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---
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
```json
{
"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
```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
```bash
curl -X POST https://YOUR-SPACE-URL.hf.space/predict_pet \
-F "image=@dog.jpg"
```
### JavaScript (Fetch)
```javascript
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](https://huggingface.co/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
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