---
license: mit
tags:
  - image-classification
  - bacteria
  - medical-imaging
  - efficientnet
  - pytorch
  - dibas
datasets:
  - custom
metrics:
  - accuracy
  - f1
library_name: timm
pipeline_tag: image-classification
---

# EfficientNet-B0 for Bacterial Colony Classification

This model is a fine-tuned version of **EfficientNet-B0** on the [DIBaS (Digital Image of Bacterial Species)](http://misztal.edu.pl/software/databases/dibas/) dataset for classifying bacterial colony images into 33 species.

## Model Description

- **Model Architecture:** EfficientNet-B0 (pretrained on ImageNet)
- **Task:** Multi-class image classification (33 bacterial species)
- **Dataset:** DIBaS - 660+ microscopy images of bacterial colonies
- **Framework:** PyTorch + timm

## Performance

| Metric | Value |
|--------|-------|
| **Validation Accuracy** | 91.67% |
| **Macro F1-Score** | 0.917 |
| **Parameters** | 4.05M |
| **Model Size** | 15.7 MB |
| **GPU Latency** | 5.81 ms (RTX 4070 SUPER) |
| **CPU Latency** | 25.76 ms |

### Comparison with Other Models

| Model | Params (M) | Val Accuracy |
|-------|------------|--------------|
| MobileNetV3-Large | 4.24 | **95.45%** |
| ResNet50 | 23.58 | 93.94% |
| **EfficientNet-B0** | 4.05 | 91.67% |

## Why Choose EfficientNet-B0?

- ✅ **Smallest parameter count** (4.05M) among top performers
- ✅ **Excellent accuracy-to-size ratio**
- ✅ **Good balance** between speed and accuracy
- ✅ **Mobile-friendly** for edge deployment

## Training Details

- **Optimizer:** AdamW (lr=1e-3, weight_decay=1e-4)
- **Epochs:** 10 (early convergence)
- **Batch Size:** 32
- **Image Size:** 224×224
- **Augmentation:** RandomResizedCrop, HorizontalFlip
- **Hardware:** NVIDIA RTX 4070 SUPER
- **Mixed Precision:** Enabled (AMP)
- **Train/Val/Test Split:** 70/20/10 (stratified, seed=42)

## How to Use

```python
import timm
import torch
from PIL import Image
from torchvision import transforms

# Load model
model = timm.create_model('efficientnet_b0', pretrained=False, num_classes=33)
state_dict = torch.load('pytorch_model.bin', map_location='cpu')
model.load_state_dict(state_dict)
model.eval()

# Preprocessing
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Inference
image = Image.open('bacteria_image.jpg').convert('RGB')
input_tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    outputs = model(input_tensor)
    predicted_class = outputs.argmax(dim=1).item()

print(f"Predicted class: {CLASS_NAMES[predicted_class]}")
```

### Class Labels

```python
CLASS_NAMES = [
    "Acinetobacter_baumannii", "Actinomyces_israelii", "Bacteroides_fragilis",
    "Bifidobacterium_spp", "Candida_albicans", "Clostridium_perfringens",
    "Enterococcus_faecalis", "Enterococcus_faecium", "Escherichia_coli",
    "Fusobacterium", "Lactobacillus_casei", "Lactobacillus_crispatus",
    "Lactobacillus_delbrueckii", "Lactobacillus_gasseri", "Lactobacillus_jensenii",
    "Lactobacillus_johnsonii", "Lactobacillus_paracasei", "Lactobacillus_plantarum",
    "Lactobacillus_reuteri", "Lactobacillus_rhamnosus", "Lactobacillus_salivarius",
    "Listeria_monocytogenes", "Micrococcus_spp", "Neisseria_gonorrhoeae",
    "Porphyromonas_gingivalis", "Propionibacterium_acnes", "Proteus",
    "Pseudomonas_aeruginosa", "Staphylococcus_aureus", "Staphylococcus_epidermidis",
    "Staphylococcus_saprophyticus", "Streptococcus_agalactiae", "Veillonella"
]
```

## Limitations

- Trained on single laboratory/microscope setup (DIBaS dataset)
- May not generalize to different imaging conditions
- Not validated for clinical diagnostic use

## Related Models

- [MobileNetV3-Large](https://huggingface.co/ihoflaz/dibas-mobilenet-v3-large) - Best accuracy (95.45%)
- [ResNet50](https://huggingface.co/ihoflaz/dibas-resnet50) - Classic architecture (93.94%)

## Citation

```bibtex
@inproceedings{hoflaz2025bacterial,
  title={Lightweight CNNs Outperform Vision Transformers for Bacterial Colony Classification},
  author={Hoflaz, Ibrahim},
  booktitle={IEEE Conference},
  year={2025}
}
```

## Resources

- **GitHub:** [ihoflaz/bacterial-colony-classification](https://github.com/ihoflaz/bacterial-colony-classification)
- **DIBaS Dataset:** [http://misztal.edu.pl/software/databases/dibas/](http://misztal.edu.pl/software/databases/dibas/)