# E-Waste PCB Component Purity & Cannibalization Index - Google Drive Package

This folder contains all project documentation, updated dataset configurations, pipeline source code, and auto-annotated YOLO label specs for the **Component Purity & Cannibalization Index** feature.

---

## 📁 Drive Folder Structure

```
google_drive_export/
├── README.md                                # Package documentation & usage guide
├── docs/                                    # Master project planning & context docs
│   ├── PROJECT_CONTEXT_AND_ACTION_PLAN.md   # Master context, requirements & roadmap
│   ├── implementation_plan_component_purity_index.md # Component purity index architecture plan
│   ├── implementation_plan.md               # Scraper pipeline implementation plan
│   └── scraper_plan_v2.md                   # Dataset pipeline architecture spec v2
├── config/                                  # Project configuration files
│   ├── taxonomy.yaml                        # Fine-grained 7-class component taxonomy
│   ├── queries.yaml                         # Search query matrix for e-waste & desoldered pads
│   └── config.yaml                          # Pipeline & aspect ratio validation settings
├── pipeline/                                # Source code scripts
│   ├── auto_annotate.py                     # Zero-shot YOLO-World auto-annotation script
│   ├── crawler.py                           # URL harvesting engine
│   ├── validator.py                         # Image integrity & resolution validator
│   ├── processor.py                         # EXIF transpose & image normalizer
│   ├── dedup.py                             # Exact & perceptual hash deduplication engine
│   ├── manifest.py                          # SQLite state store abstraction layer
│   ├── queries.py                           # Query expansion generator
│   └── splitter.py                          # Cluster-aware dataset splitter
└── dataset_annotated/                       # Auto-annotated dataset files
    ├── data.yaml                            # YOLO dataset configuration spec
    ├── auto_annotation_summary.json         # Auto-annotation statistics summary
    └── labels/                              # 3,568 YOLO format (.txt) bounding box label files
```

---

## 🏷️ Fine-Grained Component Taxonomy (`taxonomy.yaml`)

| Class ID | Class Name | Description |
| :--- | :--- | :--- |
| `0` | `cpu_socket` | LGA, PGA, or ZIF processor socket footprints |
| `1` | `gold_finger` | Gold-plated edge connector contacts (PCIe, RAM contacts) |
| `2` | `ic_chip` | Packaged ICs and BGA microchips |
| `3` | `tantalum_capacitor` | SMD tantalum capacitors (molded yellow/black/orange) |
| `4` | `forcibly_desoldered_pad` | Exposed pads with jagged solder, blowtorch marks, torn traces, burnt flux (**fraud signal**) |
| `5` | `factory_unpopulated_pad` | Clean silkscreen outline with smooth solder dots left empty from factory (**NOT fraud**) |
| `6` | `occluded_region` | Footprints obscured by heatsinks, metal shielding, or heavy dust |

---

## 📊 Dataset Statistics (`auto_annotation_summary.json`)

* **Total Images Processed**: 4,303 images (3,923 clean normalized JPEGs)
* **Total Bounding Boxes Generated**: 3,784 draft bounding boxes
* **Class Detections**:
  * `cpu_socket`: 3,570
  * `tantalum_capacitor`: 121
  * `ic_chip`: 67
  * `occluded_region`: 10
  * `gold_finger`: 7
  * `factory_unpopulated_pad`: 6
  * `forcibly_desoldered_pad`: 3

---

## ⚡ Next Steps & Usage Guide

1. **Upload to Google Drive**: Upload this entire `google_drive_export` folder (or `ewaste_purity_index_drive_package.zip`) to your Google Drive.
2. **Annotation Review**: Import `dataset_annotated/data.yaml` and `dataset_annotated/labels/` alongside your images into **CVAT**, **Label Studio**, or **Roboflow** for annotation review.
3. **YOLO Model Fine-Tuning**: Pass `dataset_annotated/data.yaml` to `ultralytics` to train your baseline YOLOv8 model:
   ```python
   from ultralytics import YOLO

   model = YOLO("yolov8n.pt")
   model.train(data="dataset_annotated/data.yaml", epochs=50, imgsz=960)
   ```