README.md

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title: Monitait Step-by-Step User Guide
subtitle: Hardware (WatcherJET 3.0) & AI Training Platform
author: Monitait.com
date: '2025-10-13'
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short_description: Docs

Monitait Step-by-Step User Guide

WatcherJET 3.0 Hardware + AI Training Platform
Version: 1.0 – Date: October 13, 2025
Powered by Monitait – monitait.com

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Download Full Illustrated Manuals (PDF)

Document	Pages	Link
Chapter 1 – Hardware & WatcherJET 3.0	27	CHAP-1-AI-HARDWARE.pdf
Chapter 2 – AI Training	15	CHAP-2-AI-TRAINING.pdf

All wiring diagrams, screenshots, and detailed illustrations are in these PDFs.

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Chapter 1 – Hardware & WatcherJET 3.0

Quick Setup (4 Steps)

1. Connect the sensors
2. Connect the power supply (12–24 V DC)
3. Provide an access point (AP)
4. Register at console.monitait.com/factory/watchers

Step 1: Connect the Sensors

External Machine Signal

• Production Count → Connect any 12–24 V signal to OK inputs (3 & 4)
• Defect Count → Connect ejector/NG signal to NG inputs (5 & 6)
  → Bidirectional, opto-isolated

Push Button

• Production: Button → OK (4) + GND (1); bridge OK (3) → +V (2)
• Defects: Same wiring using NG (5 & 6)

Obstacle Sensor

• Black wire → OK or NG input
• Brown → +V (2)
• Blue → GND (1)
• Bridge remaining OK/NG → +V (2)

Encoder

White → NG (6) | Black → OK (4) | Brown → +V | Blue → GND
Bridge (3) & (5) → +V (2)

RS485 Protocol

A → terminal 8 | B → terminal 7

Step 2: Power Supply

• 12–24 V DC, max 2 A
• Connect to terminals (1) & (2) → green power LED turns on

Step 3: Network Connection

• Best: Wired LAN (add firewall rule *.monitait.com)
• Temporary: Mobile hotspot → SSID: Monitait, Password: p@ssword

Step 4: Register the Watcher

1. Go to console.monitait.com → Watchers → Add Watcher
2. Enter Registration ID (shown on phone when connected to hotspot)
3. Set station, multiplication factor, timeout, etc.
4. Test: Trigger sensor → red heart LED must blink

High-Current Power, Emitters & Actuators

• High-current PSU ≤ 48 V → terminals (9 & 10)
• Emitters: Positive → PSU, Negative → U (12) or B (11) depending on alignment
• Ejector output (15), Warning output (16) → connect to PLC (opto-isolated NPN)

Keys & Indicators

• Key-1: Buzzer on/off
• Key-3: Enable camera
• Key-4: Enable scanner
• Green power, red heart (data), checkmark (OK), rejection (NG), thunder (PPS)

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Chapter 2 – AI Training

Table of Contents

• Step 1: AI Training Platform
• Step 2: Administration (Tasks & Images)
• Step 3: Evaluation
• Step 4: Deploy Weights

Step 1: AI Training Platform – Annotation

Basic Workflow

1. Confirm correct task
2. Choose category
3. Draw bounding box (click → drag → click)
4. Save → next image

Keyboard Shortcuts (fast annotation)

1 2 3 4 Q W E R T Y U I → select categories instantly

Tips

• Overlapping objects → draw elsewhere then drag, or hide with eye icon
• No images visible → all annotated; use < > buttons
• Add metadata (optional) via “+” in right panel

Step 2: Administration – Create Task

1. Tasks → + NEW TASK
   • Enter name (e.g., “Bottle Defect Detection”)
   • Set quantity, type (object detection, classification, etc.)
   • Add labels/categories with colors
2. After creation → ⋮ → Upload Images (.jpg, .png, .jpeg)

Best Practices

• Use visually distinct categories
• Always label the main object (e.g., “bottle”)
• Use descriptive IDs (bottle-pk-100, pen-dp-105)
• Monitait team performs training and selects best augmentations

Step 3: Evaluation

Key Concepts

• TP = correct detection
• FP = false alarm
• FN = missed defect
• TN = correctly identified good item

Metric	Formula	Meaning
Precision	TP / (TP + FP)	Accuracy of positive predictions
Recall	TP / (TP + FN)	How many real defects were found
F1-Score	2 × (P × R) / (P + R)	Balance between precision & recall
mAP	Mean Average Precision	Overall model quality (higher = better)

How to Improve Accuracy & Recall

• Diverse dataset (lighting, angles, backgrounds)
• Tight, consistent bounding boxes
• Multiple reviewers
• Avoid similar/confusing categories
• Consult Monitait team for optimal augmentation and training settings

Step 4: Deploy Trained Model

Every successful training produces:

• best.pt (final weights)
• Unique Training ID → record it!

Deployment on production machine:

cp best.pt /home/projects/inference/best.pt
# Restart inference service → new model is live