jim800121chen
3f0175f1a9
從 local-tool 複製出獨立的「visionA Agent」桌面應用（A3 純橋樑： tunnel client + 配對 UI + 設定，不開 HTTP port、不做本機裝置/推論 UI）。 Bundle ID 與 local-tool 不同（com.innovedus.visiona-agent vs visiona-local）， 雙 app 可共存。fork 後不主動 sync，需要時手動 cherry-pick。 Backend / Wails Go（AB1-AB13）： - internal/tunnel：6 狀態機（Idle/Connecting/Connected/Reconnecting/Failed/Stopped） + Pair/Unpair/Reconnect/Disconnect binding + ClientHooks event - internal/auth：encrypted file token store（AES-GCM + scrypt + machineID fallback salt + 13 tests） - internal/config：YAML validation + atomic write + 11 tests - internal/log：ring buffer + ExportLog 升級 zip - visionA-backend /api/pairing/exchange：SessionTokenStore + 17 new tests - 三平台 build 驗證（macOS DMG 160 MB / Windows EXE / Linux AppImage） - end-to-end 5 milestone 全綠（pairing → tunnel → forward → reuse 防護 → tunnel drop failover） Frontend / Next.js（AF1-AF7，沿用 visionA-frontend 基礎）： - AppShell + Header + TabNav（StatusView / PairView / SettingsView 三 tab） - ConnectionStatusBadge 5 種狀態 - TokenInput regex 驗證 + 7 種錯誤 + 0.5s auto-switch 到狀態頁 - 設定頁 4 區塊（含重新配對 AlertDialog） - agent-api.ts 封裝 Wails bindings（mock/real 雙實作）+ 90 tests Phase 0.7 review-driven fix（Round 2）： - A1 Session fixation 防護（RotateSessionID） - A3 mock pairing 預設改 false（必須明確 opt-in）+ startup log - A4 Pair 失敗後 state 清理矩陣（exchange/Save/Start fail 各自終態） - A5 Pair/Unpair/Reconnect lifecycleMu + 50 goroutine race test - F1 重新配對次按鈕 / F2 PairView Esc cancel / F3 Wails BrowserOpenURL / F4 Settings draft 持久 + 未儲存 badge 驗證：agent backend go test -race -count=3 ./... 4 packages 全綠 / agent frontend pnpm test 119 tests 全綠 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
267 lines
6.7 KiB
JSON
267 lines
6.7 KiB
JSON
[
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{
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"id": "kl520-yolov5-detection",
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"name": "YOLOv5 Detection (KL520)",
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"description": "YOLOv5 object detection model compiled for Kneron KL520. No upsample variant optimized for NPU inference at 640x640 resolution.",
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"thumbnail": "/images/models/yolov5-det.png",
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"taskType": "object_detection",
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"categories": [
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"general",
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"multi-object"
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],
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"framework": "NEF",
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"inputSize": {
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"width": 640,
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"height": 640
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},
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"modelSize": 7200000,
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"quantization": "INT8",
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"supportedHardware": [
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"KL520"
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],
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"labels": [
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"person",
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"bicycle",
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"car",
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"motorcycle",
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"airplane",
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"bus",
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"train",
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"truck",
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"boat",
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"traffic light"
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],
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"filePath": "data/nef/kl520/kl520_20005_yolov5-noupsample_w640h640.nef",
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"version": "1.0.0",
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"author": "Kneron",
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"license": "Apache-2.0",
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"createdAt": "2024-01-01T00:00:00Z",
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"updatedAt": "2024-01-01T00:00:00Z"
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},
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{
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"id": "kl520-fcos-detection",
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"name": "FCOS Detection (KL520)",
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"description": "FCOS (Fully Convolutional One-Stage) object detection with DarkNet53s backbone, compiled for KL520. Anchor-free detection at 512x512.",
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"thumbnail": "/images/models/fcos-det.png",
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"taskType": "object_detection",
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"categories": [
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"general",
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"multi-object"
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],
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"framework": "NEF",
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"inputSize": {
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"width": 512,
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"height": 512
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},
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"modelSize": 8900000,
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"quantization": "INT8",
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"supportedHardware": [
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"KL520"
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],
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"labels": [
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"person",
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"bicycle",
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"car",
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"motorcycle",
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"airplane",
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"bus",
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"train",
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"truck",
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"boat",
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"traffic light"
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],
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"filePath": "data/nef/kl520/kl520_20004_fcos-drk53s_w512h512.nef",
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"version": "1.0.0",
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"author": "Kneron",
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"license": "Apache-2.0",
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"createdAt": "2024-01-01T00:00:00Z",
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"updatedAt": "2024-01-01T00:00:00Z"
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},
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{
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"id": "kl520-ssd-face-detection",
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"name": "SSD Face Detection (KL520)",
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"description": "SSD-based face detection with landmark localization, compiled for KL520. Lightweight model suitable for face detection and alignment tasks.",
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"thumbnail": "/images/models/ssd-face.png",
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"taskType": "object_detection",
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"categories": [
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"face",
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"security"
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],
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"framework": "NEF",
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"inputSize": {
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"width": 320,
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"height": 240
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},
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"modelSize": 1000000,
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"quantization": "INT8",
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"supportedHardware": [
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"KL520"
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],
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"labels": [
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"face"
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],
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"filePath": "data/nef/kl520/kl520_ssd_fd_lm.nef",
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"version": "1.0.0",
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"author": "Kneron",
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"license": "Apache-2.0",
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"createdAt": "2024-01-01T00:00:00Z",
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"updatedAt": "2024-01-01T00:00:00Z"
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},
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{
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"id": "kl520-tiny-yolov3",
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"name": "Tiny YOLOv3 (KL520)",
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"description": "Tiny YOLOv3 object detection model compiled for KL520. Compact and fast model for general-purpose multi-object detection on edge devices.",
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"thumbnail": "/images/models/tiny-yolov3.png",
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"taskType": "object_detection",
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"categories": [
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"general",
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"multi-object"
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],
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"framework": "NEF",
