README.md

---
license: mit
pipeline_tag: image-classification
library_name: pytorch
inference: false
tags:
- pytorch
- resnet
- transfer-learning
- image-classification
- grad-cam
- computer-vision
---

# Cat vs Dog Classifier 🐱🐶

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Code on GitHub](https://img.shields.io/badge/Code-GitHub-181717.svg?logo=github)](https://github.com/mtkl6/cat-dog-classifier)

A **ResNet50 transfer-learning** classifier that distinguishes cats from dogs at
**~94% validation accuracy (AUC 0.98)**, trained in two stages on the
Oxford-IIIT Pet dataset.

Full training code, Grad-CAM inference, and a complete beginner's guide:
👉 **https://github.com/mtkl6/cat-dog-classifier**

> ⚠️ The inference widget is disabled because this is a custom head on a
> torchvision backbone (not a `transformers` model) — load it with the snippet below.

## Files

| File | What |
|---|---|
| `cat_dog_classifier.pt` | trained weights (raw `state_dict`, ~90 MB) |
| `config.json` | architecture & preprocessing metadata |

## Usage

```python
import torch, torch.nn as nn
from torchvision import models, transforms
from huggingface_hub import hf_hub_download
from PIL import Image

model = models.resnet50()
model.fc = nn.Sequential(nn.Dropout(0.4), nn.Linear(2048, 1))
weights = hf_hub_download("mtkl6/cat-dog-classifier", "cat_dog_classifier.pt")
model.load_state_dict(torch.load(weights, weights_only=True))
model.eval()

tf = transforms.Compose([
    transforms.Resize((224, 224)), transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
x = tf(Image.open("pet.jpg").convert("RGB")).unsqueeze(0)
p_dog = torch.sigmoid(model(x)).item()
print("dog" if p_dog > 0.5 else "cat", f"({max(p_dog, 1 - p_dog):.1%})")
```

Labels: **0 = cat, 1 = dog**. The model outputs a single logit; apply `sigmoid`
and threshold at 0.5.

## Training

| | |
|---|---|
| Backbone | ResNet50 (`IMAGENET1K_V1`), head `Dropout(0.4) → Linear(2048, 1)` |
| Stage 1 | frozen backbone, head only — `lr 1e-3`, 10 epochs → 86.3% val |
| Stage 2 | fine-tune `layer4` — `lr 1e-5`, 10 epochs → **94.2% val, AUC 0.98** |
| Loss / optim | `BCEWithLogitsLoss`, Adam, `ReduceLROnPlateau` |
| Input | 224×224 RGB, ImageNet normalization |
| Dataset | [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/) (37 breeds → binary) |

## Citation

```bibtex
@software{cat_dog_classifier_2026,
  author = {Moritz (mtkl6)},
  title  = {Cat vs Dog Classifier: a ResNet50 transfer-learning tutorial},
  year   = {2026},
  url    = {https://github.com/mtkl6/cat-dog-classifier}
}
```

## License

Code & weights: **MIT**. Dataset: [Oxford-IIIT Pet](https://www.robots.ox.ac.uk/~vgg/data/pets/)
(Parkhi et al., 2012), used under its own research/educational terms.