deepfake-detector-v12

Deepfake Detector V12 - RAM Optimized (2 Hour Runtime)

🎯 Production-Grade Fine-tuned Ensemble (16K Samples, 2 Epochs)

Built on V11, RAM-Safe Training for 2 Hour Runtime

This is V12 RAM Optimized, a fine-tuned version of the V11 ensemble detector with 30 real datasets, minimal synthetic generation, and 2-epoch high-quality fine-tuning optimized for RAM safety and 2 hour runtime.

📊 Performance

V12 Ensemble Performance (Test Set - NEVER SEEN):

• Test Accuracy: 97.94%
• Test Precision: 0.9957
• Test Recall: 0.9486
• Test F1 Score: 0.9715

Individual Models:

• Model 1: 95.95% val accuracy ✓ from V11
• Model 2: 97.40% val accuracy ✓ from V11
• Model 3: 96.25% val accuracy ✓ from V11

Successfully loaded 3/3 models from V11!

⚡ RAM Optimizations

Training Configuration:

• Epochs: 2 (high-quality fine-tuning)
• Batch Size: 32 (RAM safe)
• Target Samples: 16K (reduced for RAM)
• Pin Memory: Enabled
• Num Workers: 2 (parallel loading)
• Device: GPU (CUDA) or CPU
• Expected RAM: ~5-6GB during training
• Training Time: ~1.5 hours

RAM Safety Strategy:

• Reduced samples: 16K vs 30K (47% less data)
• Smaller batches: 32 vs 64 (50% less per batch)
• Same dataset diversity: All 50 datasets still used
• Per-dataset targets unchanged
• Should stay well under 12GB RAM

📦 Dataset Strategy

Real Images (30 Datasets) - UNCHANGED:

• Core datasets: beans, cats_vs_dogs, tiny-imagenet, flowers, oxford-pets
• Classification: cifar10, mnist, fashion_mnist, caltech101, food101
• Specialized: stanford_dogs, gtsrb, eurosat, aircraft, sun397
• Medical/Scientific: patch_camelyon, NIH chest x-rays
• Target: ~8K real images with minimal synthetic (<1.5K if needed)

Fake Images (20 Datasets) - UNCHANGED:

• GAN datasets: AFHQ, pokemon, wikiart, metfaces, celeba
• Style transfer: winter2summer, horse2zebra, watercolor2photo
• Diffusion models: pokemon-gpt4-captions, few-shot-universe
• Target: ~8K fake images with minimal synthetic (<1.5K if needed)

🎯 Key Features

1. 2 Epochs: High-quality fine-tuning from V11 base
2. RAM Safe: 16K samples, batch 32
3. Same Datasets: All 50 datasets still used (30 real + 20 fake)
4. Minimal Synthetic: Only if <70% of target reached
5. GPU Accelerated: Optimized for both GPU and CPU
6. Fine-tuned from V11: Transfer learning from proven V11 architecture

💾 Training Details

• Training Time: 23.0 minutes (~0.4h)
• Epochs per Model: 2
• Batch Size: 16 (RAM optimized)
• Target Samples: 10,000
• Models Loaded from V11: 3/3
• Real Datasets: 31 (unchanged)
• Fake Datasets: 20 (unchanged)
• Synthetic Used: Minimal (only if needed)

🛡️ Anti-Memorization

80/10/10 Split (STRICT)

• Training: 80% (10,470 samples)
• Validation: 10% (1,308 samples)
• Test: 10% (1,310 samples) - NEVER SEEN

📄 License

MIT License

---

Model Version: V12 RAM Optimized (16K Dataset, 2 Epochs) Base Model: ash12321/deepfake-detector-v11 Release Date: 2025-11-06 Training Time: ~1.5 hours Status: Production Ready ✅ (RAM Safe + High-Quality Fine-tuning)