Anime/Real/Rendered Image Classifier (TF-EfficientNetV2-S)
Higher-capacity classifier with improved generalization for anime, photo, and 3D detection.
Model Details
- Architecture: TF-EfficientNetV2-S (timm)
- Input Size: 224×224 RGB
- Classes: anime, real, rendered
- Parameters: 21.5M (4× larger than B0)
- Validation Accuracy: 97.55% (+0.11% vs B0)
- Training Speed: ~3 min/epoch (GPU)
- Inference Speed: ~60ms per image (RTX 3060)
Performance
| Class | Precision | Recall | F1-Score |
|---|---|---|---|
| anime | 1.00 | 0.97 | 0.98 |
| real | 0.98 | 0.99 | 0.98 |
| rendered | 0.93 | 0.90 | 0.91 |
| macro avg | 0.97 | 0.95 | 0.96 |
Comparison to EfficientNet-B0
| Metric | B0 | V2-S | Winner |
|---|---|---|---|
| Final Accuracy | 97.44% | 97.55% | V2-S +0.11% |
| Best Accuracy | 97.99% | 97.99% | Tied |
| Params | 5.3M | 21.5M | B0 (lighter) |
| Speed | 1 min/epoch | 3 min/epoch | B0 (faster) |
| Convergence | Epoch 4 | Epoch 13 | B0 (faster) |
Verdict: V2-S learns training data better with marginally improved generalization. Use B0 for speed, V2-S for accuracy.
Usage
from PIL import Image
import torch
from torchvision import transforms
import timm
from safetensors.torch import load_file
# Load model
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]),
])
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
- Anime: 2,357 curated animation frames
- Rendered: 1,610 AAA games + 61 Pixar stills
- Total: 8,967 images (8,070 train / 897 perceptually-hashed val)
Training Details
- Augmentation: None (raw resize to 224×224)
- Optimizer: AdamW (lr=0.001)
- Loss: CrossEntropyLoss with class weighting
- Epochs: 20
- Batch Size: 40 (GPU memory constrained)
- Hardware: NVIDIA RTX 3060 (12GB)
Known Behavior
- Better Anime Detection: Perfect precision (1.00) but 97% recall
- Stronger Real Recognition: 99% recall on real images
- Rendered Uncertainty: 90% recall suggests photorealistic games still challenging
- Slower Inference: ~3× slower than B0 due to model size
Recommendations
- Production: Ensemble both models for maximum confidence
- Real-time: Use B0 for speed-critical applications
- Accuracy-critical: Use V2-S as primary model
- Confidence Thresholding: Only trust predictions >80% confidence
License
OpenRAIL - Free for research and educational purposes