architecture changes needed between image/video modes.