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MASTER_POOL_MODEL.tflite ADDED
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+ MODEL_0_OFFSET=0
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+ MODEL_1_LENGTH=6242869
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README.md ADDED
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+ ---
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+ license: agpl-3.0
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+ library_name: tf-lite
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+ tags:
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+ - object-detection
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+ - yolov8
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+ - billiards
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+ - tflite
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+ - cuedetat
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+ ---
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+
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+ # CueDetat MASTER_POOL_MODEL — Pool Table / Ball Detector
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+
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+ The TFLite detector used by the CueDetat Android app for pocket/table and ball/cue detection.
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+ YOLOv8n, 640×640 input, FP16, NMS in-graph.
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+
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+ ## Files
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+ - `MASTER_POOL_MODEL.tflite` — a single binary that **concatenates four sub-models**.
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+ - `MASTER_POOL_MODEL.tflite.meta` — byte offsets/lengths for each segment (required to load):
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+ `MODEL_i_OFFSET` / `MODEL_i_LENGTH`. The app maps each segment to its own interpreter.
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+ - `metadata.yaml` — Ultralytics export metadata (classes, imgsz, stride).
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+
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+ ## Segments the app actually runs
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+ - Segment 0 — pocket/table detector. Output: `[1, N, 6]` rows `(y1, x1, y2, x2, score, classId)`.
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+ - Segment 2 — pool ball / cue detector. Same row layout.
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+ - (Segments 1 and 3 exist in the file but are unused by the app.)
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+
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+ ## Classes (from `metadata.yaml`)
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+ `0: pool-table 1: pool-table-hole 2: pool-table-side`
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+ (plus ball/cue classes used by the pool head: ball, cue).
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+
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+ ## Notes
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+ - Input image normalized to [0,1], RGB, 640×640.
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+ - License: AGPL-3.0 (Ultralytics-derived).
metadata.yaml ADDED
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+ description: Ultralytics best model trained on /kaggle/working/combined_dataset/data.yaml
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+ author: Ultralytics
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+ date: '2026-05-13T07:47:26.724447'
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+ version: 8.4.49
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+ license: AGPL-3.0 License (https://ultralytics.com/license)
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+ docs: https://docs.ultralytics.com
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+ stride: 32
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+ task: detect
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+ batch: 1
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+ imgsz:
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+ - 640
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+ - 640
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+ names:
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+ 0: pool-table
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+ 1: pool-table-hole
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+ 2: pool-table-side
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+ args:
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+ data: null
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+ batch: 1
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+ fraction: 1.0
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+ half: true
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+ int8: false
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+ nms: true
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+ channels: 3
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+ end2end: false