---
license: openrail
language: en
library_name: timm
tags:
  - image-classification
  - anime
  - real
  - rendered
  - 3d-graphics
datasets:
  - coco
  - custom-anime
  - steam-screenshots
---

# TF-EfficientNetV2-S - Anime/Real/Rendered Classifier

Higher-capacity classifier with improved generalization for distinguishing photographs from anime and 3D rendered images.

## Model Summary

- **Model Name:** tf_efficientnetv2_s
- **Framework:** PyTorch + TIMM
- **Input:** 224×224 RGB images
- **Output:** 3 classes (anime, real, rendered)
- **Parameters:** 21.5M (4× larger than B0)
- **Size:** 81.4 MB

## Intended Use

Same as EfficientNet-B0, but with higher accuracy and better generalization:
- **anime**: Drawn 2D or cel-shaded animation
- **real**: Photographs and real-world footage
- **rendered**: 3D graphics (games, CGI, Pixar, etc.)

## Performance

**Validation Accuracy:** 97.55% (+0.11% vs B0)

| Class | Precision | Recall | F1-Score | Support |
|-------|-----------|--------|----------|---------|
| anime | 1.00 | 0.97 | 0.98 | 236 |
| real | 0.98 | 0.99 | 0.98 | 500 |
| rendered | 0.93 | 0.90 | 0.91 | 161 |
| **weighted avg** | **0.97** | **0.95** | **0.96** | **897** |

## Training Data

Identical to EfficientNet-B0:
- **Real images:** 5,000 COCO 2017 validation set
- **Anime images:** 2,357 curated frames
- **Rendered images:** 1,549 AAA games + 61 Pixar stills
- **Total:** 8,967 images (8,070 train / 897 diverse val)

## Training Details

- **Framework:** PyTorch
- **Augmentation:** Resize only (224×224)
- **Loss Function:** CrossEntropyLoss with inverse frequency weighting
- **Optimizer:** AdamW (lr=0.001)
- **Batch Size:** 40 (GPU memory constrained)
- **Epochs:** 20
- **Hardware:** NVIDIA RTX 3060 (12GB VRAM)
- **Training Time:** ~60 minutes

## Comparison to EfficientNet-B0

| Metric | B0 | V2-S | Delta |
|--------|-----|------|-------|
| Final Accuracy | 97.44% | 97.55% | +0.11% |
| Best Accuracy | 97.99% | 97.99% | Tied |
| Params | 5.3M | 21.5M | +4× |
| Speed | ~20ms | ~60ms | -3× |
| Convergence | Epoch 4 | Epoch 13 | -9 epochs |
| Train Loss | 0.1022 | 0.0003 | Better |
| Val Loss | 0.5519 | 0.1134 | Better |

**Verdict:** V2-S learns training distribution more thoroughly, but marginal real-world improvement. Use B0 for speed, V2-S for maximum accuracy.

## Limitations

1. Slower inference (60ms vs B0's 20ms)
2. Larger model (81.4MB vs B0's 16.2MB)
3. Same fundamental challenges: photorealistic games, cel-shading, artistic renders
4. Performance degrades on images <224×224

## Recommendations

- **Real-time/Mobile:** Use EfficientNet-B0 instead
- **Accuracy-Critical:** This model preferred
- **Ensemble:** Use both models for highest confidence
- **Confidence Threshold:** ≥80% for reliable predictions
- **Edge Cases:** Manually inspect when models disagree

## How to Use

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

# Load
model = timm.create_model('tf_efficientnetv2_s', 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]),
])
img = Image.open('image.jpg').convert('RGB')
x = transform(img).unsqueeze(0)

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

labels = ['anime', 'real', 'rendered']
print(f"{labels[pred]}: {probs[0, pred]:.1%}")
```

## Ensemble Strategy

For maximum accuracy, use both models:

```python
# Load both
b0 = load_model('efficientnet_b0')
v2s = load_model('tf_efficientnetv2_s')

# Infer
with torch.no_grad():
    probs_b0 = torch.softmax(b0(x), dim=1)
    probs_v2s = torch.softmax(v2s(x), dim=1)
    
    # Average predictions
    ensemble_probs = (probs_b0 + probs_v2s) / 2
    pred = ensemble_probs.argmax().item()
```

## Benchmarks

**Inference Speed (RTX 3060)**
- Single image: ~60ms
- Batch of 16: ~200ms

## Ethical Considerations

Same as EfficientNet-B0. This model:
- NOT designed for deepfake detection
- May have cultural bias in anime/rendered representation
- Should be used with human review for content moderation

## Contact

For questions: [GitHub repo]

## License

OpenRAIL - Free for research and commercial use with proper attribution