🐕🐈 Oxford-IIIT Pets Classification

Exploratory Data Analysis for Image Classification

Key Statistics Overview

Dataset composition and scale

7,393

Sample Images

37

Breeds

12

Cats

25

Dogs

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About This Project

This is an educational demonstration of exploratory data analysis techniques applied to the Oxford-IIIT Pet Dataset. The reports showcase various visualization and analysis methods for image classification tasks, including feature extraction, dimensionality reduction, and similarity analysis.

Available Reports

Comprehensive analysis across multiple computer vision tasks

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Core EDA Report

✅ Ready

Basic dataset statistics, image properties, dimension distributions, color analysis, and sample visualizations.

Key Features:

  • Image Statistics
  • Dimension Analysis
  • Color Distribution
  • Sample Gallery
View Core Report →
🏷️

Classification EDA Report

✅ Ready

Class distribution, breed analysis, feature extraction (ResNet50), similarity matrix, and clustering insights.

Key Features:

  • 37 Breeds Analysis
  • t-SNE Visualization
  • Similarity Matrix
  • Feature Extraction
View Classification Report →
🎯

Detection EDA Report

✅ Ready

Bounding box analysis, spatial distribution, scale variation, and detection challenges.

Key Features:

  • Bbox Statistics
  • Spatial Analysis
  • Scale Variation
  • Detection Challenges
View Detection Report →
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Segmentation EDA Report

✅ Ready

Tri-map analysis, pixel distribution, boundary quality, and segmentation challenges.

Key Features:

  • Mask Statistics
  • Pixel Analysis
  • Boundary Quality
  • Segmentation Challenges
View Segmentation Report →