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