| # 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) | |