youth-ai-initiative/Animals10_Classifier_Group_4

🦁 ResNet50 Fine-Tuned on Animals-10 Dataset

Note: This project was developed by Group 4 as part of the Youth AI Initiative. It demonstrates how to achieve state-of-the-art performance (>98% accuracy) using modern Fine-Tuning techniques on standard architectures.

📝 Overview

This project implements a high-performance Image Classification model capable of identifying 10 different animal species with near-perfect accuracy.

Unlike complex ensemble approaches that consume vast resources, we focused on optimizing a single robust backbone (ResNet50) using advanced training strategies like OneCycleLR, Label Smoothing, and Mixed Precision Training. This resulted in a lightweight yet extremely powerful model that outperforms standard baselines.

🎯 Objectives

• —High Accuracy: Achieve >95% accuracy on the test set (Achieved: 98.32%).
• —Robustness: Prevent overfitting using regularization techniques (Label Smoothing, Weight Decay).
• —Efficiency: Utilize GPU acceleration (AMP) for faster training.
• —Explainability: Analyze errors using Confusion Matrices and Per-Class metrics.

🏆 Performance Metrics

The model was evaluated on an independent test set (10% split) and achieved exceptional results across all metrics.

📊 Confusion Matrix & Error Analysis

The confusion matrix below demonstrates the model's robustness. The dark diagonal line indicates near-perfect classification.

📈 Per-Class Performance

The model maintains high performance (>95%) even on difficult classes.

⚙️ Methodology & Training Techniques

To achieve 98.32% accuracy while maintaining a healthy Bias-Variance Tradeoff, we employed the following advanced techniques:

🛠️ Installation & Requirements

To run this model, you need to install the following dependencies. We recommend using a GPU for faster inference.

pip install torch torchvision torchaudio pillow

💻 Usage Code (GPU Supported)

You can use this model directly with PyTorch. The code below automatically detects if you have a GPU (CUDA).

import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image

# 1. Device Configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 2. Define Architecture
model = models.resnet50(weights=None)
model.fc = nn.Linear(model.fc.in_features, 10)

# 3. Load Weights
# Ensure 'best_resnet50_animals.pt' is in your directory
model.load_state_dict(torch.load("best_resnet50_animals.pt", map_location=device))
model = model.to(device)
model.eval()

# 4. Preprocess Image
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])

# 5. Predict
img_path = "test_image.jpg" 
try:
    img = Image.open(img_path).convert("RGB")
    input_tensor = transform(img).unsqueeze(0).to(device)

    with torch.no_grad():
        output = model(input_tensor)
        probabilities = torch.nn.functional.softmax(output[0], dim=0)
        confidence, pred = torch.max(probabilities, 0)

    classes = ['cane', 'cavallo', 'elefante', 'farfalla', 'gallina', 
               'gatto', 'mucca', 'pecora', 'ragno', 'scoiattolo']
               
    print(f"Prediction: {classes[pred.item()].upper()} ({confidence.item():.2%})")

except FileNotFoundError:
    print("Image not found.")

👥 Team Members (Group 4)

• —[Kuzey KAYA, Maruf Salih ATALA, Umut ÇATAK, Kuzey ÇALIŞKAN, Mücahit YETER, Yusuf BATMACA, Göktüğ...] <!-- end list -->