MODEL_CARD.md

Model Card: Lithotheque Edge AI Models

Model Details

• Developer: GeoStratum
• Model Versions: VisionAiManager (MobileNetV5-300m / MobileNetV4-Large) and LocalAiManager (Metrology).
• Model Type: Convolutional Neural Networks (CNN) optimized for Edge Inference (INT8/INT4 Quantization).
• License: Proprietary (GeoStratum).
• Training Data: Hybrid corpus of proprietary metrology images and open-source MIT-licensed geological datasets (Udayl, Stealth Technologies).

Intended Use

• Primary Use Case: Offline, on-device geological and lithological classification for field geologists, students, and enthusiasts.
• Secondary Use Case: Scale detection and topographical metrology mapping via reference objects.
• Target Environment: Mobile devices operating in remote areas without internet connectivity.

Out-of-Scope Use

These models are designed for educational and preliminary field classification. They MUST NOT be used for:

• Critical Engineering Decisions: Assessing rock mechanics, stability, or load-bearing capacities for civil engineering or construction.
• Financial/Mining Exploration: Making high-stakes financial decisions regarding resource extraction or ore grade estimation.
• Safety: Detecting hazardous environments or toxic minerals for safety compliance. Always consult laboratory analysis (e.g., XRD, XRF) for definitive geological validation.

Metrics & Performance

• Accuracy (Standardized Multi-Source Benchmark):
  • MobileNetV5-300M (Premium / Standard Tier): Up to 88.33% Top-1 / 92.67% Top-3 in baseline FP16/BF16, 83.33% Top-1 / 89.33% Top-3 in INT8 (W8A8), and 80.33% Top-1 / 87.67% Top-3 in W4A8 Full Integer.
  • MobileNetV4-Large (Balanced / Legacy Tier): 78.78% Top-1 / 91.37% Top-3 in baseline FP16/BF16, 77.84% Top-1 / 90.42% Top-3 in INT8, and 73.91% Top-1 / 87.15% Top-3 in W4A8 (reaching >91% Top-1 on in-situ daylight field subsets).
• Latency: Sub-3 ms on dedicated NPU hardware for MobileNetV4-Large (~400 FPS steady-state), and 12–18 ms for 1024×1024 Scale Detection.
• For comprehensive hardware profiling, please refer to the main README.md.

Limitations & Bias

• Occlusion & Surface Conditions: Accuracy degrades if the rock sample is heavily covered in mud, moss, or artificial coatings. Freshly broken surfaces yield the most accurate predictions.
• Lighting Dependency: Extreme low-light conditions or harsh direct camera flash may alter texture perception, slightly increasing the margin of error.
• Micro-crystalline vs. Cryptocrystalline: Some visually identical rocks require chemical or microscopic analysis for definitive differentiation. The model predicts the highest mathematical probability based strictly on visual morphological features.