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language: en
tags:
- pokemon
- card-grading
- image-classification
- onnx
metrics:
- accuracy
---
# Poke-Grader Defect Classifier
Fine-tune de `microsoft/swin-small-patch4-window7-224` para clasificar defectos en cartas Pokémon TCG.
## Cabezas de clasificación
| Cabeza | Clases |
|---|---|
| `surface_scratch` | none / micro / light / medium / heavy |
| `corner_wear` | none / light / medium / heavy |
| `edge_whitening` | none / light / medium / heavy |
## Métricas (mejor epoch: 28)
| Métrica | Valor |
|---|---|
| val_loss | 0.1163 |
| Exactitud surface | **93.3%** |
| Exactitud corners | **96.0%** |
| Exactitud edges | **99.8%** |
| **Exactitud media** | **96.4%** |
Entrenado con **5000 cartas** (~mitad inglés, ~mitad japonés) durante **30 épocas** de fine-tuning.
## Uso (ONNX)
```python
import onnxruntime as ort
import numpy as np
sess = ort.InferenceSession('card_defect_classifier.onnx')
# img: np.array float32, shape (1, 3, 224, 224), normalizado ImageNet
surface, corners, edges = sess.run(None, {'pixel_values': img})
```
Clases: `['none','micro','light','medium','heavy']` / `['none','light','medium','heavy']` / `['none','light','medium','heavy']`
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