soufyane/dogs-vs-cats

language: en tags:

• —pytorch
• —image-classification
• —cats-vs-dogs
• —computer-vision datasets:
• —microsoft/catsvsdogs 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/catsvsdogs
• —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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