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visionA/local-tool/server/data/models.json
jim800121chen c54f16fca0 Initial commit: visionA monorepo with local-tool subproject 
local-tool/: visionA-local desktop app
- M1: Wails shell + Go server + Next.js UI + Mock mode (macOS dmg ready)
- M2: i18n (zh-TW/en) + Settings 4-tab refactor
- M3: Embedded Python 3.12 runtime (python-build-standalone) + KneronPLUS wheels
- M4: Windows Inno Setup script (build on Windows runner)
- M5: Linux AppImage script + udev rule (build on Linux runner)
- M6: ffmpeg (GPL, pending legal review) + yt-dlp bundled
- Lifecycle: watchServer health check, fatal native dialog,
            Wails IPC raise endpoint, stale process cleanup

.autoflow/: full PRD / Design Spec / Architecture / Testing docs
            (4 rounds tri-party discussion + cross review)
.github/workflows/: macOS / Windows / Linux build CI

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-11 22:10:38 +08:00

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[
{
"id": "yolov5-face-detection",
"name": "YOLOv5 Face Detection",
"description": "Real-time face detection model based on YOLOv5 architecture, optimized for edge deployment on Kneron KL720. Detects faces with high accuracy in various lighting conditions.",
"thumbnail": "/images/models/yolov5-face.png",
"taskType": "object_detection",
"categories": ["face", "security", "people"],
"framework": "ONNX",
"inputSize": {"width": 640, "height": 640},
"modelSize": 14200000,
"quantization": "INT8",
"accuracy": 0.92,
"latencyMs": 33,
"fps": 30,
"supportedHardware": ["KL720", "KL730"],
"labels": ["face"],
"version": "1.0.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-01-15T00:00:00Z",
"updatedAt": "2024-06-01T00:00:00Z"
},
{
"id": "imagenet-classification",
"name": "ImageNet Classification (ResNet18)",
"description": "ResNet18-based image classification model trained on ImageNet. Supports 1000 object categories with efficient inference on KL520 edge devices.",
"thumbnail": "/images/models/imagenet-cls.png",
"taskType": "classification",
"categories": ["general", "image-classification"],
"framework": "ONNX",
"inputSize": {"width": 224, "height": 224},
"modelSize": 12000000,
"quantization": "INT8",
"accuracy": 0.78,
"latencyMs": 15,
"fps": 60,
"supportedHardware": ["KL520", "KL720", "KL730"],
"labels": ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"],
"filePath": "data/nef/kl520/kl520_20001_resnet18_w224h224.nef",
"version": "2.1.0",
"author": "Kneron",
"license": "MIT",
"createdAt": "2024-02-10T00:00:00Z",
"updatedAt": "2024-07-15T00:00:00Z"
},
{
"id": "person-detection",
"name": "Person Detection",
"description": "Lightweight person detection model optimized for real-time surveillance and people counting. Low latency with high accuracy on person class.",
"thumbnail": "/images/models/person-det.png",
"taskType": "object_detection",
"categories": ["people", "security", "surveillance"],
"framework": "ONNX",
"inputSize": {"width": 416, "height": 416},
"modelSize": 11800000,
"quantization": "INT8",
"accuracy": 0.89,
"latencyMs": 28,
"fps": 35,
"supportedHardware": ["KL720", "KL730"],
"labels": ["person"],
"version": "1.2.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-03-01T00:00:00Z",
"updatedAt": "2024-08-01T00:00:00Z"
},
{
"id": "vehicle-classification",
"name": "Vehicle Classification",
"description": "Vehicle type classification model that identifies cars, trucks, buses, motorcycles, and bicycles. Ideal for traffic monitoring and smart parking.",
"thumbnail": "/images/models/vehicle-cls.png",
"taskType": "classification",
"categories": ["vehicle", "traffic", "transportation"],
"framework": "ONNX",
"inputSize": {"width": 224, "height": 224},
"modelSize": 6200000,
"quantization": "INT8",
"accuracy": 0.85,
"latencyMs": 12,
"fps": 75,
"supportedHardware": ["KL520", "KL720", "KL730"],
"labels": ["car", "truck", "bus", "motorcycle", "bicycle"],
"version": "1.0.0",
"author": "Kneron",
"license": "MIT",
"createdAt": "2024-03-20T00:00:00Z",
"updatedAt": "2024-05-10T00:00:00Z"
},
{
"id": "hand-gesture-recognition",
"name": "Hand Gesture Recognition",
