---
license: apache-2.0
datasets:
- microsoft/cats_vs_dogs
language:
- en
metrics:
- accuracy
- f1
- recall
- precision
pipeline_tag: image-classification
---

---
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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