README.md

metadata

title: Dog Breed Classification API
emoji: 🐕
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
pinned: false

🐕 Dog Breed Classification API

ConvNextV2-large-DogBreed model ile köpek ırkı tahmini yapan API.

🚀 Kullanım

Endpoint

POST /predict_pet

Request

Content-Type: multipart/form-data

Body:

• image (file): Köpek fotoğrafı (JPEG, PNG, WebP)

Response

{
  "breed": "Doberman_pinscher",
  "confidence": 0.533,
  "top_5": [
    {"breed": "Doberman_pinscher", "confidence": 0.533},
    {"breed": "Beauceron", "confidence": 0.065},
    {"breed": "German_pinscher", "confidence": 0.041},
    {"breed": "Black_and_tan_coonhound", "confidence": 0.023},
    {"breed": "Greater_swiss_mountain_dog", "confidence": 0.011}
  ],
  "model": "ConvNextV2-large-DogBreed",
  "accuracy": "91.39%"
}

📝 Örnekler

Python

import requests

url = "https://YOUR-SPACE-URL.hf.space/predict_pet"

with open("dog.jpg", "rb") as f:
    files = {"image": f}
    response = requests.post(url, files=files)
    
result = response.json()
print(f"Breed: {result['breed']}")
print(f"Confidence: {result['confidence']:.2%}")

cURL

curl -X POST https://YOUR-SPACE-URL.hf.space/predict_pet \
  -F "image=@dog.jpg"

JavaScript (Fetch)

const formData = new FormData();
formData.append('image', fileInput.files[0]);

const response = await fetch('https://YOUR-SPACE-URL.hf.space/predict_pet', {
  method: 'POST',
  body: formData
});

const result = await response.json();
console.log(result.breed, result.confidence);

ℹ️ Model Bilgisi

• Model: Pavarissy/ConvNextV2-large-DogBreed
• Accuracy: 91.39% (validation set)
• Architecture: ConvNextV2-large-22k-224
• Training: 50 epochs, Stanford Dogs Dataset
• Classes: 120 dog breeds

🔧 Performans

• İlk istek: 10-15 saniye (model yükleme)
• Sonraki istekler: 2-4 saniye
• Hardware: CPU basic (HF Spaces free tier)

📄 License

MIT