---
license: mit
tags:
- image-classification
- food-recognition
- raw-food
- pytorch
- resnet
- se-resnet
- computer-vision
- model-comparison
datasets:
- ibrahimdaud/raw-food-recognition
metrics:
- accuracy
---

# Raw Food Recognition Models: ResNet-50 vs SE-ResNet-50

This repository contains both ResNet-50 and SE-ResNet-50 models trained for raw food ingredient recognition using the merged raw food recognition dataset.

## Model Comparison

| Model | Parameters | Validation Accuracy | Architecture |
|-------|-----------|-------------------|--------------|
| ResNet-50 | ~25.6M | 97.84% | Standard residual network |
| SE-ResNet-50 | ~26.0M | 95.72% | ResNet-50 with SE attention |

## Dataset

Both models were trained on the [ibrahimdaud/raw-food-recognition](https://huggingface.co/datasets/ibrahimdaud/raw-food-recognition) dataset, which contains 90+ raw food categories.

## Usage

### Download Both Models

```python
from huggingface_hub import hf_hub_download
import torch
from models.resnet50 import create_resnet50
from models.se_resnet50 import create_se_resnet50

# Download ResNet-50 checkpoint
resnet_path = hf_hub_download(
    repo_id="ibrahimdaud/raw-food-recognition-models",
    filename="resnet50_pytorch_model.bin"
)
resnet_checkpoint = torch.load(resnet_path, map_location='cpu')

# Download SE-ResNet-50 checkpoint
se_resnet_path = hf_hub_download(
    repo_id="ibrahimdaud/raw-food-recognition-models",
    filename="se_resnet50_pytorch_model.bin"
)
se_resnet_checkpoint = torch.load(se_resnet_path, map_location='cpu')
```

### Load ResNet-50

```python
# Create ResNet-50 model
resnet_model = create_resnet50(
    num_classes=90,
    pretrained=False
)
resnet_model.load_state_dict(resnet_checkpoint['model_state_dict'])
resnet_model.eval()
```

### Load SE-ResNet-50

```python
# Create SE-ResNet-50 model
se_resnet_model = create_se_resnet50(
    num_classes=90,
    pretrained=False,
    reduction=16
)
se_resnet_model.load_state_dict(se_resnet_checkpoint['model_state_dict'])
se_resnet_model.eval()
```

### Compare Predictions

```python
import torch
from PIL import Image
import torchvision.transforms as transforms

# Preprocess image
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])
])

image = Image.open('path/to/image.jpg').convert('RGB')
image_tensor = transform(image).unsqueeze(0)

# ResNet-50 prediction
with torch.no_grad():
    resnet_outputs = resnet_model(image_tensor)
    resnet_probs = torch.nn.functional.softmax(resnet_outputs[0], dim=0)
    resnet_pred_id = torch.argmax(resnet_probs).item()
    resnet_pred_class = resnet_checkpoint['class_names'][resnet_pred_id]
    resnet_confidence = resnet_probs[resnet_pred_id].item()

# SE-ResNet-50 prediction
with torch.no_grad():
    se_resnet_outputs = se_resnet_model(image_tensor)
    se_resnet_probs = torch.nn.functional.softmax(se_resnet_outputs[0], dim=0)
    se_resnet_pred_id = torch.argmax(se_resnet_probs).item()
    se_resnet_pred_class = se_resnet_checkpoint['class_names'][se_resnet_pred_id]
    se_resnet_confidence = se_resnet_probs[se_resnet_pred_id].item()

# Compare results
print("ResNet-50 Prediction:")
print(f"  Class: {resnet_pred_class}")
print(f"  Confidence: {resnet_confidence*100:.2f}%")

print("\nSE-ResNet-50 Prediction:")
print(f"  Class: {se_resnet_pred_class}")
print(f"  Confidence: {se_resnet_confidence*100:.2f}%")
```

## Model Details

### ResNet-50
- **Architecture**: Standard residual network with bottleneck blocks
- **Parameters**: ~25.6M
- **Pretrained**: ImageNet weights
- **Best Validation Accuracy**: 97.84%

### SE-ResNet-50
- **Architecture**: ResNet-50 with Squeeze-and-Excitation attention blocks
- **Parameters**: ~26.0M
- **Pretrained**: ImageNet weights (excluding SE blocks)
- **SE Reduction Ratio**: 16
- **Best Validation Accuracy**: 95.72%

## Training Details

- **Dataset**: ibrahimdaud/raw-food-recognition
- **Number of Classes**: 90
- **Image Size**: 224x224
- **Optimizer**: Adam
- **Learning Rate**: 0.001
- **Batch Size**: 32

## Files in Repository

- `resnet50_pytorch_model.bin` - ResNet-50 model weights
- `se_resnet50_pytorch_model.bin` - SE-ResNet-50 model weights
- `resnet50_metadata.json` - ResNet-50 metadata
- `se_resnet50_metadata.json` - SE-ResNet-50 metadata
- `README.md` - This file

## Citation

If you use these models, please cite:

```bibtex
@model{raw_food_recognition_models_2024,
  title={Raw Food Recognition Models: ResNet-50 and SE-ResNet-50},
  author={Ibrahim Daud},
  year={2024},
  publisher={HuggingFace},
  url={https://huggingface.co/ibrahimdaud/raw-food-recognition-models}
}
```

## License

MIT License