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https://github.com/rishit7/bird-species-classification

COL333 Assignment 3 part a, A CNN Based Bird Species Classifier
https://github.com/rishit7/bird-species-classification

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COL333 Assignment 3 part a, A CNN Based Bird Species Classifier

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README 

        
# **Bird Species Classification Using CNNs**



### **Overview**  

This project explores a convolutional neural network (CNN)-based approach for **Bird Species Classification**. The model's performance is analyzed across various optimization techniques, providing insights into its learning behavior and interpretability through **Class Activation Maps (CAM)**.  



---



### **Features**  



1. **Model Architecture**:  

   - A robust CNN architecture featuring residual connections, Batch Normalization, and Adaptive Average Pooling for efficient representation learning.  



2. **Training Insights**:  

   - Performance metrics, including **training and validation loss/accuracy trends across epochs**, are visualized for clear understanding.  



3. **Optimization Techniques**:  

   - Impact of methods such as **data augmentation**, **dropout**, **Compound Scaling Laws**, and **StepLR scheduling** on model accuracy is explored and tabulated.  



4. **Class Activation Maps (CAM)**:  

   - Visualization of regions focused on by the model for classification decisions, providing interpretability and identifying areas of misclassification.  



---



### **Model Architecture**  



| Layer                    | Configuration                        | Output Shape         |

|--------------------------|---------------------------------------|----------------------|

| Input                    | Image (300x300x3)                    | (300, 300, 3)        |

| Conv2D + BatchNorm + ReLU| 7x7, stride 2, padding 3             | (64, 150, 150)       |

| MaxPool2D               | 3x3, stride 2                        | (64, 75, 75)         |

| Residual Blocks          | Several Conv2D layers with skip connections and strides| Intermediate (128 to 512 channels) |

| AdaptiveAvgPool2D        | Adaptive Average Pooling             | (512, 1, 1)          |

| Fully Connected Layer    | Linear (512 → 10)                    | (10)                 |



The table captures the architecture of the **birdClassifier** CNN, optimized for scalability and feature extraction.  



---



### **Performance Metrics**  



#### **Training and Validation Trends**  

Plots for **Loss** and **Accuracy** across epochs highlight model convergence and generalization:  

- Training-to-validation split: **80% train**, **20% validation**.  



#### **Optimization Techniques**  

| Technique               | Validation Accuracy | Notes                                          |

|--------------------------|---------------------|------------------------------------------------|

| No Optimization          | 60.13%             | Baseline model                                |

| Compound Scaling Laws    | 81.78%             | Improved scaling                              |

| Data Augmentation        | 89.98%             | Enhanced generalization with augmented data   |

| Dropout                  | 86.50%             | Slower learning but robust performance        |

| StepLR (all combined)    | **90.72%**         | Best accuracy achieved                        |



---



### **Class Activation Maps (CAM)**  



**CAM visualizations** provide insights into the model's decision-making:  

- Heatmaps reveal the image regions most influential in classification.  

- Misclassified examples show scattered attention, indicating potential areas for further optimization.  



#### **Examples of CAM**:  

- Correct classifications emphasize relevant textures and objects in the images.



![Class 1: GRAD-CAM](./A3/images/cam_class10.png)

![Class 1: GRAD-CAM](./A3/images/cam_class1.png)



- Misclassified examples display poor focus, leading to incorrect predictions.  



![Class 5 misclassified as 1: GRAD-CAM](./A3/images/miss_cam_class_5_1.png)

![Class 9 misclassified as 2: GRAD-CAM](./A3/images/miss_cam_class_9_2.png)



---



### **Usage**  



1. Clone the repository:  

   ```bash

   git clone 

   cd 

   ```



2. Running the Code

   ```bash

   # Running code for training. save the model in the same directory with name "bird.pth"

   python bird.py path_to_dataset train bird.pth 

  

   # Running code for inference

   python bird.py path_to_dataset test bird.pth

   ```



---



### **Authors**  

- **Rishit Jakharia**  

- **Aditya Jha** 



---



### **Acknowledgments**  

This work was completed as part of **COL333 Assignment 3.1** at **IIT Delhi**, exploring advanced deep learning techniques under academic supervision.