Dogs and Cats Breed Detector Computer Vision Project

Dataset and Model Visualization: Multi-Label Classification for Domestic Cats and Dogs

This project includes a robust dataset of annotated images and a pre-trained multi-label classification model, all accessible and visualized on the same page. The model complements the dataset by demonstrating real-world application and performance.

Dataset Overview:

The dataset contains high-quality images of domestic cats and dogs, each labeled with multi-label annotations for:

• Species Identification: "Cat" and/or "Dog" labels, allowing single or overlapping classifications.
• Physical Traits: Attributes like coat color (e.g., "black," "white"), patterns (e.g., "spotted," "striped"), and fur type (e.g., "fluffy").
• Contextual Settings: Labels for indoor or outdoor environments.

Model Features:

• Trained on the Dataset: The included model was trained on this dataset, showcasing its capability to identify multiple features in a single image.
• Interactive Visualization: Users can upload images or explore predictions directly on the project page. The model's outputs are displayed alongside corresponding ground truth labels.
• High Accuracy: Demonstrates reliable performance in distinguishing between cats, dogs, and their attributes under diverse scenarios.

Project Highlights:

• Integrated Learning and Visualization: Seamlessly access the dataset and trained model in one place for a comprehensive understanding.
• Interactive Exploration: Real-time prediction and comparison against labeled data for hands-on insights.
• Application Potential: Ideal for research, development, and deployment of multi-label classifiers in pet identification systems and more.