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---
license: cc-by-nc-2.0
---
<h1 style="font-size: 60px;">🍝 RistoNet</h1>
**RistoNet** is an **EfficientNet** model trained on the **Gourmet Photography Dataset** for **food image aesthetic assessment**. It’s a tool for **designers, restaurants, and e-commerce** to evaluate and select the **best possible pictures of their dishes**. Perfect for making menus, websites, or social media shine! 😎🍕
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## 🚀 Features
- Assess the aesthetic quality of food images
- Helps pick the most appealing photos for menus, websites, or e-commerce
- Lightweight and fast thanks to EfficientNet
- Ideal for designers, chefs, and food entrepreneurs
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## 📊 Model Performance
| Dataset | Accuracy |
|---------------|----------|
| GFD (Test split) | ✅ 91,17% |
---
## 🖼️ Quick Usage
```python
## 🔎 Inference Example
import torch
from PIL import Image
from torchvision import models, transforms
from huggingface_hub import hf_hub_download
# ------------------------------
# 1. Load model
# ------------------------------
MODEL_REPO = "Orkidee/RistoNet"
MODEL_FILE = "ristonet.pth"
model = models.efficientnet_b0(weights=None) # no pretrained weights
num_features = model.classifier[1].in_features
model.classifier[1] = torch.nn.Linear(num_features, 2) # 2 classes
# Download weights from Hub and load
model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE)
model.load_state_dict(torch.load(model_path, map_location="cpu"))
model.eval()
# ------------------------------
# 2. Define preprocessing
# ------------------------------
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
# ------------------------------
# 3. Run inference on an image
# ------------------------------
image = Image.open("my_food.jpg").convert("RGB")
input_tensor = transform(image).unsqueeze(0) # add batch dim
with torch.no_grad():
outputs = model(input_tensor)
probs = torch.nn.functional.softmax(outputs, dim=1)
predicted_class = probs.argmax().item()
# 0 = Negative, 1 = Positive
print("Predicted class:", predicted_class)
print("Probabilities:", probs.numpy())
|