Update readme

readme_.md

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 This app compares 3 image classification approaches on car images:
 
-- Fine-tuned ViT model ([`nenzilea/car-classification`](https://huggingface.co/nenzilea/car-classification))
+- Fine-tuned ViT model (`nenzilea/car-classification`)
 - Zero-shot CLIP (`openai/clip-vit-large-patch14`)
 - OpenAI vision model (GPT-4o image classification)
 
 ## Dataset Used For Training
 
-- Hugging Face dataset: `tanganke/stanford_cars`
+- Hugging Face dataset: https://huggingface.co/datasets/tanganke/stanford_cars
 - The Stanford Cars dataset contains 196 fine-grained classes (car make/model/year combinations). We group them into 9 brand-level classes for a cleaner, more visually meaningful classification task.
 - Number of classes: `9`
 - Classes: `BMW`, `Dodge`, `Ferrari`, `Ford`, `Jeep`, `Lamborghini`, `Porsche`, `Rolls-Royce`, `Toyota`
@@ -24,7 +24,7 @@ This app compares 3 image classification approaches on car images:
 
 ## Trained Model
 
-- Hugging Face model link: [https://huggingface.co/nenzilea/car-classification]
+- Hugging Face model link: https://huggingface.co/nenzilea/car-classification
 - Base model: `google/vit-base-patch16-224`
 - Only the final classification head was fine-tuned (all other layers frozen).
 - Trainable parameters: ~4,614 out of ~85.8M total.
@@ -41,7 +41,7 @@ This app compares 3 image classification approaches on car images:
 
 ## Hugging Face Space
 
-- App link: [https://huggingface.co/spaces/nenzilea/car-classification](https://huggingface.co/spaces/nenzilea/car-classification)
+- App link: https://huggingface.co/spaces/nenzilea/car-classification
 
 ## Example Image Results