Name	Name	Last commit message	Last commit date
parent directory ..
etc_v2h	etc_v2h
etc_v2l	etc_v2l
exe_v2h	exe_v2h
exe_v2l	exe_v2l
images	images
src	src
gitattributes	gitattributes
readme.md	readme.md

Fish Classification Application

Application: Overview

The fish classification application allows to classify between 31 different fish species. Label List

The application could be used to classify fishes during fish farming, or from certain areas on the sea/river through drones, etc.

It has 4 modes of running.

Mode	RZ/V2L	RZ/V2H
MIPI Camera	`Supported`	`-`
USB Camera	`Supported`	`Supported`
Image	`Supported`	`Supported`
Video	`Supported`	`Supported`
Web App	`Supported` (Link to readme)	`Supported` (Link to readme)

Supported Product

RZ/V2L Evaluation Board Kit (RZ/V2L EVK)
RZ/V2H Evaluation Board Kit (RZ/V2H EVK)

Demo

Following is the demo for RZ/V2H EVK.

Application: Requirements

Hardware Requirements

For	Equipment	Details
RZ/V2L	RZ/V2L EVK	Evaluation Board Kit for RZ/V2L. Includes followings. MIPI Camera Module(Google Coral Camera) Used as a camera input source. MicroUSB to Serial Cable for serial communication.
	AC Adapter	USB Power Delivery adapter for the board power supply.
	MicroHDMI Cable	Used to connect the HDMI Monitor and the board. RZ/V2L EVK has microHDMI port.
RZ/V2H	RZ/V2H EVK	Evaluation Board Kit for RZ/V2H.
	AC Adapter	USB Power Delivery adapter for the board power supply. 100W is required.
	HDMI Cable	Used to connect the HDMI Monitor and the board. RZ/V2H EVK has HDMI port.
	USB Camera	Used as a camera input source.
Common	USB Cable Type-C	Connect AC adapter and the board.
	HDMI Monitor	Used to display the graphics of the board.
	microSD card	Used as the filesystem. Must have over 4GB capacity of blank space. Operating Environment: Transcend UHS-I microSD 300S 16GB
	Linux PC	Used to build application and setup microSD card. Operating Environment: Ubuntu 20.04
	SD card reader	Used for setting up microSD card.
	USB Hub	Used to connect USB Keyboard and USB Mouse to the board.
	USB Keyboard	Used to type strings on the terminal of board.
	USB Mouse	Used to operate the mouse on the screen of board.

Note: All external devices will be attached to the board and does not require any driver installation (Plug n Play Type)

Connect the hardware as shown below.

RZ/V2L EVK	RZ/V2H EVK

Note 1: When using the keyboard connected to RZ/V Evaluation Board, the keyboard layout and language are fixed to English.
Note 2: For RZ/V2H EVK, there are USB 2.0 and USB 3.0 ports.
USB camera needs to be connected to appropriate port based on its requirement.

Application: Build Stage

Note: User can skip to the next stage (deploy) if they do not want to build the application.
All pre-built binaries are provided.

Prerequisites

This section expects the user to have completed Step 5 of Getting Started Guide provided by Renesas.

After completion of the guide, the user is expected of following things.

AI SDK setup is done.
Following docker container is running on the host machine.

Board Docker container

RZ/V2L EVK rzv2l_ai_sdk_container

RZ/V2H EVK rzv2h_ai_sdk_container

Note: Docker environment is required for building the sample application.

Board	Docker container
RZ/V2L EVK	`rzv2l_ai_sdk_container`
RZ/V2H EVK	`rzv2h_ai_sdk_container`

Application File Generation

On your host machine, copy the repository from the GitHub to the desired location.
1. It is recommended to copy/clone the repository on the data folder, which is mounted on the Docker container.
```
cd <path_to_data_folder_on_host>/data
git clone https://github.com/renesas-rz/rzv_ai_sdk.git
```
Note: This command will download the whole repository, which include all other applications.
If you have already downloaded the repository of the same version, you may not need to run this command.
Run (or start) the docker container and open the bash terminal on the container.
E.g., for RZ/V2L, use the rzv2l_ai_sdk_container as the name of container created from rzv2l_ai_sdk_image docker image.

Note that all the build steps/commands listed below are executed on the docker container bash terminal.
Set your clone directory to the environment variable.
```
export PROJECT_PATH=/drp-ai_tvm/data/rzv_ai_sdk
```

Go to the application source code directory.

cd ${PROJECT_PATH}/Q04_fish_classification/src

Create and move to the build directory.
```
mkdir -p build && cd build
```

Build the application by following the commands below.
For RZ/V2L

cmake -DCMAKE_TOOLCHAIN_FILE=./toolchain/runtime.cmake ..
make -j$(nproc)

For RZ/V2H

cmake -DCMAKE_TOOLCHAIN_FILE=./toolchain/runtime.cmake -DV2H=ON ..
make -j$(nproc)

The following application file would be generated in the ${PROJECT_PATH}/Q04_fish_classification/src/build directory
- fish_classification

Application: Deploy Stage

Prerequisites

This section expects the user to have completed Step 7-1 of Getting Started Guide provided by Renesas.

