| # E-Waste PCB Component Purity & Cannibalization Index - Google Drive Package |
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| 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 |
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| ``` |
| 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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| --- |
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| ## 🏷️ Fine-Grained Component Taxonomy (`taxonomy.yaml`) |
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| | 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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| --- |
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| ## 📊 Dataset Statistics (`auto_annotation_summary.json`) |
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| * **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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| --- |
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| ## ⚡ Next Steps & Usage Guide |
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| 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) |
| ``` |
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