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README.md
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short_description: Code is designed to identify dog breeds from uploaded image
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---
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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.
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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.
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short_description: Code is designed to identify dog breeds from uploaded image
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---
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Dog breed detectors serve a variety of practical and interesting purposes across different domains. Below are some reasons why they are useful:
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Animal Shelters and Rescues
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Identification: Many dogs arrive at shelters without clear breed information. A breed detector can help staff identify them quickly.
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Adoption: Potential adopters often look for specific breeds, or have preferences related to size, temperament, or exercise needs.
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Veterinary and Health Insights
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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.
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Preventive Measures: Knowing breed predispositions can help plan preventive tests, recommended diets, and exercise routines.
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Training and Behavior
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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.
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Exercise and Enrichment Needs: High-energy breeds often need more physical and mental stimulation than low-energy breeds.
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Research and Data
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Population Studies: Researchers studying canine genetics or behavior benefit from large-scale, accurate breed detection.
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Genetic Diversity: Breeding programs aiming to maintain genetic diversity rely on breed identification tools to avoid inbreeding.
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Personal Curiosity
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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.
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Community Engagement: Apps or services that identify dog breeds can be fun and interactive, increasing user engagement on social media.
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Security and Surveillance
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Service Dogs: Some applications may ensure that official service dogs meet specific breed or training requirements.
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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.
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Educational Tools
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Learning Resources: Dog breed detectors can be used in apps or educational tools that teach users about different breeds, their histories, and care requirements.
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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.
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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.
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