---
library_name: timm
pipeline_tag: image-classification
base_model:
  - timm/efficientnet_b0
tags:
  - anime-classification
  - real-photos
  - rendered-graphics
  - pytorch
  - efficientnet
  - vision
license: openrail
model_type: efficientnet_b0
inference: true
---

# Anime/Real/Rendered Image Classifier (EfficientNet-B0)

**Fast, lightweight classifier for distinguishing photographs from anime and 3D rendered images.**

## Model Details

- **Architecture:** EfficientNet-B0 (timm)
- **Input Size:** 224×224 RGB
- **Classes:** anime, real, rendered
- **Parameters:** 5.3M
- **Validation Accuracy:** 97.44%
- **Training Speed:** ~1 min/epoch (GPU)
- **Inference Speed:** ~20ms per image (RTX 3060)

## Performance

| Class | Precision | Recall | F1-Score |
|-------|-----------|--------|----------|
| anime | 0.98 | 0.99 | 0.99 |
| real | 0.98 | 0.98 | 0.98 |
| rendered | 0.96 | 0.93 | 0.94 |
| **macro avg** | **0.97** | **0.97** | **0.97** |

## Usage

```python
from PIL import Image
import torch
from torchvision import transforms
import timm
from safetensors.torch import load_file

# Load model
model = timm.create_model('efficientnet_b0', num_classes=3, pretrained=False)
state_dict = load_file('model.safetensors')
model.load_state_dict(state_dict)
model.eval()

# Prepare image
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

image = Image.open('image.jpg').convert('RGB')
x = transform(image).unsqueeze(0)

# Predict
with torch.no_grad():
    logits = model(x)
    probs = torch.softmax(logits, dim=1)
    pred_class = probs.argmax(dim=1).item()

labels = ['anime', 'real', 'rendered']
print(f"{labels[pred_class]}: {probs[0, pred_class]:.2%}")
```

## Dataset

- **Real:** 5,000 COCO 2017 validation images (diverse real-world scenarios)
- **Anime:** 2,357 curated anime/animation frames
- **Rendered:** 1,610 AAA game screenshots + 61 Pixar movie stills
- **Total:** 8,967 images (8,070 train / 897 val)

## Training Details

- **Augmentation:** None (raw resize to 224×224)
- **Optimizer:** AdamW (lr=0.001)
- **Loss:** CrossEntropyLoss with class weighting
- **Epochs:** 20
- **Batch Size:** 80
- **Hardware:** NVIDIA RTX 3060 (12GB)

## Known Limitations

- **Real vs Rendered:** Some confusion (photorealistic games misclassified as real)
- **Stylized Games:** Cel-shaded games (e.g., Fate/Extella) may score as anime
- **Pixar:** Stylized rendered images may show mixed confidence

## Recommendations

- Use ensemble with tf_efficientnetv2_s for critical applications
- Apply confidence threshold: only trust predictions >85% confidence
- For edge cases, use the full confusion matrix to understand failure modes

## License

OpenRAIL - Free for research and commercial use with proper attribution