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P&ID Drawing Analyzer Turn Paper Plant Diagrams into a Smart, Searchable Database. Converts scanned piping & instrumentation diagrams (P&IDs) into a connected digital model of your entire plant every pipe, valve, and instrument identified automatically and linked across all drawings. No manual data entry, no cloud dependency.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Deepfake Detection System
Problem: Deepfake accessibility creates real risks—misinformation, identity fraud, digital trust erosion. I built a production-grade detector: fast, accurate, explainable, CPU-deployable.
Approach: Transfer learning with ResNet-50 (25.5M params, pre-trained ImageNet-1K V2). Rather than training from scratch on limited data, I leveraged rich hierarchical features that transfer exceptionally well to detecting artifact patterns (blending boundaries, color mismatches, compression artifacts).
Key Decisions:
Face-Centric Preprocessing: OpenCV DNN crops faces to 224×224, concentrating attention on artifact-rich regions (eyes, mouth, jawlines).
Custom Classification Head: Bottleneck design (2048→512→1) with Focal Loss, label smoothing, progressive unfreezing—prevents overfitting on ~4,000 Ciplab images.
Explainable AI: Custom Grad-CAM generates attention heatmaps showing exactly where the model detected artifacts.
Real-World Robustness: Trained on JPEG compression, Gaussian noise, blur augmentations to handle compressed video feeds.
Results:
AUC-ROC: 0.9424 | Accuracy: 87.25% | Precision/Recall: 87.27%/84.81%
Inference: <100ms per face on CPU
Live on Hugging Face Spaces (no GPU)
Tech: PyTorch, OpenCV, Albumentations, Gradio. Images, videos, live webcam—one codebase.
Demonstrates: End-to-end AI engineering with deliberate trade-offs (efficiency over raw accuracy), training discipline (Focal Loss, Cosine Annealing), and production-first thinking (CPU compatibility, interpretability, real-world robustness).
