Topic · Tasks

Image classification

Everything on this site about image classification, in the order to read it: a page is listed after the pages it builds on. The figures below are the ones those pages use to explain it; each is live: move a slider and it recomputes.

In reading order

  1. Paper · read first: Attention Meets Pixels — Windows, Axes and the First Fully Attentional Networks, Attention Is All You Need — The Transformer, Block by Block, DETR — Detection as Set Prediction, No Anchors and No NMS
  2. Paper
  3. Paper
  4. Paper
  5. Paper
  6. Paper · read first: Face Spoofing Detection in Python
  7. Project
  8. Project

The figures these pages use

Pulled from the pages above. Move a slider and the figure recomputes, exactly as on its page.

From An Image Is Worth 16×16 Words — ViT, Patch by Patch › 2. Patch Embedding, the Class Token and Positions

16224
An image as a sequence of patches. Each cell is one patch, i.e. one token. Shrink P and the sequence grows as 1/P²; the attention term grows as 1/P⁴ but stays a small share of the layer until P reaches 4. Raise the image side with P fixed, as ViT does at fine-tuning time, and N grows while the patch vector does not.

From An Image Is Worth 16×16 Words — ViT, Patch by Patch › 6. Data Scale Against Inductive Bias

6.12.9
Data first, then size. Left: Table 5 of the paper, ImageNet accuracy after pre-training on 1.3 M, 14 M and 300 M images; on ImageNet alone the Large model is below the Base one. Right: Table 6, the best accuracy reachable under a pre-training budget; the hybrid leads at small budgets, the two meet at 87.12 % near 1,600 exaFLOPs, and only ViT-H/14 goes past it. Move the two sliders and read the gap between the two ViT sizes and what a ResNet needs to match a ViT.

From Simple, Powerful, and Fast— RegNet Architecture from Facebook AI › Step 3. Making of RegNet Models — RegNetX and RegNetY

24362.5
From a straight line to stages. The line u_j rises steadily; rounding s_j turns it into a staircase, and each step of the staircase is one stage of the network.

From Simple, Powerful, and Fast— RegNet Architecture from Facebook AI › Step 3. Making of RegNet Models — RegNetX and RegNetY

24362.513
RegNet widths. One bar per block. Blocks of equal width form a stage; the line u_j is quantized into steps.

Tags that go with it

Backbone design 2Semi-supervised learning 2Domain adaptation 1Faces 1Medical imaging 1Pretraining at scale 1Self-supervised learning 1Vision transformers 1