"description": "Recognizes 10 common hand gestures in real-time. Suitable for touchless interfaces and gesture-based control systems.",
"thumbnail": "/images/models/hand-gesture.png",
"taskType": "classification",
"categories": ["gesture", "hand", "interaction"],
"framework": "ONNX",
"inputSize": {"width": 224, "height": 224},
"modelSize": 5800000,
"quantization": "INT8",
"accuracy": 0.88,
"latencyMs": 18,
"fps": 50,
"supportedHardware": ["KL520", "KL720"],
"labels": ["thumbs_up", "thumbs_down", "open_palm", "fist", "peace", "ok", "pointing", "wave", "grab", "pinch"],
"version": "1.1.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-04-05T00:00:00Z",
"updatedAt": "2024-09-01T00:00:00Z"
},
{
"id": "coco-object-detection",
"name": "COCO Object Detection",
"description": "General-purpose object detection model trained on COCO dataset. Detects 80 common object categories including people, animals, vehicles, and household items.",
"thumbnail": "/images/models/coco-det.png",
"taskType": "object_detection",
"categories": ["general", "multi-object", "coco"],
"framework": "ONNX",
"inputSize": {"width": 640, "height": 640},
"modelSize": 23500000,
"quantization": "INT8",
"accuracy": 0.82,
"latencyMs": 45,
"fps": 22,
"supportedHardware": ["KL720", "KL730"],
"labels": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow"],
"version": "3.0.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-01-01T00:00:00Z",
"updatedAt": "2024-10-01T00:00:00Z"
},
{
"id": "face-mask-detection",
"name": "Face Mask Detection",
"description": "Detects whether a person is wearing a face mask, wearing it incorrectly, or not wearing one. Built for health compliance monitoring.",
"thumbnail": "/images/models/face-mask.png",
"taskType": "object_detection",
"categories": ["face", "health", "safety"],
"framework": "ONNX",
"inputSize": {"width": 320, "height": 320},
"modelSize": 9800000,
"quantization": "INT8",
"accuracy": 0.91,
"latencyMs": 22,
"fps": 45,
"supportedHardware": ["KL720", "KL730"],
"labels": ["mask_on", "mask_off", "mask_incorrect"],
"version": "1.3.0",
"author": "Kneron",
"license": "MIT",
"createdAt": "2024-02-28T00:00:00Z",
"updatedAt": "2024-07-20T00:00:00Z"
},
{
"id": "license-plate-detection",
"name": "License Plate Detection",
"description": "Detects and localizes license plates in images and video streams. Optimized for various plate formats and viewing angles.",
"thumbnail": "/images/models/license-plate.png",
"taskType": "object_detection",
"categories": ["vehicle", "traffic", "ocr"],
"framework": "ONNX",
"inputSize": {"width": 416, "height": 416},
"modelSize": 12400000,
"quantization": "INT8",
"accuracy": 0.87,
"latencyMs": 30,
"fps": 33,
"supportedHardware": ["KL720", "KL730"],
"labels": ["license_plate"],
"version": "1.0.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-05-15T00:00:00Z",
"updatedAt": "2024-08-30T00:00:00Z"
},
{
"id": "kl520-yolov5-detection",
"name": "YOLOv5 Detection (KL520)",
"description": "YOLOv5 object detection model compiled for Kneron KL520. No upsample variant optimized for NPU inference at 640x640 resolution.",
"thumbnail": "/images/models/yolov5-det.png",
"taskType": "object_detection",
"categories": ["general", "multi-object"],
"framework": "NEF",
"inputSize": {"width": 640, "height": 640},
"modelSize": 7200000,
"quantization": "INT8",
"accuracy": 0.80,
"latencyMs": 50,
"fps": 20,
"supportedHardware": ["KL520"],
"labels": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"],
"filePath": "data/nef/kl520/kl520_20005_yolov5-noupsample_w640h640.nef",
"version": "1.0.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-01-01T00:00:00Z",
"updatedAt": "2024-01-01T00:00:00Z"
},
{
"id": "kl520-fcos-detection",
"name": "FCOS Detection (KL520)",
"description": "FCOS (Fully Convolutional One-Stage) object detection with DarkNet53s backbone, compiled for KL520. Anchor-free detection at 512x512.",
"thumbnail": "/images/models/fcos-det.png",
"taskType": "object_detection",
"categories": ["general", "multi-object"],
"framework": "NEF",
"inputSize": {"width": 512, "height": 512},
"modelSize": 8900000,
"quantization": "INT8",
"accuracy": 0.78,
"latencyMs": 45,
"fps": 22,
