config.arm64_v8a

config.xxxhdpi

config.en

Everyone

etinum

Oreo

Machine Learning Benchmark Tool (ML Bench) (AI Benchmark Tool)

Supported models :
- MobileNet v1
- MobileNet v2 
- Inception v3
- Resnet v2 50
- SSD Mobilenet v1 (Object Detection)

Supported runtime :
- Tensorflow Lite
- Tensorflow Mobile
- Android NN
- SNPE (for Qualcomm)

SideLoad Support:
How to side load your model :
1. Convert your model to tflite (using toco) or dlc (using snpe conversion tool).
2. On your local machine, create [Model Name] directory
3. Copy your model file to the directory created in step 2
4. Create a file called meta-data.json in the [Model Name] directory
example of meta-data.json :
{
"xres" : 299,
"yres" : 299,
"depth" : 3,
"input_type" : "float",
"output_type" : "float",
"input_name" : "input:0",
"output_name" : "InceptionV3/Predictions/Reshape_1:0",
"image_mean" : 0,
"image_std" : 0,
"accelerator":"dsp",
}
5. push [Model Name] directory to the target device using below command
adb push ./[Model Name] /sdcard/Android/data/com.etinum.mlbench/files/models/
Strumento di benchmark Machine Learning (ML Bench) (Strumento di benchmark AI)

Modelli supportati:
 - MobileNet v1
 - MobileNet v2
 - Inception v3
 - Resnet v2 50
 - SSD Mobilenet v1 (Object Detection)

Runtime supportato:
 - Tensorflow Lite
 - Tensorflow Mobile
 - Android NN
 - SNPE (per Qualcomm)

Supporto SideLoad:
      Come caricare sul lato il tuo modello:
1. Converti il ​​tuo modello in tflite (usando toco) o dlc (usando lo strumento di conversione snpe).
2. Sulla macchina locale, creare la directory [Nome modello]
3. Copiare il file del modello nella directory creata nel passaggio 2
4. Creare un file chiamato meta-data.json nella directory [Nome modello]
esempio di meta-data.json:
{
"xres": 299,
"yres": 299,
"profondità": 3,
"input_type": "float",
"output_type": "float",
"input_name": "input: 0",
"output_name": "InceptionV3 / Predictions / Reshape_1: 0",
"image_mean": 0,
"image_std": 0,
"Acceleratore": "DSP",
}
5. premere la directory [Nome modello] sul dispositivo di destinazione usando il comando di sotto
adb push ./[Model Name] /sdcard/Android/data/com.etinum.mlbench/files/models/

Machine Learning Benchmark Tool (ML Bench) (AI Benchmark Tool)

Supported models :
- MobileNet v1
- MobileNet v2 
- Inception v3
- Resnet v2 50
- SSD Mobilenet v1 (Object Detection)

Supported runtime :
- Tensorflow Lite
- Tensorflow Mobile
- Android NN
- SNPE (for Qualcomm)

SideLoad Support:
How to side load your model :
1. Convert your model to tflite (using toco) or dlc (using snpe conversion tool).
2. On your local machine, create [Model Name] directory
3. Copy your model file to the directory created in step 2
4. Create a file called meta-data.json in the [Model Name] directory
example of meta-data.json :
{
"xres" : 299,
"yres" : 299,
"depth" : 3,
"input_type" : "float",
"output_type" : "float",
"input_name" : "input:0",
"output_name" : "InceptionV3/Predictions/Reshape_1:0",
"image_mean" : 0,
"image_std" : 0,
"accelerator":"dsp",
}
5. push [Model Name] directory to the target device using below command
adb push ./[Model Name] /sdcard/Android/data/com.etinum.mlbench/files/models/

ML Bench

Ultima versione di ML Bench

Altre versioni