User Workflows
EdgeFirst Studio offers workflows tailored to your hardware and resources.
User Personas
The following diagram describes the workflow for identifying the user personas depending on the hardware requirements.
%%{init: {"flowchart": {"defaultRenderer": "elk"}} }%%
flowchart LR
%% User Definitions
user([User])
platform{Has a platform?}
edgefirst_platform{Has an EdgeFirst Platform?}
raivin_platform{Has a Raivin?}
record{Will use a smartphone to create a dataset?}
annotate_pd{Will explore annotating datasets?}
with_lidar{Has LiDAR integrated?}
tourist([Tourist]):::yellow
tourist_plus([Tourist+]):::pink
web_user([Web]):::indigo
maivin_user([Maivin]):::blue
raivin_user([Raivin]):::orange
raivin_lidar_user([LiDAR]):::darker_orange
imx8mp_user([i.MX 8M Plus]):::green
imx95_user([i.MX 95]):::teal
orin_user([Jetson Orin]):::purple
pi_user([Raspberry Pi 5]):::coral
other_platforms[Other Platforms]
classDef yellow fill:#fff2a8,font-weight:bold;
classDef pink fill:#f7b6d9,font-weight:bold;
classDef indigo fill:#c8d3ff,font-weight:bold;
classDef blue fill:#d0ecff,font-weight:bold;
classDef teal fill:#a2e8ed,font-weight:bold;
classDef orange fill:#ffd699,font-weight:bold;
classDef darker_orange fill:#ffbb66,font-weight:bold;
classDef green fill:#a9e5bb,font-weight:bold;
classDef purple fill:#d6c1f5,font-weight:bold;
classDef coral fill:#ffc2bb,font-weight:bold;
classDef all fill:#f0e6f5,font-weight:bold;
%% Flowchart
user --> platform
platform -- Yes --> edgefirst_platform
platform -- No --> record
record -- Yes --> web_user
record -- No --> annotate_pd
annotate_pd -- Yes --> tourist_plus
annotate_pd -- No --> tourist
edgefirst_platform -- Yes --> raivin_platform
edgefirst_platform -- No --> other_platforms
other_platforms --> imx8mp_user
other_platforms --> imx95_user
other_platforms --> orin_user
other_platforms --> pi_user
raivin_platform -- Yes --> with_lidar
with_lidar -- Yes --> raivin_lidar_user
with_lidar -- No --> raivin_user
raivin_platform -- No --> maivin_userPC Requirement
It is expected that for all personas identified above, the user has a PC with Wifi access.
We've identified ten workflows: Tourist, Tourist+, Web, i.MX 8M Plus, i.MX 95, Jetson Orin, Raspberry Pi 5, Maivin, Raivin, LiDAR. The hardware requirements and the available features increases starting with the Tourist as the most basic.
| Persona | Hardware | Features | Cost |
|---|---|---|---|
| Tourist | PC | Copy Dataset, Train, Validate, Deploy Offline or Browser | TBA |
| Tourist+ | PC | Annotate 2D, Train, Validate, Deploy Offline or Browser | TBA |
| Web | PC + Smartphone | Record, Annotate 2D, Train, Validate, Deploy Offline or Browser | TBA |
| i.MX 8M Plus (coming soon) | PC + i.MX 8M Plus | Copy Dataset, Train, Validate, Deploy on i.MX 8M Plus | TBA |
| i.MX 95 (coming soon) | PC + i.MX 95 | Copy Dataset, Train, Validate, Deploy on i.MX 95 | TBA |
| Jetson Orin (coming soon) | PC + Jetson Orin | Copy Dataset, Train, Validate, Deploy on Jetson Orin | TBA |
| Raspberry Pi 5 (coming soon) | PC + Raspberry Pi 5 | Copy Dataset, Train, Validate, Deploy on Raspberry Pi 5 | TBA |
| Maivin | PC + Maivin | Record, Annotate 2D, Train, Validate, Deploy on Maivin | TBA |
| Raivin | PC + Raivin w/ Radar | Record, Annotate 2D + 3D, Train, Validate, Deploy on Raivin | TBA |
| LiDAR (coming soon) | PC + Raivin w/ LiDAR | Record, Annotate 2D + 3D (enhanced), Train, Validate, Deploy on Raivin | TBA |
User Journey
The following diagram describes the workflows for each user persona identified above.
