ML Bench

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/

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

Model yang didukung:

 - MobileNet v1

 - MobileNet v2

 - Inception v3

 - Resnet v2 50

 - SSD Mobilenet v1 (Deteksi Objek)

Waktu proses yang didukung:

 - Tensorflow Lite

 - Tensorflow Mobile

 - Android NN

 - SNPE (untuk Qualcomm)

Dukungan SideLoad:

      Bagaimana cara mem-load model Anda:

1. Ubah model Anda menjadi tflite (menggunakan toco) atau dlc (menggunakan alat konversi snpe).

2. Pada mesin lokal Anda, buat direktori [Nama Model]

3. Salin file model Anda ke direktori yang dibuat pada langkah 2

4. Buat file bernama meta-data.json di 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 / Predictions / Reshape_1: 0",

"image_mean": 0,

"image_std": 0,

"akselerator": "dsp",

}

5. dorong [Nama Model] direktori ke perangkat target menggunakan perintah di bawah ini

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/