Create README.md

README.md

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+---
+license: mit
+language:
+- en
+---
+
+## Model Summary
+
+- **Architecture**: Vision Transformer (ViT).
+- **Backbone**: Token embedding via image patches, Multi-Head Self-Attention (MHSA), and MLP blocks.
+- **Dataset**: [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (10 classes, 60k images).
+- **Training Framework**: PyTorch.
+- **Performance**: Demonstration-level training loop for illustration.
+
+---
+
+## Training Process
+
+1. **Dataset & Transforms**  
+   - We used CIFAR-10 (32×32 color images).  
+   - Images were resized to 224×224 to match the original ViT patching approach.  
+   - [Optional] Normalization can be applied as needed, e.g. using mean/std of CIFAR-10.  
+
+2. **Model Architecture**  
+   - Patches of size `P × P`.  
+   - Embedding dimension `D`.  
+   - Multi-Head Self-Attention with `k` heads.  
+   - MLP dimension of `mlp_size`.  
+   - A stack of `L` Transformer blocks.
+
+3. **Optimizer & Loss**  
+   - **Optimizer**: Adam (learning rate = 1e-4).  
+   - **Loss**: CrossEntropyLoss.  
+
+4. **Training Loop**  
+   - Standard PyTorch loop with mini-batches.  
+   - Multiple epochs.  
+   - Tracked the training loss and accuracy.
+
+---
+
+## How to Use the Model
+
+### 1. Installation
+
+Make sure you have the following libraries installed:
+
+```bash
+pip install torch torchvision matplotlib gradio huggingface_hub
+```
+
+### 2. Loading the Model
+
+If you have a local `vit_cifar_model.pth` (the trained state dict), you can load the model like this:
+
+```python
+import torch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Import or define your ViT class
+from model_definition import ViT  # your model code
+
+model_cifar = ViT().to(device)
+checkpoint = torch.load("vit_cifar_model.pth", map_location=device)
+model_cifar.load_state_dict(checkpoint)
+model_cifar.eval()
+```
+
+### 3. Inference on a Single Image
+
+```python
+from PIL import Image
+import torchvision.transforms as T
+
+transform_cifar = T.Compose([
+    T.Resize((224, 224)),
+    T.ToTensor(),
+])
+
+img = Image.open("some_image.jpg")  # Load an image
+x = transform_cifar(img).unsqueeze(0).to(device)  # shape [1, 3, 224, 224]
+
+with torch.no_grad():
+    logits = model_cifar(x)
+pred = torch.argmax(logits, dim=1).item()
+print("Predicted class:", pred)
+```
+
+---
+
+## Training & Evaluation Graphs
+
+Below is a conceptual summary of the typical outputs you might see after training. (In your code, these graphs are generated using Matplotlib.)
+
+1. **Training Loss Plot**
+
+
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66afb3f1eaf3e876595627bf/gSULEs5dTSDV2d42iCKzt.png)
+
+   > Shows the training loss decreasing over epochs.
+
+2. **Training Accuracy Plot**
+
+   ![Training Accuracy Plot](https://raw.githubusercontent.com/placeholder/placeholder/master/training_acc.png)
+   > Tracks the Test Accuracy: 41.51% percentage of correct predictions on the training set each epoch.
+
+3. **Test Set Accuracy**
+
+   ![Test Accuracy Plot](https://raw.githubusercontent.com/placeholder/placeholder/master/test_acc.png)
+   > Evaluates the model on the test set across epochs.
+
+4. **Confusion Matrix**
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66afb3f1eaf3e876595627bf/UX8dFV4OdwkorGVz1HXsV.png)
+
+   > Visual representation of true labels vs. predicted labels.
