| # 🔌 E-Waste PCB Component Purity & Cannibalization Index |
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| An Edge-AI system designed for Kabadiwala Connect to detect real-time, on-device PCB component cannibalization (the illicit stripping of valuable components like CPU sockets, gold edge connector fingers, IC microchips, and SMD tantalum capacitors). |
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| The system calculates a dynamic **Board Integrity Score (BIS 0–100)**, applies category scrap grade pricing floors, emits **8-language vernacular voice warnings** to scrap collectors, and tracks intake discrepancy tolerance bands ($<12\%$ pass, $12\text{--}25\%$ advisory, $>25\%$ fraud alert) to stop supply chain fraud. |
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| --- |
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| ## 📁 Repository & Directory Structure |
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| ``` |
| . |
| ├── README.md # Master project documentation |
| ├── PROJECT_CONTEXT_AND_ACTION_PLAN.md # Architectural specification & action plan |
| ├── huggingface_dataset/ # Formatted Hugging Face Hub dataset folder |
| │ ├── README.md # Dataset Card with YAML metadata |
| │ ├── data.yaml # YOLO dataset configuration |
| │ ├── train/ # 5,733 training image & label pairs |
| │ ├── val/ # 1,228 validation image & label pairs |
| │ └── test/ # 1,229 test image & label pairs |
| ├── splits/ # Local leak-free cluster-isolated splits |
| ├── purity_engine/ # Integrity Engine & Vernacular Voice Package |
| │ ├── model_client.py # Mock & TFLite INT8 model abstraction |
| │ ├── board_profiles.json # Baseline component expectations & scrap floors |
| │ ├── purity_assay.py # BIS calculation & dynamic pricing formula |
| │ ├── vernacular_voice.py # 8-language voice alert generator (.opus fallback) |
| │ └── pcb_analyzer.py # Unified orchestrator entry point |
| ├── training/ # Custom YOLOv8 training pipeline |
| │ ├── train.py # Fine-tuning & TFLite export script |
| │ └── train_config.yaml # Copy-paste & class-weighted loss config |
| ├── pipeline/ # Dataset harvesting, validation & dedup |
| │ ├── auto_annotate.py # Zero-shot YOLO-World auto-annotation engine |
| │ ├── crawler.py # Multi-threaded web harvester (16 threads) |
| │ ├── validator.py # Image integrity & resolution validator |
| │ ├── processor.py # EXIF transpose & image normalizer |
| │ ├── dedup.py # Exact & perceptual hash deduplication |
| │ └── splitter.py # Cluster-aware dataset partitioner |
| ├── prepare_hf_dataset.py # Formats splits into huggingface_dataset/ |
| └── tests/ # Automated pytest test suite (20 tests) |
| └── test_purity_assay.py |
| ``` |
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| --- |
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| ## 📊 Dataset Statistics & Hugging Face Hub CLI Upload |
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| The dataset contains **8,190 clean normalized JPEGs** and **6,377 fine-grained bounding boxes**. |
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| ### Dataset Partitioning |
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| | Split | Images & Bounding Box Pairs | Ratio | |
| | :--- | :--- | :--- | |
| | **Train** | **5,733 images** | $70\%$ | |
| | **Validation** | **1,228 images** | $15\%$ | |
| | **Test** | **1,229 images** | $15\%$ | |
| | **Total** | **8,190 images** | $100\%$ | |
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| ### 🚀 Pushing Dataset to Hugging Face via HF CLI |
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| The dataset is pre-formatted in `huggingface_dataset/` with YAML metadata. Push it directly to your Hugging Face Hub account using the Hugging Face CLI: |
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| ```bash |
| # 1. Login to Hugging Face |
| huggingface-cli login |
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| # 2. Upload the staged huggingface_dataset directory |
| cd huggingface_dataset |
| huggingface-cli upload <your-hf-username>/<your-dataset-name> . --repo-type=dataset |
| ``` |
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| --- |
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| ## 🏷️ 7 Fine-Grained Class Taxonomy |
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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 (**fraud signal**) | |
| | `5` | `factory_unpopulated_pad` | Clean silkscreen outline with smooth solder dots left empty (**NOT fraud**) | |
| | `6` | `occluded_region` | Footprints obscured by heatsinks, metal shielding, or heavy dust | |
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| --- |
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| ## 🏋️ Custom Model Training Pipeline |
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| Fine-tune YOLOv8 on the dataset using **Copy-Paste synthetic augmentation** (`copy_paste: 0.8`) and **elevated class loss gain** (`cls: 2.5`): |
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| ```bash |
| # Run training using project virtual environment |
| .venv/bin/python training/train.py --epochs 50 --imgsz 960 --batch 8 |
| ``` |
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| ### Dynamic Scrap Grade Price Formula |
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| $$\text{Adjusted Price} = \text{Base Price} \times \left( \text{Floor}_{\text{category}} + (1 - \text{Floor}_{\text{category}}) \times \frac{\text{BIS}}{100} \right)$$ |
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| * **Server / GPU PCBs**: $\text{Floor} = 15\%$ |
| * **Desktop PCBs**: $\text{Floor} = 30\%$ |
| * **Low-Grade Power PCBs**: $\text{Floor} = 60\%$ |
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| --- |
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| ## 🛡️ Running Integrity Assay Engine (`purity_engine`) |
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| ```python |
| from purity_engine import PCBAnalyzer |
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| analyzer = PCBAnalyzer() |
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| # 2-Photo Workflow (Front + Back PCB Photos) |
| result = analyzer.analyze_board( |
| front_image="front_top_down.jpg", |
| back_image="back_bottom_down.jpg", |
| base_price=100.0, |
| form_factor="desktop_atx", |
| lang="hi", # Hindi voice alert |
| ) |
| |
| print(f"Board Integrity Score: {result['board_integrity_score']}%") |
| print(f"Adjusted Purchase Price: ₹{result['adjusted_price']}") |
| print(f"Voice Alert: {result['voice_alert']['transcript']}") |
| ``` |
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| --- |
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| ## 🧪 Running Automated Unit Tests |
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| ```bash |
| .venv/bin/python -m pytest tests/test_purity_assay.py tests/test_pipeline.py |
| ``` |
| *(All 20 unit tests passing)* |
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