After completion of the guide, the user is expected of following things.

microSD card setup is done.

File Configuration

For the ease of deployment all the deployable file and folders are provided in following folders.

Board	`EXE_DIR`
RZ/V2L EVK	exe_v2l
RZ/V2H EVK	exe_v2h

Each folder contains following items.

File	Details
fish_classification_model	Model object files for deployment.
fish_class_list.txt	Label list for Fish classes
Bangus.jpg	sample image
output.mp4	sample video
fish_classification	application file.

Instruction

Copy the following files to the /home/root/tvm directory of the rootfs (SD Card) for the board.

File Details

All files in EXE_DIR directory Including deploy.so file.

fish_classification application file Generated the file according to Application File Generation
Check if libtvm_runtime.so exists under /usr/lib64 directory of the rootfs (SD card) on the board.

File	Details
All files in `EXE_DIR` directory	Including `deploy.so` file.
`fish_classification` application file	Generated the file according to Application File Generation

Folder structure in the rootfs (SD Card) would look like:

|-- usr
|   `-- lib64
|       `-- libtvm_runtime.so
`-- home
    `-- root
        `-- tvm
            |-- fish_classification_model
            |   |-- deploy.json
            |   |-- deploy.params
            |   `-- deploy.so
            |-- Bangus.jpg
            |-- fish_class_list.txt
            |-- fish_classification
            `-- output.mp4

Note: The directory name could be anything instead of tvm. If you copy the whole EXE_DIR folder on the board, you are not required to rename it tvm.

Application: Run Stage

Prerequisites

This section expects the user to have completed Step 7-3 of Getting Started Guide provided by Renesas.

After completion of the guide, the user is expected of following things.

The board setup is done.
The board is booted with microSD card, which contains the application file.

Instruction

On Board terminal, go to the tvm directory of the rootfs.
```
cd /home/root/tvm/
```
Run the application.
- For Image Mode
```
./fish_classification IMAGE Bangus.jpg
```
Note: Tested with image file format .png and .jpg
- For USB Camera Mode
```
./fish_classification USB
```
- For MIPI Camera Mode [RZ/V2L only]
```
./fish_classification MIPI
```
- For VIDEO Mode
```
./fish_classification VIDEO output.mp4
```
Note: Tested with video file format .mp4 and .avi
Following window shows up on HDMI screen.

RZ/V2L EVK RZ/V2H EVK

On application window, following information is displayed.
- Camera capture
- Classification result (class name and score.)
- Processing time taken for AI inference.
- Frames per Second
- Top 5 Classification Results (Based on the score)
To terminate the application, follow the termination method below.
- For RZ/V2L, press Esc key to terminate the application.
- For RZ/V2H, switch the application window to the terminal by using Super(windows key)+Tab and press ENTER key on the terminal of the board.

