FaustDerLegende/Micro_component_dataset

🔌 E-Waste PCB Component Purity & Cannibalization Index

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).

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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📁 Repository & Directory Structure

.
├── 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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📊 Dataset Statistics & Hugging Face Hub CLI Upload

The dataset contains 8,190 clean normalized JPEGs and 6,377 fine-grained bounding boxes.

Dataset Partitioning

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	$100%$

🚀 Pushing Dataset to Hugging Face via HF CLI

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:

# 1. Login to Hugging Face
huggingface-cli login

# 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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🏷️ 7 Fine-Grained Class Taxonomy

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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🏋️ Custom Model Training Pipeline

Fine-tune YOLOv8 on the dataset using Copy-Paste synthetic augmentation (copy_paste: 0.8) and elevated class loss gain (cls: 2.5):

# Run training using project virtual environment
.venv/bin/python training/train.py --epochs 50 --imgsz 960 --batch 8

Dynamic Scrap Grade Price Formula

$$\text{Adjusted Price}=\text{Base Price}\times ({\text{Floor}}_{\text{category}}+(1-{\text{Floor}}_{\text{category}})\times \frac{\text{BIS}}{100})\backslash text\{Adjusted\; Price\}\; =\; \backslash text\{Base\; Price\}\; \backslash times\; \backslash left(\; \backslash text\{Floor\}\_\{\backslash text\{category\}\}\; +\; (1\; -\; \backslash text\{Floor\}\_\{\backslash text\{category\}\})\; \backslash times\; \backslash frac\{\backslash text\{BIS\}\}\{100\}\; \backslash right)$$

• Server / GPU PCBs: $\text{Floor} = 15%$
• Desktop PCBs: $\text{Floor} = 30%$
• Low-Grade Power PCBs: $\text{Floor} = 60%$

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🛡️ Running Integrity Assay Engine (purity_engine)

from purity_engine import PCBAnalyzer

analyzer = PCBAnalyzer()

# 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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🧪 Running Automated Unit Tests

.venv/bin/python -m pytest tests/test_purity_assay.py tests/test_pipeline.py

(All 20 unit tests passing)