README.md

metadata

title: DogBreadDetector
emoji: 🐢
colorFrom: yellow
colorTo: red
sdk: gradio
sdk_version: 5.13.1
app_file: app.py
pinned: false
short_description: Code is designed to identify dog breeds from uploaded image

Dog breed detectors serve a variety of practical and interesting purposes across different domains. Below are some reasons why they are useful:

Animal Shelters and Rescues

Identification: Many dogs arrive at shelters without clear breed information. A breed detector can help staff identify them quickly. Adoption: Potential adopters often look for specific breeds, or have preferences related to size, temperament, or exercise needs. Veterinary and Health Insights

Breed-Specific Conditions: Certain breeds are prone to specific health issues (e.g., hip dysplasia in large breeds, breathing difficulties in brachycephalic breeds). Identifying a dog’s breed can guide veterinarians and owners toward more proactive care. Preventive Measures: Knowing breed predispositions can help plan preventive tests, recommended diets, and exercise routines. Training and Behavior

Temperament: Different breeds tend to have unique behavioral traits or energy levels. A breed detector helps trainers and owners anticipate and manage breed-specific behaviors. Exercise and Enrichment Needs: High-energy breeds often need more physical and mental stimulation than low-energy breeds. Research and Data

Population Studies: Researchers studying canine genetics or behavior benefit from large-scale, accurate breed detection. Genetic Diversity: Breeding programs aiming to maintain genetic diversity rely on breed identification tools to avoid inbreeding. Personal Curiosity

Mixed-Breed Dogs: People who adopt mixed-breed dogs (often called “mutts”) can use breed detectors to learn about their dogs’ heritage. This knowledge often satisfies curiosity and can help inform better care. Community Engagement: Apps or services that identify dog breeds can be fun and interactive, increasing user engagement on social media. Security and Surveillance

Service Dogs: Some applications may ensure that official service dogs meet specific breed or training requirements. Restricted Breeds: Certain regions have laws about specific “restricted” or “banned” breeds (though these laws are controversial). Breed detection can help clarify breed status in ambiguous cases. Educational Tools

Learning Resources: Dog breed detectors can be used in apps or educational tools that teach users about different breeds, their histories, and care requirements.

This repository contains a Gradio application that uses a vanilla (ImageNet-pretrained) VGG16 model to classify images. The application: (1). Allows users to upload or drag-and-drop an image. (2). Displays the top 3 ImageNet classes predicted by VGG16. (3). Lets users adjust a confidence threshold slider to filter out low-confidence predictions.

Features: (1). Image Upload: Users can drag & drop or click to upload an image. (2). Confidence Threshold: A slider that filters predictions below a chosen probability. (3). Custom UI: (Optional) Custom background or gradient for a more website-like appearance. (4). Fast Inference: Powered by PyTorch and TorchVision’s pretrained VGG16 model.

Contributing: (1). Fork the repo. (2). Create your feature branch: git checkout -b feature/awesome-feature. (3). Commit your changes: git commit -m 'Add awesome feature'. (4). Push to the branch: git push origin feature/awesome-feature. (5). Create a new Pull Request on GitHub or share your branch to merge changes.

License: This project is open-sourced under the MIT License. See the LICENSE file for details (include a license file if you wish).

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference