Add ResNet50 cat/dog classifier (~94% acc) + model card

README.md

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+---
+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) |
+
+## 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.

cat_dog_classifier.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b38e598c03bd94d546a394905907691675fc2c18794f6bdcbbb8a09069105b34
+size 94357505

config.json

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+{
+  "model_type": "resnet50",
+  "task": "image-classification",
+  "num_labels": 1,
+  "id2label": {"0": "cat", "1": "dog"},
+  "image_size": 224,
+  "normalization": {
+    "mean": [0.485, 0.456, 0.406],
+    "std": [0.229, 0.224, 0.225]
+  },
+  "backbone": "torchvision resnet50 (IMAGENET1K_V1)",
+  "head": "Dropout(0.4) -> Linear(2048, 1)",
+  "output": "single logit; sigmoid(logit) > 0.5 => dog",
+  "weights_file": "cat_dog_classifier.pt",
+  "library_name": "pytorch",
+  "license": "mit"
+}