README.md

---
license: mit
tags:
  - image-classification
  - cs5242
  - intel-image-classification
---

# CS5242 Intel Image Classification — Model Checkpoints

Trained checkpoints for the NUS CS5242 group project (Group 13). Models classify
6-class natural scene images (buildings, forest, glacier, mountain, sea, street)
from the Intel Image Classification dataset.

## Files

| File | Architecture | Notes |
|------|--------------|-------|
| `mlp_best.pt` | MLP baseline | Flattened-pixel input |
| `cnn_best.pt` | CNN baseline | Reference CNN |
| `cnn_2block_best.pt` | CNN, 2 conv blocks | Capacity ablation |
| `cnn_3block_best.pt` | CNN, 3 conv blocks | Capacity ablation |
| `cnn_dropout03_best.pt` | CNN + dropout 0.3 | Regularization ablation |
| `cnn_dropout07_best.pt` | CNN + dropout 0.7 | Regularization ablation |
| `cnn_strong_aug_best.pt` | CNN + strong augmentation | Augmentation ablation |
| `vit_best.pt` | Vision Transformer | Best-performing model |
| `*_history.json` | — | Per-epoch loss/accuracy curves |

## Loading

```python
import torch
from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(repo_id="daryl336/cs5242-intel-classification",
                            filename="vit_best.pt")
state = torch.load(ckpt_path, map_location="cpu")
```