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エッジAIアプリケーション用の Model Library

MERAの開発者は、DNA IPが搭載されたEdgeCortix AIアクセラレータ・チップまたはFPGAに最適化された、事前にトレーニングされたAI推論モデルである当社のModel Libraryを利用することで、すぐに開発を始められます。コードはMERAにドロップされ、すぐに実行または変更できます。アプリケーションには、識別、物体検出、セグメンテーション、ポーズ推定などが含まれます。 

AI 推論モデル

lidar
SFA3D
Framework: PyTorch
3D LiDAR Object Detection
Resolution: 608x608
モデルを取得する
vision
MonoDepth
Framework: PyTorch
Monocular Depth Estimation
Resolution: 384x288
モデルを取得する
pose-estimation
MoveNet Thunder
Framework: TFLite
Pose Estimation
Resolution: 256x256
モデルを取得する
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Model
ResNet18-v1.5
Framework
PyTorch
Application
Classification
Input Resolution
224x224
Calibration Data
Real-Data
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Model
ResNet50-v1.5
Framework
PyTorch
Application
Classification
Input Resolution
224x224
Calibration Data
Real-Data
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Model
YoloV3
Framework
TFLite
Application
Object-Detection
Input Resolution
416x416
Calibration Data
Real-Data
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Model
Yolov5s
Framework
TFLite
Application
Object-Detection
Input Resolution
448x448
Calibration Data
Real-Data
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Model
YoloV5m
Framework
TFLite
Application
Object-Detection
Input Resolution
640x640
Calibration Data
Real-Data
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Model
SFA3D
Framework
PyTorch
Application
3D-LiDAR-Object-Detection
Input Resolution
608x608
Calibration Data
Real-Data
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Model
EfficientNet-Lite-0
Framework
TFLite
Application
Classification
Input Resolution
240x240
Calibration Data
Real-Data
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Model
EfficientNet-Lite-2
Framework
TFLite
Application
Classification
Input Resolution
260x260
Calibration Data
Real-Data
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Model
EfficientNet-Lite-3
Framework
TFLite
Application
Classification
Input Resolution
280x280
Calibration Data
Real-Data
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Model
EfficientNet-Lite-4
Framework
TFLite
Application
Classification
Input Resolution
300x300
Calibration Data
Real-Data
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Model
EfficientNetV2-B0
Framework
TFLite
Application
Classification
Input Resolution
224x224
Calibration Data
Random-Data
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Model
EfficientNetV2-B1
Framework
TFLite
Application
Classification
Input Resolution
224x224
Calibration Data
Random-Data
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Model
EfficientNetV2-B2
Framework
TFLite
Application
Classification
Input Resolution
224x224
Calibration Data
Random-Data
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Model
EfficientNetV2-B3
Framework
TFLite
Application
Classification
Input Resolution
224x224
Calibration Data
Random-Data
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Model
EfficientNetV2-s
Framework
TFLite
Application
Classification
Input Resolution
224x224
Calibration Data
Random-Data
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Model
MonoDepth
Framework
PyTorch
Application
Monocular-Depth-Estimation
Input Resolution
384x288
Calibration Data
Real-Data
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Model
U-Net
Framework
TFLite
Application
Segmentation
Input Resolution
128x128
Calibration Data
Real-Data
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Model
MoveNet-Thunder
Framework
TFLite
Application
Pose-Estimation
Input Resolution
256x256
Calibration Data
Real-Data
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Model
YoloV4-Tiny
Framework
TFLite
Application
Object-Detection
Input Resolution
640x640
Calibration Data
Real-Data
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Model
DeepLabEdgeTPU-m
Framework
TFLite
Application
Segmentation
Input Resolution
512x512
Calibration Data
Real-Data
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Model
DeepLabEdgeTPU-s
Framework
TFLite
Application
Segmentation
Input Resolution
512x512
Calibration Data
Real-Data
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Model
MoveNet-Lighting
Framework
TFLite
Application
Pose-Estimation
Input Resolution
192x192
Calibration Data
Real-Data
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Model
MobileNetV2-SSD
Framework
PyTorch
Application
Object-Detection
Input Resolution
640x480
Calibration Data
Real-Data
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Model
DeepLabEdgeTPU-xs
Framework
TFLite
Application
Segmentation
Input Resolution
512x512
Calibration Data
Real-Data
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Model
GladNet
Framework
TFLite
Application
Low-Light-Enhancement
Input Resolution
640x480
Calibration Data
Real-Data
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Model
SR-Mobile-Quantization (ABPN)
Framework
TFLite
Application
Super-Resolution
Input Resolution
640x360 to HD
Calibration Data
Real-Data
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Model
YoloV7-Quantizer
Framework
MERA
Application
Object-Detection
Input Resolution
640x640
Calibration Data
Real-Data
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Model
YoloV4
Framework
TFLite
Application
Object-Detection
Input Resolution
416x416
Calibration Data
Real-Data
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Model
SCI-Quantizer
Framework
TFLite
Application
Low-Light-Enhancement
Input Resolution
1280x720
Calibration Data
Real-Data
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SAKURA-II M.2モジュール＆PCIeカード

EdgeCortix SAKURA-IIは、ソフトウェア開発やAIモデル推論タスクのホストシステムに容易に統合できます。

M.2モジュールまたはPCIeカードを注文し、ぜひ試してみてください！