Spaces:
Sleeping
Sleeping
| title: Monitait Step-by-Step User Guide | |
| subtitle: Hardware (WatcherJET 3.0) & AI Training Platform | |
| author: Monitait.com | |
| date: '2025-10-13' | |
| format: | |
| html: | |
| toc: true | |
| toc-depth: 4 | |
| toc-location: left | |
| theme: cosmo | |
| code-fold: true | |
| grid: | |
| sidebar-width: 300px | |
| body-width: 900px | |
| margin-width: 300px | |
| pdf: | |
| toc: true | |
| keep-tex: true | |
| documentclass: scrreprt | |
| classoption: | |
| - paper=a4 | |
| - twoside=false | |
| sdk: docker | |
| emoji: 🌍 | |
| colorFrom: purple | |
| colorTo: indigo | |
| pinned: true | |
| 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](https://monitait.com) | |
| --- | |
| ## Download Full Illustrated Manuals (PDF) | |
| | Document | Pages | Link | | |
| |---------------------------------------------|-------|---------------------------------------------------------------------------------------| | |
| | 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 wiring diagrams, screenshots, and detailed illustrations are in these 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 (AP)** | |
| 4. **Register at** [console.monitait.com/factory/watchers](https://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) | |
| --- | |
| # 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:** | |
| ```bash | |
| cp best.pt /home/projects/inference/best.pt | |
| # Restart inference service → new model is live |