| --- |
| 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 🐱🐶 |
|
|
| [](https://opensource.org/licenses/MIT) |
| [](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. |
|
|