%%{init: {"flowchart": {"defaultRenderer": "elk", "nodeSpacing": 60}, "themeVariables": { "fontSize": "60px" }} }%%
flowchart LR
%% ---------------- USERS ----------------
subgraph Users
direction TB
users_pad[" "]:::invisible
tourist([Tourist]):::yellow
click tourist "tourist" "Open Tourist Workflow"
tourist_plus([Tourist+]):::pink
click tourist_plus "tourist_plus" "Open Tourist Plus Workflow"
web_user([Web]):::indigo
click web_user "web" "Open Web Workflow"
maivin_user([Maivin]):::blue
click maivin_user "maivin" "Open Maivin Workflow"
raivin_user([Raivin]):::orange
click raivin_user "raivin" "Open Raivin Workflow"
raivin_lidar_user([Raivin + LiDAR]):::darker_orange
imx8mp_user([i.MX 8M Plus]):::green
imx95_user([i.MX 95]):::teal
orin_user([Jetson Orin]):::purple
pi_user([Raspberry Pi 5]):::coral
end
%% ---------------- HARDWARE ----------------
subgraph Hardware
direction TB
setup_pad[" "]:::invisible
raivin_lidar_setup[Setup Raivin + LiDAR]:::darker_orange
click raivin_lidar_setup "../../platforms/quickstart/raivin/setup" "Open Raivin Setup"
raivin_setup[Setup Raivin]:::orange
click raivin_setup "../../platforms/quickstart/raivin/setup" "Open Raivin Setup"
maivin_setup[Setup Maivin]:::blue
click maivin_setup "../../platforms/quickstart/maivin/setup" "Open Maivin Setup"
imx8mp_setup[Setup i.MX 8M Plus]:::green
click imx8mp_setup "../../platforms/quickstart/imx8mplus/setup" "Open i.MX 8M Plus Setup"
imx95_setup[Setup i.MX 95]:::teal
click imx95_setup "../../platforms/quickstart/imx95/setup" "Open i.MX 95 Setup"
orin_setup[Setup Jetson Orin]:::purple
click orin_setup "../../platforms/quickstart/jetson_orin/setup" "Open Jetson Orin Setup"
pi_setup[Setup Raspberry Pi 5]:::coral
click pi_setup "../../platforms/quickstart/raspberrypi/setup" "Open Raspberry Pi Setup"
end
%% ---------------- DATA ----------------
subgraph Dataset
direction TB
record_mcap[Record MCAP]
click record_mcap "../../datasets/tutorials/capture/#record-mcap" "Record MCAP"
capture[Capture Phone Video/Images]
click capture "../../datasets/tutorials/capture/#capture-with-a-phone" "Capture Phone Video/Images"
snapshot[Upload MCAP]
click snapshot "../../datasets/tutorials/capture/#upload-mcap" "Upload MCAP"
copy_dataset[Copy Public Dataset]
click copy_dataset "../copy_dataset" "Copy Public Dataset"
audit_2d[Auto Annotate 2D]
click audit_2d "../../datasets/tutorials/annotations/automatic/" "Auto Annotate 2D"
audit_3d[Auto Annotate 3D]
click audit_3d "../../datasets/tutorials/annotations/automatic/" "Auto Annotate 3D"
end
%% ---------------- TRAINING ----------------
subgraph Training
direction TB
train_2d[Train Vision Model]
click train_2d "../../models/training/vision" "Open Training Vision"
ara2{Device has ARA2 processor?}
validate_2d[Validate Vision Model]
click validate_2d "../../models/validation/vision/user_managed" "Open Validating Vision"
train_3d[Train Fusion Model]
click train_3d "../../models/training/fusion" "Open Training Fusion"
validate_3d[Validate Fusion Model]
click validate_3d "../../models/validation/fusion/managed" "Open Validating Fusion"
end
%% ---------------- CONVERSION ----------------
subgraph Conversion
direction TB
neutron_converter[Neutron Converter]:::teal
click neutron_converter "../../models/ultralytics/neutron" "Open Neutron Converter"
kinara_converter[Kinara Converter]
validate_imx95[Validate Converted Model]:::teal
click validate_imx95 "../../models/validation/vision/user_managed" "Open Validating Vision"
end
%% ---------------- DEPLOYMENT ----------------
subgraph Deployment
direction LR
deploy_pad[" "]:::invisible
raivin((Raivin)):::orange
click raivin "../../platforms/quickstart/raivin/deploy" "Open Raivin Deploy"
maivin((Maivin)):::blue
click maivin "../../platforms/quickstart/maivin/deploy" "Open Maivin Deploy"
imx8mp((i.MX 8M Plus)):::green
click imx8mp "../../platforms/quickstart/imx8mplus/deploy" "Open i.MX 8M Plus Deploy"
