---
license: apache-2.0
language:
- en
pipeline_tag: image-classification
tags:
- pytorch
- resnet50
- image-classification
- animals10
- fine-tuning
- vision
- YouthAI
dataset:
- alessiocorrado99/animals10
library_name: torchvision
metrics:
- accuracy
- f1
- precision
- recall
base_model:
- microsoft/resnet-50
model-index:
- name: ResNet50 Animals10 Classifier (Group 4)
  results:
  - task:
      type: image-classification
    dataset:
      type: animals10
      name: Animals-10
    metrics:
    - type: accuracy
      value: 98.32
    - type: f1
      value: 0.98
---

# 🦁 ResNet50 Fine-Tuned on Animals-10 Dataset

![Project Banner](https://img.shields.io/badge/Project-Youth_AI_Initiative-blue?style=for-the-badge&logo=python)

> **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.

| Metric | Score | Description |
| :--- | :--- | :--- |
| **Test Accuracy** | **98.32%** | Overall correct predictions. |
| **F1-Score (Weighted)** | **0.98** | Harmonic mean of precision and recall. |
| **Precision** | **0.98** | Accuracy of positive predictions. |
| **Recall** | **0.98** | Ability to find all positive instances. |

### 📊 Confusion Matrix & Error Analysis
The confusion matrix below demonstrates the model's robustness. The dark diagonal line indicates near-perfect classification.

![Confusion Matrix](https://huggingface.co/youth-ai-initiative/Animals10_Classifier_Group_4/resolve/main/model_basarisi.png)

### 📈 Per-Class Performance
The model maintains high performance (>95%) even on difficult classes.

| Class (IT/EN) | Precision | Recall | F1-Score |
| :--- | :--- | :--- | :--- |
| **Cane (Dog)** | 0.99 | 0.98 | **0.99** |
| **Cavallo (Horse)** | 0.99 | 0.99 | **0.99** |
| **Elefante (Elephant)** | 0.98 | 0.99 | **0.99** |
| **Farfalla (Butterfly)** | 0.99 | 0.98 | **0.99** |
| **Gallina (Chicken)** | 0.97 | 0.98 | **0.98** |
| **Gatto (Cat)** | 0.96 | 0.97 | **0.97** |
| **Mucca (Cow)** | 0.97 | 0.96 | **0.97** |
| **Pecora (Sheep)** | 0.98 | 0.98 | **0.98** |
| **Ragno (Spider)** | 0.99 | 0.99 | **0.99** |
| **Scoiattolo (Squirrel)** | 0.98 | 0.97 | **0.98** |

---

## ⚙️ Methodology & Training Techniques

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

| Technique | Purpose in this Project |
| :--- | :--- |
| **Model Architecture** | **ResNet50** backbone for powerful feature extraction. |
| **Optimization** | **AdamW** optimizer for better weight decay and regularization. |
| **Learning Rate Schedule** | **OneCycleLR** policy for faster and more stable convergence. |
| **Regularization** | **Label Smoothing (0.1)** to prevent overfitting and overconfidence. |
| **Data Augmentation** | `RandomErasing`, `ColorJitter`, and `Rotation` to force feature learning. |
| **Mixed Precision** | **Native AMP (fp16)** for efficient VRAM usage and speed. |
| **Training Strategy** | **Fine-Tuning** (Frozen early layers, trainable Layer 4 + FC). |

---

## 🛠️ Installation & Requirements

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

```bash
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).

```python
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...]
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```