Revert "fixed README.md file"

This reverts commit fcee07db5df946ec0de33aefeb928233cd7d89f0.

undid the README.md :wq
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README.md

@@ -1,230 +1,14 @@
-# CAR CLASSIFICATION - Brand, Model & Model Year
-
-This project is a deep learning pipeline that classifies car **brand**, **model**, and **model year** from a single image using a fine-tuned ConvNeXt model. It uses the [Stanford Cars dataset](https://huggingface.co/datasets/tanganke/stanford_cars) and leverages **transfer learning** with `facebook/convnext-large-224`. Built in **PyTorch**, this modular and scalable pipeline supports training, evaluation, and inference.
-
----
-
-## 🔍 Key Features
-
-- Download and preprocess image data from Hugging Face
-- Fine-tune pretrained ConvNeXt models (modern ConvNets inspired by transformers)
-- Track training metrics and model checkpoints
-- Predict the class of custom input images using saved models
-- Modular design for training, evaluation, and inference
-
-
----
-
-## 🧰 Installation
-
-
-## 🔧 Setup Instructions
-
-1. Clone the repo from GitHub
-``` bash
-git clone https://github.com/Brainster-Data-Science-Academy/CarClassificationTeam1  
-
-cd CarClassificationTeam1
-```
-
-2. Create and activate a virtual environment
-``` bash
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-  ```
-
-3. Install dependencies
-``` bash
-pip install -r requirements.txt
-```
-
-4. Download the dataset
-``` bash
-python-m src.data.download.download.py
-```
-
----
-
-## 📝 Requirements
-
-- Python 3.8+
-- PyTorch 2.3.0+cu126
-- torchvision 0.18.0+cu126
-- torchaudio 2.3.0+cu126
-- transformers
-- datasets
-- Other dependencies as listed in requirements.txt
-
----
-
-## 🧠 Model Architecture
-
-![ConvNeXt Architecture](images/convnext_architecture.png)
-
-We fine-tuned a pretrained [ConvNeXt](https://huggingface.co/facebook/convnext-base-224) vision transformer model:
-
-- **Model**: ConvNeXt-Base (224x224 resolution)
-- **Pretrained on**: ImageNet-1k
-- **Fine-tuned on**: Stanford Cars (196 classes)
-- **Transfer Learning**: Only the last **two ConvNeXt stages** and the **classification head** were trained
-
-
-Since the **Stanford Cars** dataset contains a relatively small number of training examples (~8,100 training and ~8,000 validation images), we adopted a **transfer learning** strategy. The ConvNeXt model was initialized with pretrained weights from ImageNet-1k, and only the final classification head was randomly initialized and fine-tuned for our 196 target classes.
-
-To balance generalization and training efficiency, we unfroze and trained only the last two stages of the ConvNeXt backbone (Stages 3 and 4), along with the classification head. Earlier layers remained frozen to preserve robust pretrained features.
-
-
-**Data Augmentation**:
-
-```python
-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.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),
-])
-```
-
----
-
-## 📊 Performance
-
-- **Train Accuracy**: `98.62%`
-- **Validation Accuracy**: `92.30%`
-- **Train Loss (Cross Entrophy)**: `0.9010`
-- **Validation Loss (Cross Entrophy)**: `1.1231`
-
----
-
-## 🚀 Usage (Example)
-
-```python
-from PIL import Image
-from transformers import AutoImageProcessor, ConvNextForImageClassification
-import torch
-
-# Load model and processor
-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")
-inputs = processor(images=image, return_tensors="pt")
-
-# Predict
-with torch.no_grad():
-    logits = model(**inputs).logits
-    predicted_class = logits.argmax(-1).item()
-
-print(f"Predicted class ID: {predicted_class}")
-```
-
----
-
-## 🏋️ Training Details
-
-- **Framework**: PyTorch
-- **Hardware**: NVIDIA RTX 4060
-- **Epochs**: 32 (early stopped training after 28 epochs)
-- **Batch Size**: 32
-- **Optimizer**: AdamW (lr=1e-4, weight_decay=1e-4)
-- **Loss Function**: Cross Entropy(label_smoothing=0.1)
-- **Scheduler**: ReduceLROnPlateau (factor=0.5, patience=2, min_lr=1e-6)
-
-![Training and Validation Metrics](reports/figures/Training_Validation-Loss_Accuracy.png)
-
-This result demonstrates the effectiveness of fine-tuning high-capacity pretrained models on medium-sized, domain-specific datasets. The model generalizes well despite visual similarities between different car models and years.
-
----
-
-## ⚠️ Limitations
-
-- Trained only on 196 classes from Stanford Cars (mostly 1990–2012 U.S. models)
-- Poor performance on:
-  - Damaged or modified vehicles
-  - Non-standard angles or lighting
-- Not suitable for unseen/new car models — retraining needed
-
----
-
-## 🛠 Project Details
-
-- **Developed by**: Todor Ristov, Goran Nikoloski, Milana Sokolova
-- **For**: TwinCar Project, Sols (Skopje, North Macedonia)
-- **Language**: Python
-- **Framework**: PyTorch
-- **License**: [MIT](LICENSE)
-
----
-
-## 🔗 Resources
-
-- 📚 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: [https://huggingface.co/spaces/todorristov/car-classification-convnext](https://huggingface.co/spaces/todorristov/car-classification-convnext)
-
 ---
-## 🤝 Contributing
-
-Contributions are welcome! Please open an issue or submit a pull request. Make sure to update tests and documentation as needed.
-
+title: Car Classification Convnext
+emoji: 👁
+colorFrom: yellow
+colorTo: blue
+sdk: gradio
+sdk_version: 5.34.2
+app_file: app.py
+pinned: false
+license: mit
+short_description: Car classification model  trained on Stanford Cars
 ---
-## 📂 Project Structure
- 
-```
-project_root/
-│
-├── images/                     # Model architecture visualizations
-│
-├── models/                     # Stores trained model checkpoints (e.g., best_model.pt)
-│   └── best_model.pt
-│
-├── notebooks/                  # Jupyter notebooks for model exploration and experiments
-│
-├── reports/                    # Training logs (loss, accuracy, LR, time, etc.)
-│
-├── src/                        # Source code
-│   ├── data/                   # Data-related scripts
-│   │   ├── datadownloader.py   # Downloads and saves dataset to local folders
-│   │   └── datatransforms.py   # Data augmentation and preprocessing transforms
-│   │
-│   ├── models/                 # Model utilities
-│   │   └── load_model.py       # Loads model, processor, and device
-│   │
-│   ├── utils/                  # Utility scripts
-│   │   └── save_label_map.py   # Saves class label map
-│   │
-│   ├── evaluate.py             # Evaluation logic per epoch
-│   ├── inference.py            # Inference script for classifying new images
-│   ├── train_utils.py          # Training helper functions (e.g., metric calc, logging)
-│   ├── train.py                # Main training script
-│   └── visualize.py            # Visualizations (e.g., confusion matrix, sample predictions)
-│
-├── README.md                   # Project documentation
-└── requirements.txt            # Project dependencies
-```
-
----
-
-## 💬 Citation
-
-```
-@misc{twin-car-classification,
-  title={Car Classification - Brand, Model & Model Year},
-  author={Todor Ristov},
-  year={2025},
-  howpublished={\url{https://huggingface.co/todorristov/car_classification_model}},
-  note={A deep learning pipeline for vehicle recognition.}
-}
-```
-
----
-
-Feel free to ⭐ the repo and share your feedback!
 
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference