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| title: Monitait Complete User Guide | |
| subtitle: WatcherJET 3.0 Hardware • AI Training Platform | |
| author: Monitait.com | |
| date: "2025-11-23" | |
| format: | |
| html: | |
| grid: | |
| sidebar-width: 300px | |
| body-width: 900px | |
| ::: {.callout-note} | |
| **Version:** 1.0 • **Date:** October 13, 2025 | |
| **Powered by Monitait** – [monitait.com](https://monitait.com) | |
| ::: | |
| ## Download Full Illustrated PDFs | |
| | Document | Pages | Download | | |
| |---------------------------------------------|-------|----------| | |
| | Chapter 1 – Hardware & WatcherJET 3.0 | 27 | [CHAP-1-AI-HARDWARE.pdf](CHAP-1-AI-HARDWARE.pdf) | | |
| | Chapter 2 – AI Training | 15 | [CHAP-2-AI-TRAINING.pdf](CHAP-2-AI-TRAINING.pdf) | | |
| *All diagrams, wiring schematics, and screenshots are inside the PDFs.* | |
| # 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 | |
| 4. Register at console.monitait.com/factory/watchers | |
| ### Step 1: Connect the Sensors | |
| #### External Machine Signal | |
| - **Production Count**: Any 12–24 V signal → OK inputs (3 & 4) | |
| - **Defect Count**: Ejector/NG signal → NG inputs (5 & 6) | |
| → Bidirectional, opto-isolated | |
| #### Push Button | |
| Production: Button → OK(4) + GND(1), bridge OK(3) → +V(2) | |
| Defects: Same using NG(5 & 6) | |
| #### Obstacle Sensor | |
| Black → OK/NG input | Brown → +V(2) | Blue → GND(1) | Bridge remaining → +V(2) | |
| #### Encoder | |
| White → NG(6) | Black → OK(4) | Brown → +V | Blue → GND | |
| Bridge (3) & (5) → +V(2) | |
| #### RS485 | |
| A → terminal 8 | B → terminal 7 | |
| ### Step 2: Power Supply | |
| 12–24 V DC, max 2 A → terminals (1 & 2) → green power LED on | |
| ### Step 3: Network | |
| **Best:** Wired LAN (allow `*.monitait.com`) | |
| **Temporary:** Hotspot → SSID: `Monitait`, Password: `p@ssword` | |
| ### Step 4: Register Watcher | |
| 1. console.monitait.com → Watchers → Add Watcher | |
| 2. Enter Registration ID | |
| 3. Test: trigger sensor → red heart LED blinks | |
| ### High-Current PSU, Emitters & Actuators | |
| - High-current ≤48 V → terminals (9 & 10) | |
| - Emitters: Positive → PSU, Negative → U(12) or B(11) | |
| - Ejector (15), Warning (16) → PLC (opto-isolated NPN) | |
| ### Keys & Indicators | |
| - Key-1: Buzzer on/off | |
| - Key-3: Camera enable | |
| - Key-4: Scanner enable | |
| - Green = power, Red heart = data, Checkmark = OK, Rejection = NG | |
| # Chapter 2 – AI Training | |
| ### Step 1: AI Training Platform – Annotation | |
| 1. Confirm task | |
| 2. Choose category | |
| 3. Draw bounding box | |
| 4. Save → next image | |
| **Keyboard shortcuts** (very fast): | |
| `1 2 3 4 Q W E R T Y U I` → instant category selection | |
| **Tips** | |
| - Overlapping objects → draw elsewhere → drag | |
| - No images visible → all done, use < > buttons | |
| - Metadata → click “+” in right panel | |
| ### Step 2: Administration – Create Task | |
| 1. Tasks → **+ NEW TASK** | |
| - Name, quantity, type, dates | |
| - Define labels/categories with colors | |
| 2. ⋮ → **Upload Images** (.jpg/.png/.jpeg) | |
| **Best Practices** | |
| - Visually distinct categories | |
| - Always label main object (e.g., “bottle”) | |
| - Use descriptive IDs (bottle-pk-100) | |
| - Monitait team handles training & augmentation | |
| ### Step 3: Evaluation | |
| | Term | Meaning | Formula | | |
| |-------|-----------------------------|-----------------------------| | |
| | TP | Correct detection | — | | |
| | FP | False alarm | — | | |
| | FN | Missed defect | — | | |
| | Precision | TP/(TP+FP) | Accuracy of detections | | |
| | Recall | TP/(TP+FN) | % of real defects found | | |
| | F1-Score | 2×(P×R)/(P+R) | Balanced score | | |
| | mAP | Mean Average Precision | Overall quality (higher = better) | | |
| **How to improve** | |
| - Diverse lighting/angles/backgrounds | |
| - Tight, consistent bounding boxes | |
| - Multiple reviewers | |
| - Avoid similar categories | |
| - Let Monitait team choose best augmentations | |
| ### Step 4: Deploy Model | |
| Successful training produces: | |
| - `best.pt` (weights) | |
| - Unique **Training ID** (record it!) | |
| **On production machine:** | |
| ```bash | |
| cp best.pt /home/projects/inference/best.pt | |
| # restart inference service → new model live |