Update readme

readme_.md

@@ -52,12 +52,4 @@ The table below reports the true class and Top-3 predictions for ViT, CLIP, and
 | `Dodge.jpg` | `Dodge` | BMW (0.3564), Dodge (0.2218), Rolls-Royce (0.1807) | Dodge (0.9432), Jeep (0.0393), Lamborghini (0.0078) | Dodge (1.00) |
 | `Ferrari.jpg` | `Ferrari` | Ferrari (0.6007), Lamborghini (0.2946), Ford (0.0296) | Ferrari (0.9958), Lamborghini (0.0032), Ford (0.0004) | Ferrari (1.00) |
 | `BMW.jpg` | `BMW` | BMW (0.2737), Porsche (0.1800), Dodge (0.1630) | BMW (0.9969), Porsche (0.0014), Ferrari (0.0007) | BMW (1.00) |
-| `Porsche.jpg` | `Porsche` | BMW (0.5858), Dodge (0.2040), Toyota (0.0667) | Porsche (0.9887), Lamborghini (0.0047), Dodge (0.0022) | Porsche (1.00) |
-
-## Model Comparison Summary
-
-| Model | Approach | Strengths | Weaknesses |
-|---|---|---|---|
-| **Custom ViT** | Supervised fine-tuning on 9 car brands | High accuracy on known brands | Only classifies the 9 trained brands |
-| **CLIP** | Zero-shot with brand name as text prompt | No training needed, flexible labels | Lower accuracy; may confuse visually similar brands |
-| **OpenAI GPT-4o** | LLM vision with natural language prompt | Strong reasoning, handles unusual angles | API cost, latency, black-box |
+| `Porsche.jpg` | `Porsche` | BMW (0.5858), Dodge (0.2040), Toyota (0.0667) | Porsche (0.9887), Lamborghini (0.0047), Dodge (0.0022) | Porsche (1.00) |