fixed README.md file

README.md

@@ -81,8 +81,12 @@ transforms.Compose([
     transforms.RandomResizedCrop(image_size, scale=(0.8, 1.0), ratio=(0.75, 1.33)),
     transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
     transforms.RandomHorizontalFlip(),
+    transforms.RandomRotation(degrees=15),
+    transforms.RandomGrayscale(p=0.1),
     transforms.ToTensor(),
-    transforms.Normalize(mean=mean, std=std)
+    transforms.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)),
+    transforms.RandomErasing(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3)),
+    transforms.Normalize(mean=mean, std=std),
 ])
 ```
 
@@ -105,8 +109,8 @@ from transformers import AutoImageProcessor, ConvNextForImageClassification
 import torch
 
 # Load model and processor
-model = ConvNextForImageClassification.from_pretrained("sols/car-classification-convnext")
-processor = AutoImageProcessor.from_pretrained("sols/car-classification-convnext")
+model = ConvNextForImageClassification.from_pretrained("todorristov/car_classification_model")
+processor = AutoImageProcessor.from_pretrained("todorristov/car_classification_model")
 
 # Load and preprocess image
 image = Image.open("example.jpg").convert("RGB")
@@ -163,7 +167,7 @@ This result demonstrates the effectiveness of fine-tuning high-capacity pretrain
 - 📚 Stanford Cars Dataset: [https://huggingface.co/datasets/tanganke/stanford\_cars](https://huggingface.co/datasets/tanganke/stanford_cars)
 - 🤗 Model Card: [https://huggingface.co/sols/car-classification-convnext](https://huggingface.co/sols/car-classification-convnext)
 - 🌐 GitHub Repository: [https://github.com/Brainster-Data-Science-Academy/CarClassificationTeam1](https://github.com/Brainster-Data-Science-Academy/CarClassificationTeam1)
-- 🌟 Demo Space: Available on Hugging Face Spaces
+- 🌟 Demo Space: [https://huggingface.co/spaces/todorristov/car-classification-convnext](https://huggingface.co/spaces/todorristov/car-classification-convnext)
 
 ---
 ## 🤝 Contributing