The best solution for
starting your AI applications.
Provided by Renesas Electronics Corporation
This project is maintained by renesas-rz
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Function Name of AI Application | TLT Project Name | AI Model | Task Type | Download Dataset Name *1 |
Target product in AI Application |
|
---|---|---|---|---|---|---|
RZ/V2L | RZ/V2H and RZ/V2N | |||||
01_Head_count | 01_Head_count_yolov3 | YOLOv3 | Object detection | 01 Head Count(141MB) | ✔ | ✔ |
02_Line_crossing_object_counting | 02_Line_crossing_object_counting_tinyyolov3 | TinyYOLOv3 | Object detection | 02 Line Cross Object Counting(141MB) | ✔ | - |
02_Line_crossing_object_counting_yolov3 | YOLOv3 | Object detection | 02 Line Cross Object Counting(141MB) | - | ✔ | |
03_Elderly_fall_detection | 03_Elderly_fall_detection_tinyyolov2 | TinyYOLOv2 | Object detection | 03 Elderly Fall Detection TinyYolov2(18.6GB) | ✔ | ✔ |
03_Elderly_fall_detection_HRNet | HRNet | Keypoint detection | 03 COCO 16 Keyopoint | ✔ | ✔ | |
04_Safety_helmet_vest_detection | 04_Safety_helmet_vest_detection_yolov3 | YOLOv3 | Object detection | 04 Safety Helmet Vest Detection(3.42GB) | ✔ | ✔ |
07_Animal_detection | 07_Animal_detection_yolov3 | YOLOv3 | Object detection | 07 Animal Detection(1.83GB) | ✔ | ✔ |
09_Human_gaze_detection | 09_Human_gaze_detection_resnet18 | ResNet18 | Classification | 09 Human gaze detection resnet18(17GB)*2 | ✔ | ✔ |
09_Human_gaze_detection_tinyyolov2 | TinyYOLOv2 | Object detection | 09 Human gaze detection(4.43GB)*2 | ✔ | ✔ | |
10_Driver_monitoring_system | 10_Driver_monitoring_system_tinyyolov2 | TinyYOLOv2 | Object detection | 10 Driver Monitoring System TinyYolov2(4.43GB) | ✔ | - |
10_Driver_monitoring_system_yolov3 | HRNet | Object detection | 10 Driver Monitoring System Yolov3(1.80GB) | - | ✔ | |
10_Driver_monitoring_system_Deeppose | DeepPose | Keypoint detection | 10 Driver monitoring system Deeppose(723MB) | ✔ | ✔ | |
11_Head_count_topview | 11_Head_count_topview_yolov3 | YOLOV3 | Object detection | 11 Head Count Topview(1.8GB) | ✔ | ✔ |
12_Hand_gesture_recognition_v2 | 12_Hand_gesture_recognition_v2_resnet_18 | ResNet18 | Classification | 12 Hand Gesture Recognition Resnet(21.9GB) | ✔ | - |
12_Hand_gesture_recognition_v2_tinyyolov3 | TinyYOLOv3 | Object detection | 12 Hand Gesture Recognition Tiny Yolo(1.2GB) | ✔ | - | |
12_Hand_gesture_recognition_yolov3 | YOLOv3 | Object detection | 12 Hand gesture recognitionyolov3(161MB) | - | ✔ | |
13_Car_ahead_departure_detection | 13_Car_ahead_departure_detection_tinyyolov3 | TinyYOLOv3 | Object detection | COCO | ✔ | ✔ |
14_Multi_camera_vehicle_detection | 14_Multi_camera_vehicle_detection_yoloxl | YOLOX-l | Object detection | 14 Multi Camera Vehicle Detection(249MB) | - | ✔ |
15_Road_lane_segmentation | 15_Road_lane_segmentation_unet | Unet | Segmentation | 15 Road Lane Segmentation(68MB) | - | ✔ |
Q01_footfall_counter | Q01_footfall_counter_tinyyolov3 | TinyYOLOv3 | Object detection | COCO | ✔ | - |
Q01_footfall_counter_yolov3 | YOLOV3 | Object detection | COCO | - | ✔ | |
Q03_smart_parking | Q03_smart_parking | Custom model | Classification | Q03 Smart Parking(421MB) | ✔ | - |
Q04_fish_classification | Q04_fish_classification_resnet34 | ResNet34 | Classification | Q04 Fish Classification(1.58GB) | ✔ | ✔ |
Q06_expiry_date_detection | Q06_expiry_date_detection_tinyyolov3 | TinyYOLOv3 | Object detection | Q06 Expiry Date Detection(1.32GB) | ✔ | - |
Q06_expiry_date_detection_yolov3 | YOLOv3 | Object detection | Expiry Date Detection(1.32GB) | - | ✔ | |
