google_drive_export/README.md

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.

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📁 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

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🏷️ 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

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📊 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

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⚡ 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:

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