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---
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license: mit
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tags:
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- image-classification
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- pytorch
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- dog-breed
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- mobilenet
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- computer-vision
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datasets:
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- custom
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---
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# Dog Breed Classifier
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This is a MobileNetV2 model fine-tuned for dog breed classification with 120 breed classes.
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## Supported Breeds
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- Chihuahua
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- Japanese Spaniel
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- Maltese Dog
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- Pekinese
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- Shih-Tzu
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- Blenheim Spaniel
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- Papillon
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- Toy Terrier
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- Rhodesian Ridgeback
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- Afghan Hound
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- ... and 110 more breeds
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## Model Details
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- **Architecture**: MobileNetV2 with custom classifier
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- **Input Size**: 224x224 pixels
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- **Number of Classes**: 120
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- **Framework**: PyTorch
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## Usage
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### Via Hugging Face Inference API
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```python
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import requests
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API_URL = "https://api-inference.huggingface.co/models/valentinocc/dog-breed-classifier"
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headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
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def query(filename):
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with open(filename, "rb") as f:
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data = f.read()
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response = requests.post(API_URL, headers=headers, data=data)
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return response.json()
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# Predict dog breed
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output = query("dog_image.jpg")
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print(f"Predicted breed: {output[0]['label']} ({output[0]['score']:.2%} confidence)")
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```
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### Django Integration
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```python
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from utils.huggingface_client import HuggingFaceClient
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hf_client = HuggingFaceClient()
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result = hf_client.classify_image(image_file, "valentinocc/dog-breed-classifier")
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```
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## Training Details
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This model was trained on a custom dataset of dog breed images using transfer learning on MobileNetV2.
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The model achieves good performance across 120 different dog breeds.
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## Model Performance
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- Input: RGB images, 224x224 pixels
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- Output: Probability distribution over 120 dog breeds
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- Architecture: MobileNetV2 backbone + custom classifier
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