Computer Vision Foundations
4 reading notes
Build a foundation for reading methods papers through classic vision architectures, training choices, and evaluation evidence.
Reader question
Which design choices and evidence from a classic model still transfer to today’s systems?
Reading library
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Every paper reading currently filed under this research topic.
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ResNet: Residuals Make Depth Trainable, but ImageNet 2015 Is Not a Ready-Made Detection or ViT Contract
Intermediate Build the foundations firstA source-grounded reading of He et al., CVPR 2016 / arXiv:1512.03385: identity shortcuts let stacked layers learn residual F(x)+x and fix plain-net degradation. ResNet-152 reaches 4.49% top-5 validation error on ImageNet; this is 2015 classification evidence, not a YOLO, ViT, or modern ConvNet leaderboard contract.
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YOLO: Detect From One Full-Image Pass, but VOC 2016 Does Not Represent the Later YOLO Family
Intermediate Build the foundations firstA source-grounded reading of Redmon et al., CVPR 2016 / arXiv:1506.02640: object detection as a single forward-pass regression—an S×S grid, B boxes, and C class probabilities in one shot. On VOC 2007, YOLO reaches 63.4% mAP at 45 FPS; this is 2016 unified-detection evidence, not YOLOv3 COCO or Ultralytics product numbers.
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AlexNet Part 2: Turning the Training Recipe into Testable Design Choices
Intermediate Build the foundations firstA source-grounded reading of ReLU, multi-GPU splitting, overlapping pooling, augmentation, and dropout in Figure 1–3 and Sections 3–6.
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AlexNet Part 1: Reading the Evidence Behind an ImageNet Turning Point
Introductory Build the foundations firstA source-grounded rereading of AlexNet’s problem, evaluation, historical result, and evidence boundary.
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