Topic · Efficiency

Edge deployment

Everything on this site about edge deployment, 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: RT-DETR — DETRs Beat YOLOs, Encoder by Encoder
  2. Project
  3. Project
  4. Project

The figures these pages use

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

From RT-DETRv2 — A Bag of Freebies for the Decoder

1 / 5 · v11 / 8 · whole pipeline
One pipeline, five versions. The RT-DETR pipeline for a 640 × 640 image, block by block. Step through the stages to see each block's output shape; switch the version to see which blocks it changes (rust outline), and switch to training to see the dashed parts that exist only while the model learns and are removed at inference.

From RT-DETRv2 — A Bag of Freebies for the Decoder › 2. Points per Scale

(0.45, 0.4)
Point budget
4441 / 5 · S (R18)
Reading between cells. A toy 6 × 6 feature map. Drag the rust point: the bilinear read mixes the four cells around it with the weights shown, and the blue arrow is the way its slope pushes the point during training. Switch to the nearest-cell read: one cell, weight 1, and along the row the value becomes a staircase whose slope is 0, so the offsets get no signal. The point-budget sliders count the sampling points per image; Table 3 of the paper measured only equal counts, on the S model.

From RT-DETRv2 — A Bag of Freebies for the Decoder › 6. Results

5 / 5 · the RT-DETR line1 / 4 · v1
Which families to show
Speed against accuracy, one table at a time. Latency on a T4 GPU against COCO val2017 AP, as each paper reports it. Step through the tables: every view is one paper's own comparison, because the papers time the same models differently. Dotted verticals join two models that run the same network at the same cost.

Tags that go with it

Object detection 3Efficient attention 1Pruning 1Quantization 1Traffic analytics 1Training recipes 1