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---
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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