dogs-vs-cats

---

language: en tags:

• pytorch
• image-classification
• cats-vs-dogs
• computer-vision datasets:
• microsoft/cats_vs_dogs model-index:
• name: Dogs vs Cats Classifier results:
• task: type: image-classification name: Image Classification metrics:
  • type: accuracy value: 93.25 name: Validation Accuracy
  • type: roc_auc value: 0.9942 name: ROC AUC
  • type: precision value: 0.9769 name: Precision
  • type: recall value: 0.9615 name: Recall
  • type: f1 value: 0.9691 name: F1-Score

license: mit

Dogs vs Cats Classifier

This model classifies images as either cats or dogs using a Convolutional Neural Network (CNN) architecture.

Model description

Architecture:

• 4 convolutional blocks (Conv2D → ReLU → BatchNorm → MaxPool)
• Feature channels: 3→64→128→256→512
• Global average pooling
• Fully connected layers: 512→256→1
• Binary classification output

Training

• Dataset: microsoft/cats_vs_dogs
• Training/Validation split: 80/20
• Input size: 224x224 RGB images
• Trained for 10 epochs
• Best validation accuracy: 93.25%

Intended uses

• Image classification between cats and dogs
• Transfer learning base for similar pet/animal classification tasks

Limitations

• Only trained on cats and dogs
• May not perform well on:
• Low quality/blurry images
• Unusual angles/poses
• Multiple animals in one image

Input

RGB images resized to 224x224 pixels, normalized using ImageNet statistics:

• mean=[0.485, 0.456, 0.406]
• std=[0.229, 0.224, 0.225]

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