metadata
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)
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']