Update README.md

README.md

@@ -1,111 +1,112 @@
----
-license: mit
-library_name: pytorch
-tags:
-- image-classification
-- cifar10
-- cnn
-- computer-vision
-- pytorch
-- mini-vision
-- mini-vision-series
-metrics:
-- accuracy
-pipeline_tag: image-classification
-datasets:
-- uoft-cs/cifar10
----
-
-# Mini-Vision-V1: CIFAR-10 CNN Classifier
-
-![Model Size](https://img.shields.io/badge/Params-1.34M-blue) ![Accuracy](https://img.shields.io/badge/Accuracy-78%25-green)
-
-Welcome to **Mini-Vision-V1**, the first model in the Mini-Vision series. This project demonstrates a robust implementation of a Convolutional Neural Network (CNN) for image classification using the CIFAR-10 dataset. It is designed to be lightweight, efficient, and easy to understand, making it perfect for beginners learning PyTorch.
-
-## Model Description
-
-Mini-Vision-V1 is a custom 4-layer CNN architecture. It utilizes Batch Normalization and Dropout to prevent overfitting and ensure stable training. With only **1.34M parameters**, it achieves a competitive accuracy on the CIFAR-10 test set.
-
-- **Dataset**: [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (32x32 color images, 10 classes)
-- **Framework**: PyTorch
-- **Total Parameters**: 1.34M
-
-## Model Architecture
-
-The network consists of 4 convolutional blocks followed by a classifier head.
-
-| Layer | Input Channels | Output Channels | Kernel Size | Stride | Padding | Activation | Other |
-| :--- | :---: | :---: | :---: | :---: | :---: | :--- | :--- |
-| **Conv Block 1** | 3 | 32 | 5 | 1 | 2 | ReLU | MaxPool(2), BatchNorm |
-| **Conv Block 2** | 32 | 64 | 5 | 1 | 2 | ReLU | MaxPool(2), BatchNorm |
-| **Conv Block 3** | 64 | 128 | 5 | 1 | 2 | ReLU | MaxPool(2), BatchNorm |
-| **Conv Block 4** | 128 | 256 | 5 | 1 | 2 | ReLU | MaxPool(2), BatchNorm |
-| **Flatten** | - | - | - | - | - | - | Output: 1024 |
-| **Linear 1** | 1024 | 256 | - | - | - | ReLU | Dropout(0.5) |
-| **Linear 2** | 256 | 10 | - | - | - | - | - |
-
-## Training Strategy
-
-The model was trained using standard practices for CIFAR-10 to maximize performance on a small footprint.
-
-- **Optimizer**: SGD (Momentum=0.9)
-- **Initial Learning Rate**: 0.007
-- **Scheduler**: StepLR (Step size=5, Gamma=0.5)
-- **Loss Function**: CrossEntropyLoss
-- **Batch Size**: 256
-- **Epochs**: Total 100 epochs, Best Accuracy 31 epoch
-- **Data Augmentation**:
-  - Random Crop (32x32 with padding=4)
-  - Random Horizontal Flip
-
-## Performance
-
-The model achieved the following results on the CIFAR-10 test set:
-
-| Metric | Value |
-| :--- | :---: |
-| **Test Accuracy** | **78%** |
-| Parameters | 1.34M |
-
-### Training Visualization (TensorBoard)
-
-Below are the training and testing curves visualized via TensorBoard.
-
-#### 1. Training Loss
-
-![Training Loss](assets/train_loss.png)
-*(Recorded every step)*
-
-#### 2. Test Loss
-![Test Loss](assets/test_loss.png)
-*(Recorded every epoch)*
-
-## Quick Start
-
-### Dependencies
-- Python 3.x
-- PyTorch
-- Torchvision
-- requirements.txt
-
-### Inference
-
-You can easily load the model and perform inference on a single image using the **test.py** file.
-
-## File Structure
-
-```
-.
-├── model.py               # Model architecture definition
-├── train.py               # Training script
-├── test.py                # Inference script
-├── Mini-Vision-V1.pth     # Trained model weights
-├── README.md
-└── assets
-      ├── train_loss.png   # Visualized train loss graph
-      └── test_loss.png    # Visualized test loss graph
-```
-
-## License
-
-This project is licensed under the MIT License.
