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VamsiKari/
food-vs-notfood-model

Name: food-vs-notfood-model
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🍽 Dish Image Verification Model

This model verifies whether a given image matches a provided dish name.
It has been trained on a curated dataset of food images and labels, optimized for high precision in dish recognition tasks.

📌 Model Details

Architecture: ResNet / CNN-based (PyTorch)
Training Type: Fine-tuning on custom dataset
Framework: PyTorch
Task: Image classification (verification)
Input: An image of a dish
Output: Probability of the image matching the given dish name (True/False)

📊 Performance

Metric	Score
Precision (p)	99.55%
Recall (r)	99.10%
F1-score (f1)	99.33%
Epochs Trained	15
Device	CUDA (GPU)

Example from training:


Epoch 15: p=0.9955 r=0.9910 f1=0.9932 time=65.3s

🗂 Dataset

Type: Custom dataset with dish images and dish names.
Structure:
- train/ → Training images
- val/ → Validation images
Images are labeled according to dish names and mapped to verification labels.

🚀 How to Use

python

from PIL import Image
import torch
from torchvision import transforms
from model import DishVerificationModel  # your model class

# Load model
model = DishVerificationModel()
model.load_state_dict(torch.load("base.pth", map_location="cpu"))
model.eval()

# Preprocess image
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])
img = Image.open("test_dish.jpg")
img_tensor = transform(img).unsqueeze(0)

# Predict
with torch.no_grad():
    output = model(img_tensor)
    prediction = torch.sigmoid(output).item()
    print("Match:", prediction > 0.5)

🧪 Example Use Cases

Restaurant apps verifying menu images
Food delivery services detecting incorrect uploads
Dish tagging automation

⚠️ Limitations

May misclassify visually similar dishes.
Works best with clear, high-quality images.

📄 License

This model is released under the Apache 2.0 License.

✨ Citation

If you use this model, please cite:

@software{dish_verification_2025,
  title        = {Dish Image Verification Model},
  author       = {Vamsee},
  year         = {2025},
  url          = {https://huggingface.co/vamsee99/food-vs-notfood-model}
}

VamsiKari/food-vs-notfood-model