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"inputSize": {
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"width": 416,
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"height": 416
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},
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"modelSize": 9400000,
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"quantization": "INT8",
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"supportedHardware": [
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"KL520"
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],
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"labels": [
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"person",
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"bicycle",
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"car",
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"motorcycle",
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"airplane",
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"bus",
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"train",
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"truck",
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"boat",
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"traffic light"
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],
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"filePath": "data/nef/kl520/kl520_tiny_yolo_v3.nef",
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"version": "1.0.0",
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"author": "Kneron",
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"license": "Apache-2.0",
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"createdAt": "2024-01-01T00:00:00Z",
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"updatedAt": "2024-01-01T00:00:00Z"
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},
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{
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"id": "kl720-yolov5-detection",
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"name": "YOLOv5 Detection (KL720)",
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"description": "YOLOv5 object detection model compiled for Kneron KL720. No upsample variant optimized for KL720 NPU inference at 640x640 resolution with USB 3.0 throughput.",
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"thumbnail": "/images/models/yolov5-det.png",
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"taskType": "object_detection",
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"categories": [
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"general",
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"multi-object"
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],
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"framework": "NEF",
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"inputSize": {
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"width": 640,
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"height": 640
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},
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"modelSize": 10168348,
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"quantization": "INT8",
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"supportedHardware": [
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"KL720"
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],
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"labels": [
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"person",
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"bicycle",
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"car",
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"motorcycle",
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"airplane",
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"bus",
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"train",
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"truck",
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"boat",
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"traffic light"
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],
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"filePath": "data/nef/kl720/kl720_20005_yolov5-noupsample_w640h640.nef",
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"version": "1.0.0",
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"author": "Kneron",
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"license": "Apache-2.0",
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"createdAt": "2024-01-01T00:00:00Z",
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"updatedAt": "2024-01-01T00:00:00Z"
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},
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{
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"id": "kl720-resnet18-classification",
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"name": "ImageNet Classification ResNet18 (KL720)",
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"description": "ResNet18-based image classification compiled for KL720. Supports 1000 ImageNet categories with fast inference via USB 3.0.",
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"thumbnail": "/images/models/imagenet-cls.png",
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"taskType": "classification",
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"categories": [
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"general",
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"image-classification"
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],
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"framework": "NEF",
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"inputSize": {
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"width": 224,
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"height": 224
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},
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"modelSize": 12826804,
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"quantization": "INT8",
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"supportedHardware": [
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"KL720"
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],
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"labels": [
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"airplane",
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"automobile",
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"bird",
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"cat",
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"deer",
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"dog",
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"frog",
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"horse",
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"ship",
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"truck"
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],
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"filePath": "data/nef/kl720/kl720_20001_resnet18_w224h224.nef",
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"version": "1.0.0",
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"author": "Kneron",
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"license": "MIT",
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"createdAt": "2024-01-01T00:00:00Z",
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"updatedAt": "2024-01-01T00:00:00Z"
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},
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{
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"id": "kl720-fcos-detection",
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"name": "FCOS Detection (KL720)",
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"description": "FCOS (Fully Convolutional One-Stage) object detection with DarkNet53s backbone, compiled for KL720. Anchor-free detection at 512x512.",
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"thumbnail": "/images/models/fcos-det.png",
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"taskType": "object_detection",
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"categories": [
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"general",
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"multi-object"
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],
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"framework": "NEF",
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"inputSize": {
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"width": 512,
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"height": 512
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},
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"modelSize": 13004640,
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"quantization": "INT8",
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"supportedHardware": [
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"KL720"
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],
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"labels": [
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"person",
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"bicycle",
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"car",
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"motorcycle",
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"airplane",
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"bus",
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"train",
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"truck",
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"boat",
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"traffic light"
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],
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"filePath": "data/nef/kl720/kl720_20004_fcos-drk53s_w512h512.nef",
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"version": "1.0.0",
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"author": "Kneron",
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"license": "Apache-2.0",
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"createdAt": "2024-01-01T00:00:00Z",
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"updatedAt": "2024-01-01T00:00:00Z"
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}
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]
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