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README.md

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+---
+license: mit
+tags:
+- image-classification
+- pytorch
+- cats
+- efficientnet
+library_name: pytorch
+pipeline_tag: image-classification
+---
+
+# Which Cat? - Lucy vs Madelaine Classifier
+
+A fine-tuned EfficientNet-B0 model that distinguishes between two cats: Lucy and Madelaine.
+
+## Model Details
+
+- **Base Model**: EfficientNet-B0 (pretrained on ImageNet)
+- **Task**: Binary image classification
+- **Classes**: `lucy`, `madelaine`
+- **Training Data**: ~190 personal photos
+- **Validation Accuracy**: 90%
+
+## Usage
+
+```python
+import torch
+import torch.nn as nn
+from torchvision import transforms, models
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(repo_id="khasinski/which-cat", filename="cat_classifier.pth")
+
+# Load model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+checkpoint = torch.load(model_path, map_location=device, weights_only=False)
+
+model = models.efficientnet_b0(weights=None)
+model.classifier = nn.Sequential(
+    nn.Dropout(p=0.3),
+    nn.Linear(1280, 2)
+)
+model.load_state_dict(checkpoint['model_state_dict'])
+model.to(device)
+model.eval()
+
+# Class mapping
+idx_to_class = {v: k for k, v in checkpoint['class_to_idx'].items()}
+
+# Predict
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+image = Image.open("your_cat.jpg").convert('RGB')
+tensor = transform(image).unsqueeze(0).to(device)
+
+with torch.no_grad():
+    probs = torch.softmax(model(tensor), dim=1)[0]
+    pred_idx = probs.argmax().item()
+
+print(f"Prediction: {idx_to_class[pred_idx]} ({probs[pred_idx]:.1%})")
+```
+
+## Training
+
+Trained using transfer learning with:
+- Data augmentation (flips, rotations, color jitter)
+- Weighted random sampling for class balance
+- AdamW optimizer with learning rate scheduling
+- 20 epochs on Apple MPS
+
+## Limitations
+
+This model is trained specifically on Lucy and Madelaine. It will not generalize to other cats - it will simply classify any cat image as one of the two.