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README.md

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+---
+license: mit
+tags:
+  - image-classification
+  - pytorch
+  - resnet18
+  - transfer-learning
+  - computer-vision
+pipeline_tag: image-classification
+---
+
+# Cat vs Dog Classifier (ResNet18 Transfer Learning)
+
+This model is a fine-tuned **ResNet18** for binary image classification: **cat** vs **dog**.
+
+## Model Details
+
+- **Base model:** ResNet18 (pretrained on ImageNet)
+- **Framework:** PyTorch
+- **Task:** Binary image classification (cat, dog)
+- **Input size:** 128x128 RGB images
+- **Training method:**
+  1. Feature extraction — froze all layers except the final fully connected layer, trained for 5 epochs.
+  2. Fine-tuning — unfroze `layer4` of ResNet18 and trained further with a lower learning rate for 5 epochs.
+
+## Dataset
+
+Trained on a small subset of **CIFAR-10** (cat and dog classes only), with 100 images per class for training and 50 images per class for testing.
+
+## How to Use
+
+```python
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+class_names = {0: "cat", 1: "dog"}
+
+transform = transforms.Compose([
+    transforms.Resize((128, 128)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                          std=[0.229, 0.224, 0.225]),
+])
+
+weights_path = hf_hub_download(repo_id="billahaiml/cat-dog-resnet18", filename="cat_dog_resnet18.pth")
+
+model = models.resnet18(weights=None)
+model.fc = nn.Linear(model.fc.in_features, 2)
+model.load_state_dict(torch.load(weights_path, map_location=device))
+model = model.to(device)
+model.eval()
+
+img = Image.open("your_image.jpg").convert("RGB")
+img_tensor = transform(img).unsqueeze(0).to(device)
+
+with torch.no_grad():
+    output = model(img_tensor)
+    probs = torch.softmax(output, dim=1)[0]
+    pred = class_names[torch.argmax(probs).item()]
+
+print(f"Prediction: {pred}")
+```
+
+## Limitations
+
+This model was trained on a very small dataset (100 images per class), so it is intended for educational/demo purposes rather than production use.