"supportedHardware": ["KL520"],
"labels": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"],
"filePath": "data/nef/kl520/kl520_20004_fcos-drk53s_w512h512.nef",
"version": "1.0.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-01-01T00:00:00Z",
"updatedAt": "2024-01-01T00:00:00Z"
},
{
"id": "kl520-ssd-face-detection",
"name": "SSD Face Detection (KL520)",
"description": "SSD-based face detection with landmark localization, compiled for KL520. Lightweight model suitable for face detection and alignment tasks.",
"thumbnail": "/images/models/ssd-face.png",
"taskType": "object_detection",
"categories": ["face", "security"],
"framework": "NEF",
"inputSize": {"width": 320, "height": 240},
"modelSize": 1000000,
"quantization": "INT8",
"accuracy": 0.85,
"latencyMs": 10,
"fps": 100,
"supportedHardware": ["KL520"],
"labels": ["face"],
"filePath": "data/nef/kl520/kl520_ssd_fd_lm.nef",
"version": "1.0.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-01-01T00:00:00Z",
"updatedAt": "2024-01-01T00:00:00Z"
},
{
"id": "kl520-tiny-yolov3",
"name": "Tiny YOLOv3 (KL520)",
"description": "Tiny YOLOv3 object detection model compiled for KL520. Compact and fast model for general-purpose multi-object detection on edge devices.",
"thumbnail": "/images/models/tiny-yolov3.png",
"taskType": "object_detection",
"categories": ["general", "multi-object"],
"framework": "NEF",
"inputSize": {"width": 416, "height": 416},
"modelSize": 9400000,
"quantization": "INT8",
"accuracy": 0.75,
"latencyMs": 35,
"fps": 28,
"supportedHardware": ["KL520"],
"labels": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"],
"filePath": "data/nef/kl520/kl520_tiny_yolo_v3.nef",
"version": "1.0.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-01-01T00:00:00Z",
"updatedAt": "2024-01-01T00:00:00Z"
},
{
"id": "kl720-yolov5-detection",
"name": "YOLOv5 Detection (KL720)",
"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.",
"thumbnail": "/images/models/yolov5-det.png",
"taskType": "object_detection",
"categories": ["general", "multi-object"],
"framework": "NEF",
"inputSize": {"width": 640, "height": 640},
"modelSize": 10168348,
"quantization": "INT8",
"accuracy": 0.82,
"latencyMs": 30,
"fps": 33,
"supportedHardware": ["KL720"],
"labels": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"],
"filePath": "data/nef/kl720/kl720_20005_yolov5-noupsample_w640h640.nef",
"version": "1.0.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-01-01T00:00:00Z",
"updatedAt": "2024-01-01T00:00:00Z"
},
{
"id": "kl720-resnet18-classification",
"name": "ImageNet Classification ResNet18 (KL720)",
"description": "ResNet18-based image classification compiled for KL720. Supports 1000 ImageNet categories with fast inference via USB 3.0.",
"thumbnail": "/images/models/imagenet-cls.png",
"taskType": "classification",
"categories": ["general", "image-classification"],
"framework": "NEF",
"inputSize": {"width": 224, "height": 224},
"modelSize": 12826804,
"quantization": "INT8",
"accuracy": 0.78,
"latencyMs": 10,
"fps": 100,
"supportedHardware": ["KL720"],
"labels": ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"],
"filePath": "data/nef/kl720/kl720_20001_resnet18_w224h224.nef",
"version": "1.0.0",
"author": "Kneron",
"license": "MIT",
"createdAt": "2024-01-01T00:00:00Z",
"updatedAt": "2024-01-01T00:00:00Z"
},
{
"id": "kl720-fcos-detection",
"name": "FCOS Detection (KL720)",
"description": "FCOS (Fully Convolutional One-Stage) object detection with DarkNet53s backbone, compiled for KL720. Anchor-free detection at 512x512.",
"thumbnail": "/images/models/fcos-det.png",
"taskType": "object_detection",
"categories": ["general", "multi-object"],
"framework": "NEF",
"inputSize": {"width": 512, "height": 512},
"modelSize": 13004640,
"quantization": "INT8",
"accuracy": 0.80,
"latencyMs": 30,
"fps": 33,
"supportedHardware": ["KL720"],
"labels": ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"],
"filePath": "data/nef/kl720/kl720_20004_fcos-drk53s_w512h512.nef",
"version": "1.0.0",
"author": "Kneron",
"license": "Apache-2.0",
"createdAt": "2024-01-01T00:00:00Z",
"updatedAt": "2024-01-01T00:00:00Z"
}
]
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