Dog Breed Classifier

This is a MobileNetV2 model fine-tuned for dog breed classification with 120 breed classes.

Supported Breeds

Chihuahua
Japanese Spaniel
Maltese Dog
Pekinese
Shih-Tzu
Blenheim Spaniel
Papillon
Toy Terrier
Rhodesian Ridgeback
Afghan Hound
... and 110 more breeds

Model Details

Architecture: MobileNetV2 with custom classifier
Input Size: 224x224 pixels
Number of Classes: 120
Framework: PyTorch

Usage

Via Hugging Face Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/valentinocc/dog-breed-classifier"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

# Predict dog breed
output = query("dog_image.jpg")
print(f"Predicted breed: {output[0]['label']} ({output[0]['score']:.2%} confidence)")

Django Integration

from utils.huggingface_client import HuggingFaceClient

hf_client = HuggingFaceClient()
result = hf_client.classify_image(image_file, "valentinocc/dog-breed-classifier")

Training Details

This model was trained on a custom dataset of dog breed images using transfer learning on MobileNetV2. The model achieves good performance across 120 different dog breeds.

Model Performance

Input: RGB images, 224x224 pixels
Output: Probability distribution over 120 dog breeds
Architecture: MobileNetV2 backbone + custom classifier