Q07_plant_disease_classification | Q07_plant_disease_classification_resnet34 | ResNet34 | Classification | Q07 Plant Disease(1.34GB) | ✔ | ✔ |
Q08_object_counter | Q08_object_counter_animal_tinyyolov3 | TinyYOLOv3 | Object detection | Q08 Object Counter Animal(6.97GB) | ✔ | - |
Q08_object_counter_animal_yolov3 | YOLOv3 | Object detection | Q08 Object Counter Animal(6.97GB) | - | ✔ | |
Q08_object_counter_coco_tinyyolov3 | TinyYOLOv3 | Object detection | COCO | ✔ | - | |
Q08_object_counter_coco_yolov3 | YOLOv3 | Object detection | COCO | - | ✔ | |
Q08_object_counter_vehicle_tinyyolov3 | TinyYOLOv3 | Object detection | Q08 Object Counter Vehicle(1.51GB) | ✔ | - | |
Q08_object_counter_vehicle_yolov3 | YOLOv3 | Object detection | Q08 Object Counter Vehicle(1.51GB) | - | ✔ | |
Q09_crack_segmentation | Q09_crack_segmentation_unet | Unet | Segmentation | Q09 Crack Segmentation(0.99GB) | ✔ | ✔ |
Q10_suspicious_person_detection | Q10_suspicious_person_detection_tinyyolov3 | TinyYOLOv3 | Object detection | Q10 Suspicious Person Detection(1.57GB) | ✔ | - |
Q10_suspicious_person_detection_yolov3 | YOLOv3 | Object detection | Q10 Suspicious Person Detection(1.57GB) | - | ✔ | |
Q11_fish_detection | Q11_fish_detection_tinyyolov3 | TinyYOLOv3 | Object detection | Q11 Fish Detection(708MB) | ✔ | - |
Q11_fish_detection_yolov3 | YOLOv3 | Object detection | Q11 Fish Detection(708MB) | - | ✔ | |
Q12_yoga_pose_estimation | Q12_yoga_pose_estimation_custom_classifier | Custom model | Classification | Q12 Yoga Pose Estimation Dataset(15MB) | - | ✔ |
Q12_yoga_pose_estimation_HRNet | HRNet | Keypoint detection | Q12 COCO 17 Keypoint | - | ✔ |
To start using RZ/V AI Transfer Learning Tool, PC that can display the desktop with the following environment is recommended.
Description | Specification |
---|---|
Linux OS | Ubuntu 20.04 and above. |
Processor | Intel(R) Xeon(R) and above. |
GPU | Any GPU with CUDA. |
Storage | At least 100GB of free space is necessary. |
Description | Specification |
---|---|
Python | 3.10.5 |
Docker | 28.1.1 |
This chapter describes the installation procedure when using RZ/V AI Transfer Learning Tool alone.
tar -xzvf RTK0EF0178Z06000SJ_rzv-ai-tlt.tar.gz
Folder or File Name | Description | ||
---|---|---|---|
docs/ | r21ut0254ej0600-rzv-ai-tlt.pdf | User's Manual. | |
rzv_ai_tlt_v6.00/ | tlt_backend/ | install_docker.sh | Script to install TLT. |
launch_tlt_service.sh | Script to setup the TLT docker container. | ||
requirements.txt | Package list for installing TLT. | ||
uninstall_tlt.sh | Script to uninstall TLT. | ||
assets/ | TLT projects directory. Note: This directory is created by installation procedure. |
||
datasets/ | Datasets directory Note: This directory is created by installation procedure. |
||
start_rzv_ai_tlt_gui.sh | Launch the TLT GUI. |
tlt_backend
directory and install the tool.cd RTK0EF0178Z06000SJ_rzv-ai-tlt/rzv_ai_tlt_v6.00/tlt_backend
./install_docker.sh
./launch_tlt_service.sh
rzv_ai_tlt_v6.00
directory and launch the tool.cd ../
./start_rzv_ai_tlt_gui.sh
Once the installation is complete, you can retrain the model.
rzv_ai_tlt_v6.00/tlt_backend/assets/(any YAML file of TLT project)/config.yaml
.rzv_ai_tlt_v6.00/tlt_backend/assets/Q03_smart_parking/config.yaml
.Q03 Smart Parking
.rzv_ai_tlt_v6.00/tlt_backend/datasets/
.rzv_ai_tlt_v6.00/tlt_backend/datasets/Q03_smart_parking/
.rzv_ai_tlt_v6.00/tlt_backend/assets/(Your TLT prject name)
.rzv_ai_tlt_v6.00/tlt_backend/datasets/Q03_smart_parking/valid/occupied/0.jpg
.Q03_smart_parking
is a classification that predicts whether it is "occupied" or "empty", the result is displayed as a string.rzv_ai_tlt_v6.00/tlt_backend/assets/Q03_smart_parking_new/
.