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/
Alat Penanda Aras Pembelajaran Mesin (ML Bench) (AI Benchmark Tool)

Model yang disokong:
 - MobileNet v1
 - MobileNet v2
 - Permulaan v3
 - Resnet v2 50
 - SSD Mobilenet v1 (Pengesanan Objek)

Runtime yang disokong:
 - Tensorflow Lite
 - Tensorflow Mobile
 - Android NN
 - SNPE (untuk Qualcomm)

Sokongan SideLoad:
      Bagaimana cara memasangkan model anda:
1. Tukar model anda kepada tflite (menggunakan toco) atau dlc (menggunakan alat penukaran snpe).
2. Pada mesin tempatan anda, buat direktori [Nama Model]
3. Salin fail model anda ke direktori yang dibuat dalam langkah 2
4. Buat fail yang dipanggil meta-data.json dalam direktori [Nama Model]
contoh meta-data.json:
{
"xres": 299,
"yres": 299,
"kedalaman": 3,
"input_type": "float",
"output_type": "float",
"input_name": "input: 0",
"output_name": "InceptionV3 / Ramalan / Reshape_1: 0",
"image_mean": 0,
"image_std": 0,
"pemecut": "dsp",
}
5. tekan [Nama Model] direktori ke peranti sasaran menggunakan arahan di bawah
adb push ./[Model] /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

Versi Terkini ML Bench

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