+
+*(Note: Replace placeholder image URLs with your actual plots if you have them hosted somewhere.)*
+
+---
+
+
+Classification Report:
+              precision    recall  f1-score   support
+
+           0     0.5618    0.4090    0.4734      1000
+           1     0.5385    0.3500    0.4242      1000
+           2     0.2884    0.2030    0.2383      1000
+           3     0.3481    0.1570    0.2164      1000
+           4     0.3686    0.5050    0.4262      1000
+           5     0.3280    0.3910    0.3568      1000
+           6     0.5423    0.4680    0.5024      1000
+           7     0.4477    0.4110    0.4286      1000
+           8     0.4668    0.5770    0.5161      1000
+           9     0.3602    0.6800    0.4709      1000
+
+    accuracy                         0.4151     10000
+   macro avg     0.4250    0.4151    0.4053     10000
+weighted avg     0.4250    0.4151    0.4053     10000
+
+
+###############################################################################
+# CELL: Vision Transformer Hyperparameters
+###############################################################################
+# Explanation:
+#   - This cell lists all important parameters for your ViT model.
+#   - Run it to either initialize them for the first time or to recap.
+
+# Batch size
+B = 2                   # e.g., for demonstration
+# Number of channels (RGB = 3)
+C = 3
+# Image height and width
+H = 224
+W = 224
+# Patch size
+P = 16
+# Number of patches (derived from H, W, and P)
+N = (H // P) * (W // P)
+# Embedding dimension
+D = 768
+# Number of attention heads
+k = 12
+# Dimension per head (must be compatible with D)
+Dh = D // k
+# Dropout probability
+p = 0.1
+# Hidden layer size for MLP inside the Transformer block
+mlp_size = 3072
+# Number of Transformer blocks (depth of the encoder)
+L = 12
+# Number of output classes (e.g., CIFAR-10 has 10 classes)
+n_classes = 10
+
+# Print them out in a structured format
+print("=== Vision Transformer Parameters ===")
+print(f"B (Batch Size):            {B}")
+print(f"C (Channels):              {C}")
+print(f"H (Image Height):          {H}")
+print(f"W (Image Width):           {W}")
+print(f"P (Patch Size):            {P}")
+print(f"N (Number of Patches):     {N}")
+print(f"D (Embedding Dimension):   {D}")
+print(f"k (Attention Heads):       {k}")
+print(f"Dh (Dim per Head):         {Dh}")
+print(f"p (Dropout Probability):   {p}")
+print(f"mlp_size (MLP Hidden):     {mlp_size}")
+print(f"L (Num Transformer Blocks): {L}")
+print(f"n_classes (Output Classes): {n_classes}")
+print("=====================================")
+
+
+
+
+
+## Integration with Gradio & Hugging Face Spaces
+
+### Gradio Demo
+
+A simple Gradio demo can be created to classify uploaded images:
+
+```python
+import gradio as gr
+import torch
+import torchvision.transforms as T
+from PIL import Image
+
+model_cifar.eval()
+
+class_names_cifar = [
+    "airplane", "automobile", "bird", "cat", "deer",
+    "dog", "frog", "horse", "ship", "truck"
+]
+
+def predict_cifar(img):
+    x = T.Compose([T.Resize((224, 224)), T.ToTensor()])(img).unsqueeze(0).to(device)
+    with torch.no_grad():
+        logits = model_cifar(x)
+    pred_id = torch.argmax(logits, dim=1).item()
+    return f"Prediction: {class_names_cifar[pred_id]}"
+
+gr.Interface(
+    fn=predict_cifar,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="ViT on CIFAR-10"
+).launch()
+```
+
+### Hugging Face Hub
+
+You can push the model and code to the Hugging Face Hub:
+
+```python
+from huggingface_hub import HfApi, HfFolder
+
+api = HfApi()
+repo_id = "username/my-cifar-vit"
+
+api.create_repo(repo_id=repo_id, exist_ok=True)
+api.upload_file(
+    path_or_fileobj="vit_cifar_model.pth",
+    path_in_repo="vit_cifar_model.pth",
+    repo_id=repo_id,
+    repo_type="model"
+)
+```
+
+Then create a Space with Gradio integration if you want a hosted web app.
+
+---
+
+## License
+
+[MIT License](LICENSE) or any license of your choice.
+
+---
+
+## Author
+
+- **Your Name** – [GitHub Profile](https://github.com/Haq-Nawaz-Malik)