orin((Jetson Orin)):::purple
click orin "../../platforms/quickstart/jetson_orin/deploy" "Open Jetson Orin Deploy"
pi((Raspberry Pi 5)):::coral
click pi "../../platforms/quickstart/raspberrypi/deploy" "Open Raspberry Pi Deploy"
browser((Browser))
imx95((i.MX 95)):::teal
click imx95 "../../platforms/quickstart/imx95/deploy" "Open i.MX 95 Deploy"
end
%% ---------------- USER → SETUP ----------------
raivin_lidar_user --> raivin_lidar_setup
raivin_user --> raivin_setup
maivin_user --> maivin_setup
imx8mp_user --> imx8mp_setup
imx95_user --> imx95_setup
orin_user --> orin_setup
pi_user --> pi_setup
%% ---------------- SETUP → DATA ----------------
raivin_lidar_setup --> record_mcap
raivin_setup --> record_mcap
maivin_setup --> record_mcap
web_user --> capture
imx8mp_setup --> copy_dataset
imx95_setup --> copy_dataset
orin_setup --> copy_dataset
pi_setup --> copy_dataset
tourist --> copy_dataset
tourist_plus --> copy_dataset
%% ---------------- DATA FLOW ----------------
record_mcap --> snapshot
snapshot --> audit_2d --> train_2d
snapshot -- LiDAR Only --> audit_3d --> train_3d
capture --> audit_2d
copy_dataset --> train_2d
copy_dataset -- Tourist+ --> audit_2d
%% ---------------- TRAINING PIPELINE ----------------
train_2d --> ara2
ara2 -- Yes --> kinara_converter --> validate_2d
ara2 -- No --> validate_2d
train_2d --> neutron_converter --> validate_imx95
train_3d --> validate_3d
%% ---------------- DEPLOYMENT ----------------
validate_2d --> browser
validate_2d --> maivin
validate_2d --> imx8mp
validate_2d --> orin
validate_2d --> pi
validate_2d --> raivin
validate_3d --> raivin
validate_imx95 --> imx95
%% ---------------- STYLES ----------------
classDef yellow fill:#fff2a8,font-weight:bold;
classDef pink fill:#f7b6d9,font-weight:bold;
classDef indigo fill:#c8d3ff,font-weight:bold;
classDef blue fill:#d0ecff,font-weight:bold;
classDef teal fill:#a2e8ed,font-weight:bold;
classDef orange fill:#ffd699,font-weight:bold;
classDef darker_orange fill:#ffbb66,font-weight:bold;
classDef green fill:#a9e5bb,font-weight:bold;
classDef purple fill:#d6c1f5,font-weight:bold;
classDef coral fill:#ffc2bb,font-weight:bold;
classDef invisible fill:transparent,stroke:transparent;Note
Labeled arrows suggests that only certain type of users can enter the stages pointed by the arrow. For example, only Raivin and LiDAR users can "Auto Annotate 3D".
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This workflow is intended for users with a personal computer with access to Wifi that want to use the sample datasets available for training, validating, and deploying Vision models.
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This workflow is intended for users with a personal computer with access to Wifi that want to use the same datasets available for annotating, training, validating, and deploying Vision models.
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This workflow is intended for users with a personal computer and a device with a camera with access to Wifi and a web browser. The examples shown in this workflow will be from a Windows computer and an Android phone for recording images. Proceed to this workflow to see capturing and annotating datasets that will be used to train, validate, and deploy Vision models.
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This workflow is intended for users with a personal computer with access to Wifi and a web browser and a Maivin platform. To proceed to this workflow, click on the link above.
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This workflow is intended for users with a personal computer with access to Wifi and a web browser and a Raivin platform. To proceed to this workflow, click on the link above.
Future Work
The workflows with missing links are a work in progress and currently unavailable.