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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"
+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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     documentclass: scrreprt
-    classoption: ["paper=a4", "twoside=false"]
+    classoption:
+    - paper=a4
+    - twoside=false
+sdk: docker
+emoji: 🌍
+colorFrom: purple
+colorTo: indigo
+pinned: true
+short_description: Docs
 ---
 
-::: {.callout-note}
-**Version:** 1.0 • **Date:** October 13, 2025  
-**Powered by Monitait** – monitait.com
-:::
+# 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)
 
-# Chapter 1 – Hardware & WatcherJET 3.0
+---
+
+## Download Full Illustrated Manuals (PDF)
 
-![WatcherJET 3.0 Overview](images/watcherjet-cover.png){width=80% fig-align=center}
+| 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)                                      |
 
-## Table of Contents – Setup: Collecting Data
+All wiring diagrams, screenshots, and detailed illustrations are in these PDFs.
 
-- Step 1: Connect the sensors
-  - External machine signal
-  - Push button
-  - Obstacle sensor
-  - Encoder
-  - RS485 protocol
-- Step 2: Connect the power supply
-- Step 3: Provide an access point
-- Step 4: Register at console.monitait.com
+---
 
-## Table of Contents – Setup: Taking Action
+# Chapter 1 – Hardware & WatcherJET 3.0
 
-- Step 1: Connect the high-current power supply
-- Step 2: Set up the emitters
-- Step 3: Connect the actuators
-- Controls and Signals (Keys & Indicators)
-- QC Machines & Wiring Schematics
+### 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
-![External Machine Signal](images/sensors-external.png){width=90%}
-
-- **Production Count**: Connect any 12–24 V signal to OK inputs (3 & 4)  
-- **Counting Defects**: Connect ejector/machine NG signal to NG inputs (5 & 6)  
-  → Bidirectional & opto-isolated
+- 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
-![Push Button Wiring](images/sensors-pushbutton.png){width=90%}
-
-**Production Count** → OK (4) + GND (1), bridge OK (3) → +V (2)  
-**Defect Count** → same logic using NG (6 & 5)
+- Production: Button → OK (4) + GND (1); bridge OK (3) → +V (2)  
+- Defects: Same wiring using NG (5 & 6)
 
 #### Obstacle Sensor
-![Obstacle Sensor Wiring](images/sensors-obstacle.png){width=90%}
-
-Black → OK/NG input, Brown → +V (2), Blue → GND (1), bridge remaining OK/NG → +V (2)
-
-#### Encoder & RS485
-Encoder: White → NG (6), Black → OK (4), Brown → +V, Blue → GND  
-RS485: A → (8), B → (7)
-
-### Step 2: Connect the Power Supply
-**Specifications**
-
-| Parameter              | Value           |
-|------------------------|-----------------|
-| Input/Output Voltage   | 12–24 V DC      |
-| Max Output Current     | 2 A             |
-| Operating Temperature  | –10 °C to 50 °C |
-
-### Step 3: Provide an Access Point
-**Best Practice:** Wired LAN + firewall rule `*.monitait.com`  
-**Temporary:** Mobile hotspot → Name: `Monitait`, Password: `p@ssword`
+- 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)
 
-### Step 4: Register at console.monitait.com
-Add Watcher → Enter Registration ID → Set station & advanced options (multiplication factor, timeout, etc.)
+---
 
 # Chapter 2 – AI Training
 
-![AI Training Cover](images/ai-training-cover.png){width=70% fig-align=center}
-
-## Table of Contents
-
-- Step 1: AI Training Platform
-- Step 2: Administration (Create tasks & upload images)
-- Step 3: Evaluation
-- Step 4: Deploy trained weights
+### 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
-
-![Task List](images/ai-task-list.png){width=95%}
-
-#### Basic Tools – How to Annotate
-![Basic Annotation](images/ai-basic-tools.png){width=95%}
+### Step 1: AI Training Platform – Annotation
 
+#### Basic Workflow
 1. Confirm correct task  
 2. Choose category  
-3. Draw bounding box  
+3. Draw bounding box (click → drag → click)  
 4. Save → next image
 
-#### Advanced Tools
-![Advanced Tools](images/ai-advanced-tools.png){width=95%}
-
-- Image ID display
-- Show/Hide panels
-- Pan & zoom (Ctrl + scroll)
-- Edit / delete boxes
+#### Keyboard Shortcuts (fast annotation)
+`1 2 3 4 Q W E R T Y U I` → select categories instantly
 
-#### Tips & Hints
-![Tips](images/ai-tips.png){width=90%}
+#### 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
 
-- Keyboard shortcuts: 1–9, QWERTYUI for categories
-- Overlapping objects → draw elsewhere → drag, or hide with eye icon
-- Add metadata via “+” in right panel
+### Step 2: Administration – Create Task
 
-### Step 2: Administration – Create Tasks & Upload Images
-
-1. Tasks → **+ NEW TASK**
-   - Basic Info (name, quantity, type, dates, etc.)
-   - Labels → define categories & colors
+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 main object (e.g., “bottle”)
-- Use descriptive IDs (bottle-pk-100)
-- Monitait team handles training & augmentation
+- 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
 
-#### What is Evaluation?
-Testing the model on unseen data → measures real-world performance.
-
-#### Key Metrics
-- **TP** – correctly detected defect
-- **FP** – false alarm
-- **FN** – missed defect
-- **TN** – correctly identified good item
-
-| Metric   | Formula                          | Meaning                                  |
-|----------|----------------------------------|------------------------------------------|
-| Precision| TP / (TP + FP)                   | How many detections were correct         |
-| Recall   | TP / (TP + FN)                   | How many real defects were found         |
-| F1-Score | 2 × (P × R) / (P + R)            | Balanced score                           |
-| mAP      | Mean Average Precision (0–1)     | Overall model quality (higher = better)  |
-
-#### Tips to Increase Accuracy & Recall
-- Diverse dataset (lighting, angles, backgrounds)
-- Tight, consistent bounding boxes
-- Multiple reviewers
-- Avoid similar/confusing categories
-- Consult Monitait team for optimal augmentations
+#### 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
 
-Each successful training creates:
-- `best.pt` (final weights)
-- Unique **Training ID** (record it!)
-
-**Deployment Steps**
-1. Verify new `best.pt` performs better on sample images
-2. Copy to machine:  
-   `/home/projects/inference/best.pt`
-3. Restart the inference system
-
-The new model is now live.
-
----
+Every successful training produces:
+- `best.pt` (final weights)  
+- Unique **Training ID** → record it!
 
-::: {.callout-tip}
-For technical support or custom training strategies, contact the Monitait team at **monitait.com**
-:::
+**Deployment on production machine:**
+```bash
+cp best.pt /home/projects/inference/best.pt
+# Restart inference service → new model is live