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--- |
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library_name: timm |
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pipeline_tag: image-classification |
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base_model: |
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- timm/efficientnet_b0 |
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tags: |
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- anime-classification |
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- real-photos |
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- rendered-graphics |
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- pytorch |
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- efficientnet |
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- vision |
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license: openrail |
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model_type: efficientnet_b0 |
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inference: true |
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--- |
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# Anime/Real/Rendered Image Classifier (EfficientNet-B0) |
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**Fast, lightweight classifier for distinguishing photographs from anime and 3D rendered images.** |
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## Model Details |
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- **Architecture:** EfficientNet-B0 (timm) |
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- **Input Size:** 224×224 RGB |
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- **Classes:** anime, real, rendered |
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- **Parameters:** 5.3M |
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- **Validation Accuracy:** 97.44% |
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- **Training Speed:** ~1 min/epoch (GPU) |
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- **Inference Speed:** ~20ms per image (RTX 3060) |
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## Performance |
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| Class | Precision | Recall | F1-Score | |
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|-------|-----------|--------|----------| |
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| anime | 0.98 | 0.99 | 0.99 | |
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| real | 0.98 | 0.98 | 0.98 | |
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| rendered | 0.96 | 0.93 | 0.94 | |
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| **macro avg** | **0.97** | **0.97** | **0.97** | |
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## Usage |
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```python |
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from PIL import Image |
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import torch |
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from torchvision import transforms |
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import timm |
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from safetensors.torch import load_file |
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# Load model |
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model = timm.create_model('efficientnet_b0', num_classes=3, pretrained=False) |
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state_dict = load_file('model.safetensors') |
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model.load_state_dict(state_dict) |
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model.eval() |
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# Prepare image |
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transform = transforms.Compose([ |
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transforms.Resize((224, 224)), |
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transforms.ToTensor(), |
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), |
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]) |
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image = Image.open('image.jpg').convert('RGB') |
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x = transform(image).unsqueeze(0) |
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# Predict |
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with torch.no_grad(): |
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logits = model(x) |
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probs = torch.softmax(logits, dim=1) |
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pred_class = probs.argmax(dim=1).item() |
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labels = ['anime', 'real', 'rendered'] |
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print(f"{labels[pred_class]}: {probs[0, pred_class]:.2%}") |
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``` |
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## Dataset |
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- **Real:** 5,000 COCO 2017 validation images (diverse real-world scenarios) |
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- **Anime:** 2,357 curated anime/animation frames |
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- **Rendered:** 1,610 AAA game screenshots + 61 Pixar movie stills |
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- **Total:** 8,967 images (8,070 train / 897 val) |
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## Training Details |
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- **Augmentation:** None (raw resize to 224×224) |
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- **Optimizer:** AdamW (lr=0.001) |
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- **Loss:** CrossEntropyLoss with class weighting |
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- **Epochs:** 20 |
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- **Batch Size:** 80 |
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- **Hardware:** NVIDIA RTX 3060 (12GB) |
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## Known Limitations |
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- **Real vs Rendered:** Some confusion (photorealistic games misclassified as real) |
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- **Stylized Games:** Cel-shaded games (e.g., Fate/Extella) may score as anime |
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- **Pixar:** Stylized rendered images may show mixed confidence |
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## Recommendations |
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- Use ensemble with tf_efficientnetv2_s for critical applications |
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- Apply confidence threshold: only trust predictions >85% confidence |
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- For edge cases, use the full confusion matrix to understand failure modes |
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## License |
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OpenRAIL - Free for research and commercial use with proper attribution |
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