+---
+license: mit
+library_name: pytorch
+tags:
+- image-classification
+- cifar10
+- cnn
+- computer-vision
+- pytorch
+- mini-vision
+- mini-vision-series
+metrics:
+- accuracy
+pipeline_tag: image-classification
+datasets:
+- uoft-cs/cifar10
+---
+
+# Mini-Vision-V1: CIFAR-10 CNN Classifier
+
+![Model Size](https://img.shields.io/badge/Params-1.34M-blue) ![Accuracy](https://img.shields.io/badge/Accuracy-78%25-green)
+
+Welcome to **Mini-Vision-V1**, the first model in the Mini-Vision series. This project demonstrates a robust implementation of a Convolutional Neural Network (CNN) for image classification using the CIFAR-10 dataset. It is designed to be lightweight, efficient, and easy to understand, making it perfect for beginners learning PyTorch.
+
+## Model Description
+
+Mini-Vision-V1 is a custom 4-layer CNN architecture. It utilizes Batch Normalization and Dropout to prevent overfitting and ensure stable training. With only **1.34M parameters**, it achieves a competitive accuracy on the CIFAR-10 test set.
+
+- **Dataset**: [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) (32x32 color images, 10 classes)
+- **Framework**: PyTorch
+- **Total Parameters**: 1.34M
+
+## Model Architecture
+
+The network consists of 4 convolutional blocks followed by a classifier head.
+
+| Layer | Input Channels | Output Channels | Kernel Size | Stride | Padding | Activation | Other |
+| :--- | :---: | :---: | :---: | :---: | :---: | :--- | :--- |
+| **Conv Block 1** | 3 | 32 | 5 | 1 | 2 | ReLU | MaxPool(2), BatchNorm |
+| **Conv Block 2** | 32 | 64 | 5 | 1 | 2 | ReLU | MaxPool(2), BatchNorm |
+| **Conv Block 3** | 64 | 128 | 5 | 1 | 2 | ReLU | MaxPool(2), BatchNorm |
+| **Conv Block 4** | 128 | 256 | 5 | 1 | 2 | ReLU | MaxPool(2), BatchNorm |
+| **Flatten** | - | - | - | - | - | - | Output: 1024 |
+| **Linear 1** | 1024 | 256 | - | - | - | ReLU | Dropout(0.5) |
+| **Linear 2** | 256 | 10 | - | - | - | - | - |
+
+## Training Strategy
+
+The model was trained using standard practices for CIFAR-10 to maximize performance on a small footprint.
+
+- **Optimizer**: SGD (Momentum=0.9)
+- **Initial Learning Rate**: 0.007
+- **Scheduler**: StepLR (Step size=5, Gamma=0.5)
+- **Loss Function**: CrossEntropyLoss
+- **Batch Size**: 256
+- **Epochs**: Total 100 epochs, Best Accuracy 31 epoch
+- **Data Augmentation**:
+  - Random Crop (32x32 with padding=4)
+  - Random Horizontal Flip
+
+## Performance
+
+The model achieved the following results on the CIFAR-10 test set:
+
+| Metric | Value |
+| :--- | :---: |
+| **Test Accuracy** | **78%** |
+| Parameters | 1.34M |
+
+### Training Visualization (TensorBoard)
+
+Below are the training and testing curves visualized via TensorBoard.
+
+#### 1. Training Loss
+
+![Training Loss](assets/train_loss.png)
+*(Recorded every step)*
+
+#### 2. Test Loss
+![Test Loss](assets/test_loss.png)
+*(Recorded every epoch)*
+
+## Quick Start
+
+### Dependencies
+- Python 3.x
+- PyTorch
+- Torchvision
+- requirements.txt
+
+### Inference
+
+You can easily load the model and perform inference on a single image using the **test.py** file.
+
+## File Structure
+
+```
+.
+├── model.py               # Model architecture definition
+├── train.py               # Training script
+├── test.py                # Inference script
+├── Mini-Vision-V1.pth     # Trained model weights
+├── Config.json
+├── README.md
+└── assets
+      ├── train_loss.png   # Visualized train loss graph
+      └── test_loss.png    # Visualized test loss graph
+```
+
+## License
+
+This project is licensed under the MIT License.