Application: Configuration

AI Model

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
├─Sequential: 1-1                        [-1, 512, 7, 7]           --
|    └─Conv2d: 2-1                       [-1, 64, 112, 112]        9,408
|    └─BatchNorm2d: 2-2                  [-1, 64, 112, 112]        128
|    └─ReLU: 2-3                         [-1, 64, 112, 112]        --
|    └─MaxPool2d: 2-4                    [-1, 64, 56, 56]          --
|    └─Sequential: 2-5                   [-1, 64, 56, 56]          --
|    |    └─BasicBlock: 3-1              [-1, 64, 56, 56]          73,984
|    |    └─BasicBlock: 3-2              [-1, 64, 56, 56]          73,984
|    |    └─BasicBlock: 3-3              [-1, 64, 56, 56]          73,984
|    └─Sequential: 2-6                   [-1, 128, 28, 28]         --
|    |    └─BasicBlock: 3-4              [-1, 128, 28, 28]         230,144
|    |    └─BasicBlock: 3-5              [-1, 128, 28, 28]         295,424
|    |    └─BasicBlock: 3-6              [-1, 128, 28, 28]         295,424
|    |    └─BasicBlock: 3-7              [-1, 128, 28, 28]         295,424
|    └─Sequential: 2-7                   [-1, 256, 14, 14]         --
|    |    └─BasicBlock: 3-8              [-1, 256, 14, 14]         919,040
|    |    └─BasicBlock: 3-9              [-1, 256, 14, 14]         1,180,672
|    |    └─BasicBlock: 3-10             [-1, 256, 14, 14]         1,180,672
|    |    └─BasicBlock: 3-11             [-1, 256, 14, 14]         1,180,672
|    |    └─BasicBlock: 3-12             [-1, 256, 14, 14]         1,180,672
|    |    └─BasicBlock: 3-13             [-1, 256, 14, 14]         1,180,672
|    └─Sequential: 2-8                   [-1, 512, 7, 7]           --
|    |    └─BasicBlock: 3-14             [-1, 512, 7, 7]           3,673,088
|    |    └─BasicBlock: 3-15             [-1, 512, 7, 7]           4,720,640
|    |    └─BasicBlock: 3-16             [-1, 512, 7, 7]           4,720,640
├─Sequential: 1-2                        [-1, 31]                  --
|    └─AdaptiveConcatPool2d: 2-9         [-1, 1024, 1, 1]          --
|    |    └─AdaptiveMaxPool2d: 3-17      [-1, 512, 1, 1]           --
|    |    └─AdaptiveAvgPool2d: 3-18      [-1, 512, 1, 1]           --
|    └─Flatten: 2-10                     [-1, 1024]                --
|    └─BatchNorm1d: 2-11                 [-1, 1024]                2,048
|    └─Dropout: 2-12                     [-1, 1024]                --
|    └─Linear: 2-13                      [-1, 512]                 524,288
|    └─ReLU: 2-14                        [-1, 512]                 --
|    └─BatchNorm1d: 2-15                 [-1, 512]                 1,024
|    └─Dropout: 2-16                     [-1, 512]                 --
|    └─Linear: 2-17                      [-1, 31]                  15,872
==========================================================================================
Total params: 21,827,904
Trainable params: 21,827,904
Non-trainable params: 0
Total mult-adds (G): 3.71
==========================================================================================
Input size (MB): 0.57
Forward/backward pass size (MB): 54.38
Params size (MB): 83.27
Estimated Total Size (MB): 138.21

Dataset

'Bangus', 'Big Head Carp', 'Black Spotted Barb', 'Catfish', 'Climbing Perch', 'Fourfinger Threadfin', 'Freshwater Eel', 'Glass Perchlet', 'Goby', 'Gold Fish', 'Gourami', 'Grass Carp', 'Green Spotted Puffer', 'Indian Carp', 'Indo-Pacific Tarpon', 'Jaguar Gapote', 'Janitor Fish', 'Knifefish', 'Long-Snouted Pipefish', 'Mosquito Fish', 'Mudfish', 'Mullet', 'Pangasius', 'Perch', 'Scat Fish', 'Silver Barb', 'Silver Carp','Silver Perch', 'Snakehead', 'Tenpounder', 'Tilapia'

Dataset properties: The total number of images: 13,304 image Training set size: 8791 images Test set size: 2751 images The number of classes: 1760

Dataset-Link

AI inference time

Board	AI inference time
RZ/V2L EVK	Approximately 50 ms
RZ/V2H EVK	Approximately 4 ms

Processing

Processing	Details
Pre-processing	Processed by CPU.
Inference	Processed by DRP-AI and CPU.
Post-processing	Processed by CPU.

Image buffer size

Board	Camera capture buffer size	HDMI output buffer size
RZ/V2L EVK	VGA (640x480) in YUYV format	HD (1280x720) in BGRA format
RZ/V2H EVK	VGA (640x480) in YUYV format	FHD (1920x1080) in BGRA format

Reference

For RZ/V2H EVK, this application supports USB camera only with 640x480 resolution.
To use FHD, please use MIPI camera.
Please refer to following URL for how to change camera input to MIPI camera.
https://renesas-rz.github.io/rzv_ai_sdk/latest/about-applications.

License

Apache License 2.0
For third party OSS library, please see the source code file itself.

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Q04_fish_classification

Q04_fish_classification

readme.md

Fish Classification Application

Application: Overview

Supported Product

Demo

Application: Requirements

Hardware Requirements

Application: Build Stage

Prerequisites

Application File Generation

Application: Deploy Stage

Prerequisites

File Configuration

Instruction

Application: Run Stage

Prerequisites

Instruction

Application: Configuration

AI Model

Dataset

AI inference time

Processing

Image buffer size

Reference

License

Files

Q04_fish_classification

Directory actions

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Q04_fish_classification

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parent directory

readme.md

Fish Classification Application

Application: Overview

Supported Product

Demo

Application: Requirements

Hardware Requirements

Application: Build Stage

Prerequisites

Application File Generation

Application: Deploy Stage

Prerequisites

File Configuration

Instruction

Application: Run Stage

Prerequisites

Instruction

Application: Configuration

AI Model

Dataset

AI inference time

Processing

Image buffer size

Reference

License