---
license: cc0-1.0
library_name: onnx
tags:
  - billiards
  - trajectory-prediction
  - onnx
  - cuedetat
  - flow-poke
---

# CueDetat MYRIAD — Billiards Trajectory Student

Distilled "Flow-Poke" student model used on-device by the CueDetat Android app to predict a
billiard ball's trajectory from a top-down table image and a poke. Archived for preservation;
the app has since removed the feature.

## Files
- `myriad_billiard.onnx` — FP16, ~466 KB. Exact file that shipped in the app (`assets/`).
- `myriad_student_fp32.onnx` (+ `myriad_student_fp32.onnx.data`) — FP32 source in ONNX
  external-data format; keep both files together.

## Inputs / Outputs
- `image`: Float32[1,3,128,128], CHW, normalized to [-1, 1].
- `poke`:  Float32[1,4] = (x, y, dx, dy); position normalized [0,1], displacement ≈ 0.02–0.075.
- `trajectory` (output): Float32[1,30,2] = 30 (x,y) points of the poked (ball-0) ball, normalized [0,1].

## Expected input frame (top-down synthetic)
Grey surround RGB(128,128,128); white table rectangle RGB(255,255,255) inset 6–12 px; balls as
solid circles, radius ≈ 4 px (0.0333·128); the **poked ball red** (255,0,0), all others black (0,0,0).

## Provenance
- Teacher: [CompVis/flow-poke-transformer](https://github.com/CompVis/flow-poke-transformer) (billiards physics).
- Distillation dataset: Kaggle `hereliesaz/cuedetat-trajectories` (~50k image/poke/trajectory tuples).
- Architecture: CNN(image)→128 ⊕ MLP(poke)→64 → MLP[256,128] → [30,2].
- License: CC0-1.0.