Update README.md (#7)

- Update README.md (528fa9c9343c0f685957b428b97976d097af3261)

README.md

@@ -108,23 +108,23 @@ print(f"verdict: {'fake' if fake_prob > 0.5 else 'real'}")
 
 Five pre-exported ONNX models with different size/speed trade-offs. All use the corrected config (single-class sigmoid output).
 
-| Variant | Size | Speed (CPU) | Accuracy | Best For |
+| Variant | Size | Speed (CPU) | Fidelity vs FP32 | Best For |
 |---|---|---|---|---|
-| `model.onnx` (full) | 84 MB | ★★★ | ★★★★★ | Maximum accuracy, server-side |
-| `model_int8.onnx` | 22 MB | ★★★★★ | ★★★★ | **Fastest CPU**, general deployment |
-| `model_uint8.onnx` | 22 MB | ★★★★★ | ★★★★ | Fast CPU, unsigned variant |
-| `model_quantized.onnx` | 22 MB | ★★★★★ | ★★★★ | Alias of INT8 for compatibility |
-| `model_q4.onnx` | 16 MB | ★★★ | ★★★ | Smallest, low disk/RAM |
+| `model.onnx` (full) | 83 MB | ★★★ | Reference (FP32) | Maximum accuracy, server-side baseline |
+| `model_int8.onnx` | 22 MB | ★★★★★ | High fidelity on standard inputs; may diverge on OOD generators | **Fastest CPU**, general deployment |
+| `model_uint8.onnx` | 22 MB | ★★★★★ | Alternative dynamic quantization error profile | Fast CPU deployment |
+| `model_quantized.onnx` | 22 MB | ★★★★★ | Identical to `model_int8.onnx` | Drop-in INT8 alias |
+| `model_q4.onnx` | 15 MB | ★★★ | Aggressive weight quantization; high variance on subtle inputs | Smallest disk/RAM footprint |
 
 **Which variant should I use?**
 
 | Use case | Recommended variant | Why |
 |---|---|---|
-| Server-side, maximum accuracy | `model.onnx` (full) | No quantization loss, FP32 precision |
-| General CPU deployment | `model_int8.onnx` | Fastest CPU inference, strong accuracy on real-world images |
-| Disk/RAM constrained | `model_q4.onnx` | Smallest file size, slower inference |
+| Server-side, maximum accuracy | `model.onnx` (full) | No quantization loss, FP32 precision — reference baseline |
+| General CPU deployment | `model_int8.onnx` | Fastest CPU inference, matches FP32 on clear-cut inputs |
+| Disk/RAM constrained | `model_q4.onnx` | Smallest file size (15 MB), low disk/RAM footprint |
 
-> **Quantization note**: Dynamic quantization accuracy varies by input. On real-world photos and AI-generated images, all variants agree with FP32 within ~1%. On synthetic noise or extreme aspect ratios, quantized variants may diverge more. For maximum accuracy, use `model.onnx` (FP32).
+> **Quantization note**: Dynamic per-tensor quantization without calibration causes quantized variants to diverge from FP32 on certain inputs (up to 10–70 percentage points) — particularly images from generators outside the training set. **Significant disagreement between FP32 and INT8/Q4 indicates the input is near the model's decision boundary or out-of-distribution.** For maximum single-model consistency, use `model.onnx` (FP32).
 
 ```python
 import onnxruntime as ort, numpy as np