---
language: en
tags:
  - image-classification
  - computer-vision
  - pytorch
  - intel-image-classification
  - resnet18
license: mit
datasets:
  - puneet6060/intel-image-classification
model-index:
  - name: ResNet18 Intel Image Classifier
    results: []
---

# 🏞️ ResNet18 Intel Image Classifier

📌 A ResNet18-based image classification model trained on the [Intel Image Classification dataset](https://www.kaggle.com/datasets/puneet6060/intel-image-classification), capable of recognizing six types of natural scenes. The model was fine-tuned using PyTorch, optimized for reproducibility and deployment in educational and practical scenarios.

## 🏷️ Classes

- Buildings  
- Forest  
- Glacier  
- Mountain  
- Sea  
- Street

## 🧰 Training Procedure

1. Loaded a pretrained ResNet18 model from `torchvision.models`.
2. Replaced the final classification layer with a 6-unit fully connected layer.
3. Resized all input images to 224x224 and applied ImageNet normalization.
4. Used `ImageFolder` and `random_split()` to divide the dataset:
   - 70% Training
   - 15% Validation
   - 15% Testing
5. Training setup:
   - Optimizer: Adam
   - Loss Function: CrossEntropyLoss
   - Batch size: 32
   - Learning rate: 0.001
   - Epochs: 5
6. Saved the final model as `pytorch_model.bin`.

## 📊 Performance

| Metric               | Value     |
|----------------------|-----------|
| Final Train Accuracy | 90.08%    |
| Final Val Accuracy   | 88.74%    |

## ⚙️ Framework & Environment

- Python: 3.10.12  
- PyTorch: 2.0.1+cu118  
- Torchvision: 0.15.2+cu118  
- Platform: Google Colab (GPU enabled, CUDA support)

## 🧪 Hyperparameters

| Parameter       | Value        |
|-----------------|--------------|
| Epochs          | 5            |
| Batch Size      | 32           |
| Optimizer       | Adam         |
| Learning Rate   | 0.001        |
| Loss Function   | CrossEntropy |
| Image Size      | 224x224      |
| Data Split      | 70% Train / 15% Val / 15% Test |

---